When headlines shout that AI threatens jobs, it obfuscates that job losses arising from the uses and abuses of AI are business decisions. Blame the algorithm, the machine, or the inevitable march of technology rather than deliberate attacks on corporate workforces. AI doesn't make decisions. People do. Specifically, corporate managers make decisions about how to deploy AI, and often, that decision is about cutting costs, maximising profits, and consolidating power, not about genuine innovation or societal benefit.

Corporations are pouring unprecedented amounts of capital into AI infrastructure and development. This isn't small-scale innovation; building hyper-scale data centres, acquiring immense computing power, and snarfling vast datasets. These enormous investments serve a dual purpose: to gain a competitive edge and, crucially, to create insurmountable barriers to entry for smaller players. Only the tech giants can truly play this game, further consolidating their market power.

However, these approaches require immense resources with significant ecological and social consequences.1 The energy consumption and water usage for cooling data centres rob local communities of potable water and accelerate the climate crisis. The human toll of relying on exploited ‘ghost workers’ in the Global South for low-paid data annotation and content moderation – the invisible labour2 that trains our "intelligent" machines. This isn't just about efficiency; it's about power, control, and a relentless pursuit of profit, regardless of the actual cost.

The Pressure Cooker of ROI – Human Costs as "Savings"

Investors demand returns on their colossal AI investments. How do corporate managers prove AI's worth? For many enterprise customers, the most straightforward and immediate way to demonstrate Return on Investment (ROI) from AI is by drastically reducing labour costs. The history of automation reveals a complex pattern where technological advancement has often been deployed strategically to reduce labour costs and limit worker power. Since the 1970s, the share of economic growth that goes to labour has steadily fallen, with most benefits trickling up.

The impact of enterprise AI continues this trend:

• Headcount Reduction (Layoffs): Corporate managers choose to replace human workers with automated systems, leading to layoffs. It's crucial to understand: AI isn't doing the firing; it's a management decision to prioritise profit over people.

• Wage Suppression: Even for jobs that remain, the perceived ability of AI to automate tasks or monitor performance puts significant downward pressure on wages. If a machine *could* do it, why pay a human more?

• “Disciplining" the Workforce: Management can use the ever-present threat of automation to exert greater control over employees, discouraging demands for better pay or improved working conditions. This isn't about technological advancement; it's about power dynamics.

The Imperfect Machine – Slop, Sabotage, and Still Layoffs

Despite the hype, AI is far from a magic bullet. It's often imperfect, biased, and susceptible to manipulation. We've already seen the rise of "AI Slop": much of AI output is mediocre, unoriginal, and even inaccurate because it's trained on vast amounts of low-quality, "crappy" data from the internet. This is Sturgeon's Law in action – 90% of everything is rubbish, and our AIs are learning from it. This has predictable results:

An AI-powered chatbot that replaced employees at an eating disorder hotline has been shut down after it provided harmful advice to people seeking help.

Worse, these systems are vulnerable to data poisoning attacks3 and sabotage. Malicious actors can intentionally corrupt training data to cause models to fail, spread misinformation, or produce harmful outputs.

It's ironic that corporate managers are willing to lay off thousands of workers based on the promise of a technology that is often flawed, biased, and even susceptible to external attack. This underscores that the primary driver isn't technological perfection or societal benefit, but rather the single-minded pursuit of cost savings and control. Corporations invest heavily, and workers pay the price. The risk is offloaded onto the human labour force while privatising the potential rewards.

It’s time for Accountability and Justice

The future of AI work is not predetermined by technology. Human choices, corporate priorities, and public policy shape it. We must understand that layoffs aren't an inevitable consequence of AI itself but a deliberate decision by corporate managers to maximise profits at human cost. The arrow of accountability points squarely at boardrooms and executive suites. We need policies that shield workers from arbitrary layoffs and ensure fair wages in an economy increasingly shaped by automation. Let's support the development of publicly funded and managed AI systems that prioritise societal benefit, ethical growth, and shared prosperity over private profit. We must demand greater corporate transparency about their AI deployments, energy consumption, and hidden labour practices. This means insisting that decision-making individuals are held responsible for the societal and individual impacts of the AI systems they choose to implement. You can participate in public conversations about AI governance, ensuring that democratic values, privacy, and human dignity are central to its development.

Let's not let corporations hide behind the myth of autonomous AI. It's time to hold them accountable for their choices and demand an AI future that truly serves humanity, not just the bottom line.

It’s time to understand that, like the skilled weavers who resisted the degradation of their skills and the quality of the cloth they produced rather than the technology itself, modern-day workers need to work together to resist the degradation of AI. The Luddites' ultimate defeat came partly because they lacked broader social support and faced violent state suppression. Modern resistance to exploitative AI deployment might succeed where they failed by building coalitions that include consumers who value quality, professionals who understand the importance of expertise, civil society and local organisation concerned about the environment, and policymakers concerned about economic inequality.

In, "AI Slop and Sturgeon’s Law," I pointed to a pretty serious issue arising from training AI models on ubiquitously scraped data from the internet where 90% of everything is crap. This leads to "AI slop" - mediocre, uninspired, and often biased output. But what if the problem isn't just accidental mediocrity? What if it's intentional sabotage? This isn't a hypothetical question. The same vulnerabilities that allow AI slop creation also open the door to AI data poisoning or LLM Grooming.

What is AI Data Poisoning and LLM Grooming?

If AI slop is the GIGO result from a model consuming the raw, unfiltered mess that is the Internet, then AI poisoning is what happens when someone or something slips poison into the mix. It intentionally corrupts an AI model's training data to control, disrupt, or degrade its performance. According to the Open Worldwide Application Security Project (OWASP), which tracks major security threats, data and model poisoning is now a top-10 vulnerability for LLMs.

LLM Grooming is a more subtle and insidious form of this. This approach has been identified as a strategy to manipulate large language models (LLMs) for foreign information manipulation and interference (FIMI) purposes12. Like the social definition of "grooming," it's about building a deceptive relationship with the system to coax it into producing a desired, often harmful, output. An attacker "grooms" an AI by feeding it carefully crafted data that seems innocent but gradually shifts its understanding of the world, creating hidden biases or "backdoors" that can be exploited later.

The "scrape-it-all" approach to data collection, celebrated by Big Tech as the key to powerful AI, is also its Achilles' heel. When you train a model on a vast, unregulated sea of public data, you have no real way of knowing who has tampered with that data. An attacker doesn't need to hack into a system; they need to upload cleverly corrupted data and wait for it to be scraped up by the next training run.

The goals of such an attack are varied and insidious:

• Backdoors and Triggers: An attacker can insert hidden triggers into the data. The model appears to function normally, but when it encounters a specific word, image, or phrase, it outputs something malicious—disinformation, hate speech, or dangerous computer code. It’s important to note that the amount of poison does not have to be a larger portion of the dataset to have an impact.3

• Targeted Degradation: A model can be poisoned to fail on specific topics. Imagine a financial AI trained to ignore evidence of a certain type of fraud, or a content moderation AI trained to permit hate speech directed at a specific community. A model can be groomed to fail on specific topics - for example, to ignore evidence of human rights abuses committed by a certain state actor or to dismiss scientific consensus on climate change.

• General Chaos: Sometimes the goal is to sow distrust and make a model unreliable, degrading its overall performance and eroding public faith in the technology.

The Political Weaponisation of AI

Imagine a hostile state actor, or a political opponent in an electoral contest, poisoning the AI models that news aggregators or search engines are beginning to use. According to a report from the Harvard Kennedy School, AI has dramatically lowered the barrier for creating highly targeted misinformation campaigns that can fabricate crises or incite panic. This isn't just misinformation; it's the industrialisation of propaganda, woven into the very fabric of our information ecosystem. It’s a direct threat to the informed citizenry that democracy depends on.

When artists and writers use tools like Nightshade, a project out of the University of Chicago, to "poison" their work, they do so defensively, trying to protect their labour from being scraped without consent. Nightshade makes tiny, invisible changes to pixels that can cause an AI model to misinterpret an image, such as seeing a dog as a cat. It proves the concept: our data is vulnerable. The same technique used by an artist could be used by a political operative or an extremist group to turn a helpful public tool into a weapon.

This doesn't touch on the human cost of cleaning up the reviewed data. Investigations have revealed the gruelling conditions for workers in the Global South, such as in Kenya, who are paid low wages by third-party contractors for tech giants like OpenAI and Meta to view and label the most toxic and traumatic content on the internet. A recent feature by Coda Story highlighted how this work, essential for filtering hate and violence, leaves lasting psychological scars on the very people who are least supported.

We Need a Secure Supply Chain for Data

This brings us back to the urgent need for a Canadian path on AI, led by our new Minister of AI and Digital Innovation. Canada's new AI minister, Evan Solomon, is signalling a significant shift in the country's approach to artificial intelligence governance.

In his first speech since becoming Canada’s first-ever AI minister, Evan Solomon said Canada will move away from "over-indexing on warnings and regulation" to make sure the economy benefits from AI.4

The threat of poisoning and grooming makes this announcement naive at best or a preemptive surrender to Big Tech at worst. Canada cannot simply import the reckless, growth-at-all-costs model from Silicon Valley.

A call for Public AI is more critical than ever. The solution to both AI slop and AI poisoning is the same: we must reject the "big data" paradigm in favour of a "good data" paradigm. A public Canadian AI should be built on high-quality, trusted, and secure datasets. That means:

1. Curation over Scale: Instead of scraping the entire internet, we should build smaller, high-quality training sets from public institutions such as the CBC/Radio-Canada archives, academic research from our universities, and digitised library collections.

2. Data Provenance and Transparency: We must demand to know where the data comes from. A secure "data supply chain" with clear records of origin and modification is a core part of building trustworthy AI. You wouldn't build a hospital with untested materials; why build our core information infrastructure with untested data?

3. Robust Guardrails: We need regulations that hold companies liable for the harm caused by their poisoned or biased models. This would create a powerful incentive for them to secure their data pipelines and invest in defenses.

The move from accidental "slop" to intentional "poisoning" is the predictable next step in a world that prioritises data quantity over quality and safety. Canada can lead by showing a better way—a way that is secure, sustainable, and built in the public interest. Let's not wait for the well to be poisoned before we decide to protect it.

Photo by Ben Hadfield on Unsplash.

Theodore Sturgeon was a prominent science-fiction author who may be best known for 1953's More Than Human. If it were written today, it might be characterised as a transhumanist novel. He is also credited with originating what has come to be known as Sturgeon's Law. When asked to defend the quality of his genre against critics who claimed 90 percent of it was crud, Sturgeon famously retorted, "Ninety percent of everything is crap."

He wasn't being cynical but realistic. Any content consumer today will know that most of what is produced is mediocre, uninspired, or just plain wrong. This is pretty much true across the board—literature, film, music, academic research, and particularly the endless stream of content on the internet. Only a small fraction, at most 10 per cent, is genuinely excellent.

For decades, this was just a pithy or curmudgeonly comment on the everyday creation of what we now call content. In a wonderful bit of serendipity, Sturgeon's Law has become a way to counter AI hype and provide a cautionary note about AI tools and a fundamental problem with them.

The Training Data Dilemma: An Ocean of Mediocrity

Large Language Models (LLMs), the engines behind tools like ChatGPT, Gemini, or Claude, are trained on datasets scraped indiscriminately from the Internet. They then extrude text, mimicking writing, reasoning, and creativity by identifying patterns in the text and images created by humans. This includes everything from digitised books and scientific papers to news articles, Reddit comments, and billions of social media posts. Here's the catch: if Sturgeon's Law holds for the Internet (and anyone who has spent time online knows that 90 per cent is a generous estimate), then these AI models are being trained on a dataset where at least 90 percent of the material is, for lack of a better word, crap.

When you train a system on a diet of mediocrity, you shouldn't be surprised when its output reflects that. The model learns to replicate the most common patterns it sees. It's a regression to the mean, a levelling to the lowest common denominator. The result is what many are now calling "AI slop"—content that is superficially plausible, grammatically correct, but ultimately bland, soulless, and often subtly inaccurate. It mimics the style of human writing without the substance, the spark of original thought, or the depth of lived experience.

This isn't just a matter of quality; it's a matter of truth. The "crap" in the training data includes not just poorly written prose but also misinformation, conspiracy theories, and deeply ingrained societal biases. The AI learns these patterns just as readily as it learns grammar rules.

The Consequences for Authentic Creation

The proliferation of AI slop presents a clear danger to creators, researchers, and anyone who values authentic human expression.

• The Devaluation of Original Work: As our feeds become flooded with cheap, instantly generated content, original research and authentic creativity fade into the background. It becomes harder for readers and consumers to find the genuine article amidst a sea of synthetic text. Why would a company pay a Canadian writer or journalist for a thoughtful article when they can generate a passable, keyword-stuffed equivalent for pennies? The economic foundation of creative labour is eroding.

• Poisoning the Well for Future Knowledge: We are creating a self-referential loop of mediocrity. Future AI models will inevitably be trained on the slop produced by today's AI. The internet's dataset is becoming increasingly polluted with synthetic, derivative content. This will make future AI models less reliable and make it harder for humans to find trustworthy information and untainted sources. AI models are crapping into their input funnels. The results will not be good.

• Privacy, Data Colonialism and the Human Cost: Let's not forget where this training data comes from. It's our collective intellectual and creative output—our blog posts, family photos, and late-night forum arguments—scraped and ingested without our meaningful consent to fuel the development of commercial products. This is a new form of data colonialism, where the raw material of our lives is extracted, processed, and turned into a product that benefits a handful of corporations, primarily in the Global North.

  • But the exploitation runs deeper. The "dirty work" of making these AI systems functional—the painstaking and often traumatising labour of data classification and content moderation—is frequently outsourced to workers in the Global South. These are the people paid pennies to view and label the very worst of the internet's content: hate speech, violence, and abuse. This is the hidden, human cost of our shiny new AI toys, a stark reminder of the global inequalities that underpin the digital economy.

• The Climate Connection: The computational power required to train these massive models is immense, contributing to a significant carbon footprint. Are we willing to expend vast amounts of energy and resources, contributing to our climate crisis, to generate a tidal wave of digital mediocrity? Is this a worthy trade-off?

Charting a Different Course🍁

The federal government has just appointed its first-ever Minister of Artificial Intelligence and Digital Innovation, Evan Solomon. This presents a pivotal opportunity to reject the Silicon Valley model of reckless development and instead forge a uniquely Canadian path. This is the moment to champion Public AI—AI systems built in the public interest. We can create open-source models trained not on the internet's sludge, but on high-quality, curated datasets from our world-class cultural and scientific institutions. Imagine AI tools designed to strengthen public services, not just to maximise corporate profit.

The new ministry must establish strong guardrails for sustainable AI. That means implementing policies that demand energy efficiency and transparency from data centres, protect workers from exploitative labour practices common in the industry, and ensure that the benefits of AI are shared by all Canadians, not just a select few. The new minister has signalled a focus on economic benefits over regulation, which makes it even more critical for activists and citizens to demand a framework that prioritises people and the planet.

We cannot allow the future of our information ecosystem to be dictated by a model trained on the worst of us. We need a public conversation about the digital world we want to build, led by citizens and not just tech corporations.

This means demanding transparency in how AI models are trained, fighting for policies that protect the rights and livelihoods of human creators, and investing in and creating spaces for authentic, high-quality information and art to flourish, free from the noise of the slop machine.

We cannot allow the future of our information ecosystem to be dictated by a model trained on the worst of us. We need a public conversation about the digital world we want to build, led by citizens and not just tech corporations. Sturgeon's Law was a witty observation. It was never meant to be a technical blueprint for the future of knowledge. It's time we focused on curating and celebrating the 10 per cent of excellence rather than building a global infrastructure that endlessly regurgitates the other 90 per cent.

They stand silent on the slopes of a remote island, stone giants staring out at an empty sea. The Moai of Rapa Nui (Easter Island) are marvels of human ingenuity and devotion, a testament to a society’s capacity for immense, coordinated effort. They are also silent witnesses to a catastrophic collapse. The society that carved them, that poured its identity, resources, and labour into their creation, seemingly sacrificed its very future for them.

Today, we are building our monuments. They are less tangible than stone, but no less massive. They are the sprawling, humming, and voraciously hungry data centres that power artificial intelligence. To their builders, they are the pinnacle of our achievement, the foundation of a future defined by limitless progress. But if we listen closely, past the utopian promises of Silicon Valley, we can hear a warning echoing from the shores of Rapa Nui. Our digital Moai are being erected at a staggering dollar, human, and ecological cost—and we are refusing to see the cliff edge we are racing towards.

Let’s be clear: the construction of the Moai was a symptom, not the sole disease. It represented a society’s totalizing focus on a single form of prestige and power, a cultural obsession that required the felling of entire forests to transport the monolithic statues. This deforestation led to soil erosion, the collapse of biodiversity, and ultimately, the starvation and warfare that decimated the Rapa Nui people. They likely didn’t see the last tree for the statues.

Are we any different?

The AI industry is powered by data centres that consume biblical amounts of energy and water. A single query to a generative AI model can use nearly ten times the electricity of a simple Google search. Globally, the electricity consumption of data centres is projected to more than double by 2026, surpassing the entire energy use of countries like France. In Canada, where we pride ourselves on our climate commitments, the race to build these facilities threatens to derail our goals, with some projections showing that new data centres could force provinces to rely more heavily on fossil fuels, effectively wiping out hard-won emissions reductions.

This insatiable thirst extends to water. Billions of litres of fresh water are required to cool these computational behemoths, diverting a precious resource from communities and ecosystems already under strain from a worsening climate crisis. We are, in essence, draining our digital island’s resources to power our statues.

But the cost is not just ecological. The great stone heads of Rapa Nui required the immense physical labour of their people. Our digital Moai are also built on the backs of a vast, hidden workforce—one located primarily in the Global South.

Beneath AI's sleek, automated façade lies a sprawling human infrastructure of “ghost workers.” Millions of people in countries like Kenya, the Philippines, and India perform the gruelling, low-paid data annotation and content moderation labour. These are the digital carvers, meticulously labelling images and text to train AI models, or shielding users from the internet's most toxic content by viewing it themselves.

Reports have exposed a grim reality: workers are paid less than $2 an hour, face precarious employment with no benefits, and suffer significant psychological trauma. This is the dark underbelly of the AI revolution, a system of digital colonialism that concentrates wealth and power in the Global North while outsourcing the human and environmental damage to the South. It is an extractive logic as old as empire, repackaged in the language of innovation.

Just as the Rapa Nui chiefs likely celebrated each new, larger Moai, our tech titans celebrate every bigger, more powerful AI model. The colossal sums of money, the brightest minds of a generation, and our planet’s finite resources are being poured into this singular pursuit. We are told it is for the good of humanity, that this is the only path to progress.

But a critical, honest accounting is long overdue. Could this immense allocation of capital and intellect not be better used to tackle the crises AI is exacerbating—climate change, resource scarcity, and global inequality?

The Rapa Nui left us a haunting lesson carved in stone. Their statues were a cultural apex, but also a symbol of unsustainable choices, of a society so fixated on its symbols of status that it failed to protect its foundations.

We must now look at the humming, windowless data centres rising in our communities and worldwide and ask ourselves the hard questions. What are we sacrificing for these digital giants? Whose labour are they built on? Whose water are they drinking? And when we have strip-mined our planet for the energy and materials to power them, who will be left to admire their cleverness?

It is time to demand transparency, accountability, and a radical shift in direction. We need sustainable AI, fair labour practices throughout the entire AI supply chain, and a public, democratic conversation about the kind of future we are building. Otherwise, our magnificent digital Moai will become our legacy—towering monuments to our own brilliant, self-inflicted demise, standing silent watch over a world we chose not to save.

The Carney Liberals have introduced a bill that would permanently damage our digital privacy, and it’s a story we’ve seen before. Like a zombie, the idea of "lawful access" legislation keeps rising from the dead, no matter how many times Canadians have killed it. First introduced by previous Liberal governments in the 2000s, then aggressively pushed by Stephen Harper’s Conservatives as the infamous Bill C-30, this toxic idea is now back, championed by the current Liberal government.

Buried deep inside a massive piece of legislation misleadingly titled the "Strong Borders Act" (Bill C-2) is Part 15: the Supporting Authorised Access to Information Act (SAAIS). Let's call it what it is: the same old Government Surveillance Bill in a new disguise. And just like we did before, we have to stop it.

A Zombie Bill That Refuses to Die

This isn't a new fight, and it crosses party lines.

• In 2012, the Conservative government introduced Bill C-30, the "Protecting Children from Internet Predators Act"—a cynical title for a bill that would have given authorities warrantless access to your data. Public Safety Minister Vic Toews infamously told opponents they could "either stand with us or with the child pornographers."

• Canadians from all walks of life rose. The backlash was immense and unified. The government was forced to kill the bill completely.

• We won. Canadians across the political spectrum clearly stated that warrantless access and mandated surveillance backdoors have no place in Canada.

Now, the Carney Liberals are trying again, using the classic political tactic of hiding these hugely controversial powers inside an unrelated omnibus bill, hoping we won't notice. But we've seen.

Why We Must Fight Bill C-2 Today

This new Liberal bill has all the same terrifying features as its predecessors. If passed, it would:

Force Companies to Build Backdoors

The bill would compel companies, from your messaging app to your email provider, to build the technical tools for police and spy agencies to intercept your communications. While it pays lip service to not creating a "systemic vulnerability," the government gives itself the power to define that term, creating a loophole big enough to drive a surveillance truck through.

Operate in Secrecy

Section 15 of Bill C-2 could impose a permanent gag order on any company served with an order. The company could never tell its users, the media, or the public that it has been forced to become an arm of the state. This is a recipe for a secret law system free from democratic accountability.

Sideline Our Courts

Instead of requiring a judge to approve these intrusive measures, the bill hands that power to a political minister in the Carney government. It’s a blatant power grab by the executive branch, undermining the checks and balances that protect our rights.

Affect Everyone

The bill's scope is so broad that it could apply to nearly any online service you use. With massive fines for non-compliance, no company could afford to resist.

This Is a Defining Moment

The Carney government is betting we've forgotten our history. They are hoping that the “Elbows Up” moment that we are in will distract us from fighting this battle one more time. However, “Elbows Up” is also the right approach to this authoritarian push by the Liberal government. This is not a done deal, and Canadians know that when we stand together, we win.

WHAT YOU CAN DO RIGHT NOW:

CONTACT YOUR MEMBER OF PARLIAMENT (MP) IMMEDIATELY. This is the most critical thing you can do. You can find your MP on the Parliament of Canada website. Call them. Email them. You don't need to be an expert. Just say:

MOBILIZE YOUR COMMUNITY. Don’t act alone. Are you a member of a union, a professional association, a faith group, or a community organisation? Tell them that the zombie surveillance bill is back. Ask your organisation to take a public stand against Bill C-2. The more groups that speak out, the stronger our message becomes.

SPREAD THE WORD. Share this post. Talk to your friends, family, and colleagues. Use the hashtags #StopBillC2, #StopTheZombieBill, and #ProtectOurPrivacyCA on social media. Remind everyone that we have defeated this threat before and will do it again.

This is our moment to bury this zombie bill once and for all. Let’s ensure every Ottawa MP knows that Canadians will not stand for secret surveillance. Not now, not ever.

At the heart of Bill 5 is the power for Ford's cabinet to declare "Special Economic Zones" (SEZs) anywhere in Ontario. Within these zones, the government can waive provincial laws, regulations, and municipal bylaws for specific companies or projects. The term "Special Economic Zone" itself isn't new; the first SEZs appeared in the late 1950s in industrialised nations like Ireland, designed to attract foreign investment, often through tax breaks and looser regulations. China later used SEZs extensively to draw in foreign capital. But what Ford proposes isn't about thoughtful economic development; it's about creating sacrifice zones where corporate interests trump everything else.

A Slap in the Face to Indigenous Rights and Reconciliation

Ford has already signalled his intent to designate the Ring of Fire as Ontario's first SEZ. This region, rich in minerals but ecologically vital and the heart of Treaty 9 territory, cannot become a testing ground for overriding Indigenous rights. Many First Nations leaders have rightly decried Bill 5 as violating their treaty rights. The bill was rammed through without the Free, Prior, and Informed Consent (FPIC) of affected First Nations – a core principle of the UN Declaration on the Rights of Indigenous Peoples (UNDRIP), which Canada has endorsed. Consulting after the power to create these zones is already law is not consultation; it's a sham. We must stand in solidarity with First Nations like Neskantaga, Attawapiskat, and others who courageously oppose this colonial approach and demand their right to self-determination and meaningful consent on any development in their territories.

Fueling the Climate Crisis, Not a Green Transition

The Ring of Fire isn't just a mining prospect; it's part of the Hudson Bay Lowlands, home to vast peatlands – the second-largest continuous expanse of northern peatland on Earth. These peatlands are colossal carbon sinks, storing an estimated 150 billion tonnes of carbon in Canada alone. Disturbing them for mining, facilitated by SEZ exemptions, would release enormous amounts of greenhouse gases, accelerating the climate emergency. It isn't a "green" transition if we destroy critical ecosystems and carbon stores to get so-called "critical minerals." Furthermore, Bill 5 guts the Endangered Species Act, allowing politicians to ignore scientific advice, and weakens environmental assessment processes province-wide. This is a direct contradiction to any serious climate action.

Workers and Communities: Sacrificed at the Altar of Deregulation

While Ford's officials offer verbal assurances that labour laws won't be touched in SEZs, the legislation itself is "wide-open," allowing exemptions from any provincial law. This is a recipe for eroding hard-won workers' rights, safety standards, and fair wages. It's an invitation for a race to the bottom. Remember Bill 2 and the assault on nurses and health care workers? Moreover, by allowing the province to override municipal bylaws, Bill 5 silences local communities and democratically elected councils, opening the door to "shady backroom deals."

A Better Path Forward: Resistance and a Just Alternative

Unless you are a full-throated climate change denier and corporate shill, the following should be top of mind:

• Demanding the Full Repeal of Bill 5: This legislation is fundamentally flawed and dangerous.

• Upholding Indigenous Sovereignty: Insist on fully implementing UNDRIP, including FPIC, and respecting Treaty Rights in all decisions. No development without consent.

• Champion Real Climate Action: Protect our vital carbon sinks like the peatlands. Invest in a truly just transition rooted in renewable energy and Green New Deal principles, not destructive extraction.

• Defend Workers and Local Democracy: Fight for ironclad protections for labour rights and ensure communities have the final say on what happens in their backyards.

The fight against Bill 5 is a fight for Ontario's soul. It’s about choosing a future where people and planet come before corporate profit. Let's organise, mobilise, and make our voices heard. Our collective future depends on it.

Carney's Mandate Letter Falls Short on Inequality

Prime Minister Mark Carney's first mandate letter to his cabinet reads like a document for boardrooms rather than food banks. While the letter acknowledges Canada faces "unprecedented challenges," it fundamentally misdiagnoses the core crisis facing ordinary Canadians: deepening inequality that has left millions struggling to afford basic necessities while corporate profits soar. This crisis predates any crisis related to the 'Trump Tariffs'.

The Missing Diagnosis: Inequality as Crisis

The mandate letter speaks extensively about productivity, infrastructure, and global competitiveness, but barely acknowledges the defining issue of our time - the growing chasm between the wealthy and everyone else. Canada's challenges aren't primarily about being competitive enough for global markets, but about ensuring the wealth our economy generates reaches working families.

Consider what's absent from Carney's "generational challenge" framing. There's no mention that CEO compensation has grown exponentially while wages have stagnated. There's no acknowledgement that housing has become a financialised commodity rather than a human right. There is no recognition that our healthcare and social services are crumbling, not because of productivity problems but because of deliberate underfunding while tax cuts flow to corporations and the wealthy.

The letter's focus on "weak productivity" as a root cause exemplifies this misdiagnosis. Canada's problem isn't that workers aren't productive enough - it's that the gains from increased productivity have been captured almost entirely by Canadian and foreign corporations rather than shared with labour. Workers are more productive than ever, but their share of the economic pie continues to shrink.

The Corporate Solutions Trap

Carney's seven priorities reveal an approach that sees private sector partnership as the solution to public challenges. This reflects a fundamental ideological blind spot that progressive critics have long identified: the belief that what's good for business automatically translates into what's good for working people.

Take housing, presented as priority four. The mandate speaks of "unleashing the power of public-private cooperation" and "catalysing a modern housing industry." This language suggests more of the same market-based approaches that created the housing crisis in the first place. Why are we looking to private developers to solve a crisis they helped create through speculation and commodification?

Similarly, the emphasis on attracting "the best talent in the world" while reducing overall immigration rates reveals a troubling two-tier approach. This priority suggests Canada should welcome wealthy immigrants and skilled professionals while restricting opportunities for working-class migrants and refugees, precisely the kind of policy that exacerbates inequality by serving capital's need for cheap labour while restricting worker solidarity.

Further, this skilled immigration strategy represents a form of modern economic colonialism that extracts human capital from the Global South. When Canada recruits doctors, engineers, teachers, and skilled tradespeople from countries like Nigeria, India, or the Philippines, it effectively imports professionals whose education and training were paid for by much poorer nations. These countries invested scarce public resources in universities, medical schools, and technical training programs to develop their human capital, only to see their most skilled workers migrate to wealthy countries like Canada.

The Infrastructure Mirage

The letter's promise to "build an enormous amount of new infrastructure at speeds not seen in generations" sounds ambitious, but infrastructure for whom? The focus on diversifying trading relationships and becoming an "energy superpower" suggests infrastructure designed primarily to serve export markets and corporate interests rather than communities.

Real infrastructure investment that addresses inequality would prioritise public transit that connects working family neighbourhoods, social housing that removes profit from the equation, and green energy systems owned by communities rather than corporations. The mandate letter's infrastructure vision appears designed to facilitate capital flows rather than improve daily life for ordinary Canadians.

The Productivity Obsession

Perhaps most revealing is the letter's emphasis on government productivity through AI deployment and its focus on "results over spending." This technocratic language conceals austerity politics masquerading as innovation rhetoric. The problem with Canadian governance isn't inefficiency - it's inadequate funding for public services caused by decades of tax cuts for the wealthy and corporations. When the letter promises to spend "less on government operations," it promises less public capacity to address inequality at the moment when more robust public intervention is needed.

The emphasis on AI is particularly concerning. While technology can improve service delivery, the focus on AI as a solution to government "productivity" problems suggests an approach that prioritises automation over employment and efficiency over equity.

What's Missing: Power and Redistribution

The most glaring omission from Carney's mandate is any serious discussion of redistributing power and wealth. The letter treats inequality as a byproduct of other problems rather than recognising it as the central organising challenge of our time.

A progressive approach would centre policies like wealth taxes, stronger collective bargaining rights, public ownership of key industries, and robust social programs as rights rather than safety nets. Instead, the mandate offers market-friendly tweaks to a system that fundamentally concentrates power and wealth upward.

The letter's promise to work "in true partnership" with various stakeholders sounds collaborative, but readers should recognise this as false equivalency. Labour, business, and civil society don't have equal power in our current system - pretending they do while avoiding structural reforms maintains power imbalances.

The Climate Contradiction

While the letter mentions fighting climate change, it simultaneously promises to make Canada an "energy superpower in both clean and conventional energies." This contradiction reveals the fundamental tension in liberal approaches that try to address the climate crisis without challenging the growth-dependent capitalist system driving environmental destruction.

Meaningful climate action requires reducing overall resource consumption and energy use in wealthy nations like Canada, not simply adding renewable capacity while maintaining fossil fuel production. The "energy superpower" framing suggests Canada plans to export its way out of climate responsibility rather than leading the transformation to a sustainable economy.

Continuity Disguised as Change

Ultimately, Carney's mandate letter represents continuity with the neoliberal policy framework that created Canada's inequality crisis, dressed up in the language of transformation and renewal. This is a throwback to the Liberal budgets of the 1990s that ‘solved’ the Federal deficit by throwing everything back on the provinces. While the rhetoric acknowledges serious challenges, the solutions remain firmly within the bounds of market-friendly approaches that have consistently failed to deliver broadly shared prosperity.

This mandate reveals the limitations of electoral politics when not accompanied by sustained social movements demanding structural change. Real solutions to inequality require challenging concentrated wealth and power, not partnering with it. They need robust public institutions and services, not AI-driven efficiency. They require treating housing, healthcare, and education as rights, not commodities.

Canadians facing the daily reality of unaffordable housing, precarious employment, and strained public services deserve better than another government that mistakes corporate competitiveness for social progress. The mandate letter's failure to meaningfully address inequality suggests this government, like its predecessors, will leave the fundamental structures driving division and hardship intact while offering modest reforms around the margins.

The crisis isn't that Canada isn't competitive enough - it's that too many Canadians can no longer compete for basic dignity in an economy rigged against them.

"It was hard to write, it should be hard to use"

The quote above was something a programmer friend of mine used to say in the '90s and, despite all the advances in user experience or UX design, it appears to remain effectively true. Let's look at the experience over this past weekend with the newly rolled out enhanced vaccine certificate in Ontario.

Let me preface this by saying that I appreciate that this may be version 1 of a certificate with a QR code and that my comments are intended for an improved version 1.x or 2 of the proof of vaccination. I should also note that CBC has already pointed out that Ontario's enhanced vaccine certificate system is not accessible to marginalized people.

Downloading the Certificate

You will need to go to https://covid-19.ontario.ca/get-proof/ and answer the following questions:

• How many doses of the COVID-19 vaccine do you currently have? (required)

• Did you get all your doses in Ontario? (required)

• Select which health card you have (required)

• Do you identify as First Nations, Inuit, or Métis? (required) If you are a non-Indigenous partner or household member of someone in this group, select "Yes."

• A couple of more clicks (get the certificate through the website or get it by mail or print it at a local library, ServiceOntario location, or call a friend and you will be asked to agree to the Terms of Service which includes the following:

By inputting your personal information and personal health information into the COVID-19 Vaccination Services you are agreeing to the ministry's collection, use and disclosure of this information for the purpose of researching, investigating, eliminating or reducing the current COVID-19 outbreak and as permitted or required by law in accordance with PHIPA as set out above. You also agree that your information will be made available to the Public Health Unit(s) in Ontario responsible for your geographic area for the same purpose.

More specifically, by using COVID-19 Vaccination Services, you consent to the ministry collecting identifying information, including personal health information, about you that you submit through the patient verification page so that the Ministry can ensure that it correctly identifies you for the purpose of administering the COVID-19 vaccination program.

Neither the ministry nor the Public Health Unit(s) in Ontario will further use or disclose your personal information or personal health information except for the purposes set out above.

When you click on the "Get a copy" button you may be asked to wait because a virtual queue is being used to throttle traffic. When you get through you will be asked to provide information from your health card. I have a green OHIP card with a photo so I get this screen:

Assuming the information is correct you will be shown your new certificate, including a QR code (more on this later) as a PDF. At this point, it is on you to print out copies of the two-page certificate and carry them with you. I took it upon myself to crop the certificate to include just my name, birth date & the QR code and print it out small enough that I could put it in a laminating pouch and carry it in my wallet. This enables me to go to my wallet, pull out the laminate and my driving license, and have both ready for presentation. For me, that is the simplest and easiest way. Your mileage may vary since my approach requires being comfortable with basic pdf or graphic editing and having access to a printer. I also put electronic copies on my phone so that I could use that option

Verifying the certificate

Here is what I observed at brunch (shout out to the Sunset Grill on the Danforth).

1. Staff person asks for Proof of Vaccine

2. Customer digs out their phone or paper copy of the certificate

3. The staff person looks at it (or uses the Ontario Verify App)

4. Staff person asks for a government-issued ID

5. Customer digs out their driver's license

6. The staff person looks at the ID and verifies the name

We can do better

What I oberved is NOT a user-friendly experience for either the customer or the business. For the experience to be improved it needs to be a single presentation operation of either a paper or digital certificate that the business can verify in one step. Here's an example that I mocked up some months ago (the picture is from https://thispersondoesnotexist.com/)

This provides the following functionality:

1. The existing QR code will return the same (i.e. green checkmark in the event of a good code)

2. The verifier (staff person) can compare the photo on the card with the person in front of them rather than asking for a government ID.

3. Digital verification may have the option of showing the verifier the picture on the verify app for increased assurance. Note that the Ministry of Health is already authorized to have pictures for the current health card.

Privacy and Security

The advantage of a paper and ID card presentation ritual is that it is difficult to hack. So if we are going to improve the presentation with a single credential as above, privacy and security MUST be protected. This is why a version 1 that is paper/PDF only is not a bad security and privacy choice. On the Verify Ontario app side, both the terms of use and privacy statement are reasonably clear (although the choice to use Google Analytics could be questioned) and make the right commitments  

Recommendations

Provide retailers with a verifier

It's nice that the Ontario Verify app is freely downloadable. I used it to check that the laminated cards that I made from my own certificate were readable. But this puts the burden on the retailer and their staff. When I saw someone come in with their QR code, the waitress had to dig out her personal phone and use that. Not a good solution. Either the provincial government or public health should provide retailers with a low to zero cost option to procure their own tablets for use on entry to the store.

Provide Ontarians with options

For example:

• I'm relatively tech-savvy so I'd be happy with a QR code/certificate I could add to a wallet app on my phone for easy display without fumbling around.

• ServiceOntario should provide a service to produce laminated wallet cards WITH photos to any Ontarian who shows up at a ServiceOntario site.

• On a go-forward basis, ensure that people attending vaccination clinics get printouts of their QR code based certificates WHEN they get vaccinated since the certificate includes the date of vaccination and presumably the QR code won't return a "Green" until the appropriate dated

With all of the above said, I have to say I'm happy that Ontario's first steps for vaccine certificates appear to have respected Ontarians' privacy and look to be built securely. I look forward to the next couple of weeks because I'm sure that security people will be pounding on the service to find flaws. WHEN they find flaws, let's hope that the province is responsive so that we can all benefit.  

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MyData Canada recently submitted a report to the Government of Ontario in response to its consultation for strengthening privacy protections in Ontario. You can download the submission from the MyData Canada site. I am a board member of MyData Global and a member of the MyData Canada hub. This is a brief summary of some of the recommendations in that report.

Part of what MyData Canada would like the province of Ontario to address is the current ‘gatekeeper model’ where each of us cede control to information about us under terms or privacy policies based on a flawed consent model. As the report puts it,

Behind each of the gate-keepers (“data controllers” in GDPR terms) in the centralized model are thousands of intermediaries and data brokers invisible to the individuals whose data they process. Individuals’ personal data is used in ways they could not anticipate, and often without their awareness or even the opportunity to meaningfully consent.

This needs to be fixed.

Background

Canada has a multi-jurisdictional privacy environment. That means that both levels of government have privacy commissioners and privacy laws. Ontario, Canada’s most populous province, does not have a private sector privacy law. This leaves a number of categories of persons and organizations uncovered; don’t ask why, it’s a constitutional jurisdictional thing. Thus the consultation and submission. MyData Canada believes that,

…our proposed approach will help accelerate the development and uptake of privacy- focused, human-centric innovation and ultimately serve to regain public trust and confidence in the digital economy.

2-Branch Privacy Reform

MyData Canada proposes a two branch approach to privacy law reform; a harmonization branch, and a transformation branch.

• The harmonization branch proposes an incremental approach to enable any new Ontario law to work harmoniously with other regimes, both in Canada and in the rest of the world. This branch is intended to ensure that Ontario is a low friction end point for cross border data flows with other data protection data. At the same time this branch will introduce a regulatory framework with a functional equivalency to the CCPA in the US and to the GDPR in the EU. In essence, this broad framework skates to where the puck will be with respect to global data protection laws.

• The digital transformation branch proposes to simultaneously create a ‘next-generation’ regulatory space within Ontario. This space will allow Ontario based companies or organization to create new forward looking and individually centred solutions. To continue the metaphor, this branch will allow breakaway solutions that will disrupt the current platform information gatekeepers and return autonomy to individuals.

Harmonize Up

Rather than seeking a lowest common denominator or participating in a race to the bottom, MyData recommends harmonizing ‘up’ including the following:

1. Adopting a principled and risk based approach to privacy regulation;

2. Coordinating with other provinces and the federal government, perhaps including a pan-Canadian council of information and privacy commissioners;

3. Aligning with Convention 108 and 108+;

4. Increased enforcement powers; and

5. Implementation support for businesses and organizations for compliance.

Digital Transformation

Create a regulatory environment to reward first movers with privacy enhancing technologies that put people at the centre of their own data. Recommendations include:

1. Creating a privacy technology incubator;

2. Host regulatory sandboxes and hackathons;

3. Grants and other incentives for privacy ‘retrofits’;

4. Create and support a regime for seals, badges, and privacy trust marks;

5. Foster interoperability by requiring api or similar means to prevent or counter ‘platform dominance’ and network affects; and

6. Create up-skilling programs for a multi-disciplinary privacy engineering centre of excellence in Ontario.

Summing up

The above is just a summary of the first recommendations of the report. It includes further recommendations on:

• Taking a comprehensive approach to move beyond compliance;

• Adopting a Consumer Protection and Human Rights oriented regulatory enforcement model

• Adopting a multi-stakeholder and inclusive model to spur innovation and open data

If you find this interesting please download and share the report.

Here’s a speculative fiction plot, NOT a prediction, prompted by the news frenzy of US presidential politics, influenced by my own contrarian nature. If you read history and science fiction we live in an interesting time. Whether it will be a historical turning point in US and global history is not something that we are privileged to know at the time. But if this is a historic turning point I would like to plot out a story based on where we are, not much more than a week before the US election between President Trump and former Vice-President Biden. Let’s ignore that reality strains credulity, and proceed from there. Nothing below should be read as representative of my own views or preferences.

Biden wins?

The Democrats win the House and the Senate. Joe Biden wins the popular vote but the electoral college remains unclear pending delayed counts for mail-in ballots. Election night ends with both candidates being declared the victor by different media outlets.

Taking it to the streets

On the day after the election Trump claims to be the legitimate President based on claims of a rigged election. From the White House he asks his supporters to come out to defend his victory. Activists from both sides take to the streets in every major American city. The national guard takes sides, but on different sides in different states or cities, and civic order breaks down.

COVID confusion

The electoral college convenes and declares Biden to be the winner. Trump and his staff leave the White House under political pressure. President elect Biden is then revealed to be ill with COVID, increasing uncertainty. Before the inauguration Biden dies in hospital, and Kamala Harris is sworn in as the 46th President of the United States, with Pete Buttigieg as her vice-president. The Joint Chiefs of Staff are prominently on display at the inauguration even as conflict rages on the streets of America.

Consolidation

President Harris deploys the military, aided by private military contractors, to establish order on the streets. Former President Trump flees the country to Moscow, declaring a government in exile. The Harris administration declares a continuing state of military emergency with Democratic majorities in the House and Senate. The state of emergency delays elections until the ‘emergency is over’.

Epilogue

10 years after the start of the Harris dynasty, the United States has become the Democratic Republic of America, run by the United Democratic Party under Grand President Harris. The former Republican Party has been absorbed into the United Democratic Party. Wall Street has become a financial back-water. The US overseas military presence has been dramatically reduced because of domestic military requirements. There is an underground resistance, led by Alexandria Ocasio-Cortez, the most wanted fugitive in American. Climate change is at 2.5 degrees and increasing. China has become the dominant global power, dominating the United Nation, based on its Belt and Road initiative.

End note

I hope that this will prove to be really bad speculation. The past is prologue but it is our job to create the history we want to inhabit. I wish my American friends a successful and well-run election with a successful and peaceful transition of power if the Biden-Harris ticket wins.

This was originally posted on Medium on 2010-04-12.

Google and Apple have announced a partnership on a privacy preserving COVID-19 contact tracing technology. The Pan-European Privacy-Preserving Proximity Tracing project (PEPP-PT) claims to enable tracing of infection chains across national borders. Both of these efforts claim to be privacy preserving technology efforts at contact tracing. Why are technologists choosing to respond to COVID-19 with contact tracing solutions? And are they protecting privacy?

Background

Some things we know about COVID-19:

• It is a zoonotic disease (a disease of animals) for which humans have no natural immunity.

• Indications so far are that the number of people who actually fall ill is a small percentage of the infected. Perhaps as many as 80% of those infected show no or only slight symptoms, they are still contagious.

• The symptoms of the COVID-19 are unspecific and similar to other common and minor illnesses.

• There is not effective vaccine or real treatment (as with most viral diseases). Treatment consists of treating the symptoms.

• Only a reliable test procedure will identify affected persons.

We know it will take more than a year to develop a vaccine or treatment. The rational policy response is to limit the spread of the disease so that treating the symptoms of the seriously ill will not overwhelm the health system, or ‘bend the curve’. The primary way to accomplish this is for people to modify their own behaviour. What each person chooses to do can save lives. To avoid becoming infected people should practice physical (social) distancing, good hygiene, and clean regularly. Governments globally have issued various forms of stay-at-home orders to implement this policy, with varying levels of enforcement severity. And many governments are looking at contact tracing as a way to enforce or reinforce these policies. But will contact tracing give better and useful data? And will it substantially change people’s behaviour? And are there any risks associated with this kind of contact tracing?

Contact Tracing

Sound policy depends on good epidemiological models, and that modelling depends on understanding people’s actual behaviour combined with accurate data. On the face of it, therefore, developing and implementing contact tracing technologies would seem to provide better data and therefore be a worthwhile endeavour.

Contact tracing involves three steps:

• Contact identification — identify the people that are likely to have been in contact with someone with a confirmed infection

• Contact listing — list and contact all the identified persons with information about the disease and recommended responses.

• Contact follow-up — Monitor and test for symptoms

Note that both the first and third steps require testing for the disease.

According to their press release, “Google and Apple are announcing a joint effort to enable the use of Bluetooth technology to help governments and health agencies reduce the spread of the virus, with user privacy and security central to the design.” They are proceeding in two steps. Their first is to release an Application Programming Interface (API) to enable developers for mobile devices to develop software for contact tracing. When people use apps built on this API, they will self report that they have tested positive for COVID-19 and other uses of the app that have been near them will be notified that they have been in contact with a COVID-19 positive person. This will be done by exchanging encrypted keys on a central server so that devices can search for contacts. The second step will be to build elements of this functionality into their respective operating systems such that any Android or IOS phone will be able to do contact tracing.

The Pan-European effort is similar. By using anonymous identifiers and storing location history on each person’s device, the system endeavours to reduce or eliminate the disclosure of information without the person’s consent or knowledge. Like the Apple/Google effort, a centralized and trusted service is required to mediate the system. As far as this system is concerned there are three kinds of people:

1. People who are not infected yet.

2. People who are infected and contagious but who have no symptoms

3. People who are infected and contagious and who are showing symptoms.

Other firms are jumping in, but with motives less apparently altruistic. Corona virus tracing, after all, creates a new market opportunity.

If these technologies are implemented, how many people will be contacted who will then need to be tested? As of April 11, 2020, the Government of Canada reported 404,651 tests resulting in 23,301 confirmed cases of COVID-19. If I assume that each of those people had an average of 3 contacts and that contact tracing needs to go back two weeks, that means that 978,642 Canadians need to be contacted and tested. And if it turns out that each of those 3 people generates another contact list of 3 people then the number explodes to 2,935,926. That is 8.4% of the Canadian population. The number is much larger if you consider that the consensus is that the reported cases are a much smaller number than the actual cases in the population. When the potential numbers of people needing to be contacted and tested is such a significant portion of the population, the question becomes whether or not the effort necessary to do contact tracing is the best use of resources? Should we skip the contact tracing and go right to mass testing and random testing to obtain the necessary data as quickly as possible. In either case, the clear necessity is a very large number of tests in a short period of time.

It should also be noted that implementing contact tracing through smart phone API’s and smart phone operating system updates raises the possibility that only people with reasonably current model, or expensive phones, will be able ‘take advantage’ of the new systems. In other words, this approach raises the possibility of, yet again, exacerbating digital divides and putting the poor and marginalized at greater risk.

If the benefits of contact tracing are less than they first appear, what about the risks? The technology proposals, notwithstanding the claims to be privacy protective, increase the speed of transition to a surveillance society. It is already the case that everyone with a smart phone is carrying a self-identifying surveillance device. These proposals create the surveillance infrastructure to accompany the surveillance device. The projects’ claims to be privacy protective are contradictory. Both apply privacy by design, and build their conception of privacy into their proposals. But does the current technological conceptualization of privacy align with peoples’ lived experiences?

Privacy shouldn’t be thought of as secrecy or be based on the notion of people seeking solitude. Privacy is sometimes characterized as the “The right to be left alone” but that fails to capture the essence of privacy. The formulation of privacy as the claim of individuals to determine what information about them is communicated to others [Westin, Privacy and Freedom] is better. But privacy is not just an individual right. To think that is to miss the fundamentally social and relational nature of privacy. Whether I choose to share intimate details of my life with a close friend, or I choose to share an interesting life experience with a group of colleagues at a social event, I am choosing what I disclose and to whom using a set of social rules and conventions that I share (usually) with the person or people with whom I share the information. Information disclosed this way becomes co-managed and jointly owned. Systems based on ‘notice and consent’ or ‘terms of use’ do not work this way. Rather they involve transferring management of the information to another and losing control. This approach or understanding of privacy works for ‘surveillance capitalism’ but tends to be disempowering for individuals — even in the context of contact tracing based on centralized services and systems. These proposals have a potentially dystopian end-state.

I’m pretty sure that someone, somewhere, has come up with the idea that the unit cost per population for a wrist worn device that has GPS and Bluetooth with a QR code display would be cheap and allow for population wide mandatory contact tracing — and any other population surveillance that the state deems appropriate. What could possibly go wrong?

Notwithstanding the contact tracing technologies described above, it is likely that accurate and reliable epidemiological models of the current pandemic will not be available until after the pandemic has receded and full data sets are available. Those models can be used to improve preparations for future outbreaks and to provide lessons for the next ‘new’ virus outbreak. In the meantime a better use of our limited resources right now would be to expand testing. This will provide better data now, enabling more people to self-isolate and to inform governments and others where to provide the supports necessary for individuals and health service providers.

We need a social solution, not a technical solution, to limiting the spread of this type of pandemic[^As opposed to the clear and present necessity for contact tracing for a viral infection like Ebola]. Governments that proactively and transparently engage and inform their citizen’s have the best chance of dealing with this pandemic without risking the creation of an authoritarian toolkit available for any future government to pick up.

Addendum: See Ross Anderson’s post “Contact Tracing in the Real World” for a leading security expert’s view on this.

This is an updated version of a post originally published on the JLINC blog (March 21, 2018).

Alice and Bob are the primary characters in a cast of characters first used in cryptographic circles as placeholder names to represent different parties in a transaction. The story goes like this:

Alice wants to send a message to Bob, but she doesn’t want anyone but Bob to be able to read it. Bob also doesn’t want anyone to read his messages to Alice. They do this by securely exchanging special ‘keys’ to exchange documents and data.

All well and good so far. This transaction and this representation make sense because Alice and Bob are peers. They have a common interest in having a secret communication and have the same capability – defined in cryptographic protocols. In terms of this exchange, Alice and Bob are equals. And because Alice and Bob are such a cute couple they have come to represent many other different types of transactions on the web. But when we take Alice and Bob out of the crypto context and use them as placeholders for normal web transactions, their relationship is different. There is no equality via protocol. By and large these relationships are defined by the privacy policies and terms of use of the websites that Alice visits. And these do not define a relationship between peers.

The utility of privacy policies

Privacy policies are created and published, by and large, for the purpose of reducing corporate risk. In that sense, they serve the same purpose as any warranty that you receive with any product. If you read the warranty on your latest household appliance you will probably find, after translating the legalese into English, that the company provides very little in the way of an actual guarantee. Privacy policies serve the same purpose. In a 2014 survey The Pew Research Centre asked people if having a privacy policy means that a company will keep user information confidential. The correct answer is, sadly, “FALSE”. But only 44% of respondents answered correctly. 56% of users answered “TRUE”.

Forbes found one refreshingly honest privacy policy that ended with the phrase “All your privacy are belong to us. We mean it.”

Terms of Use are similarly weighted away from Alice. These are standard form contracts or ‘contracts of adhesion’ where the terms are set by the website and Alice is effectively left with two options: take it; or leave it.

When Alice is surfing the web she is typically using sites set up by companies or organizations. When Alice visits one of these sites, she implicitly or explicitly agrees with each site’s “Terms of Use” and/or implicitly consents to their “Privacy Policy”. But unlike the key exchange between Alice and Bob, this not a relationship between equals. The top 5 most visited sites on the web are (data from Alexa):

Globally

• Google

• YouTube

• Tmall

• Qq

• Facebook

United States

• Google

• Youtube

• Amazon

• Facebook

• Yahoo

Does it really make sense to think of Google, YouTube or Facebook as Alice’s “peer” in the way that Alice and Bob are peers for exchanging cryptographic keys? Obviously not. There is a serious power imbalance between this Alice and this Bob. It would really be better to think of these relationships as between Alice and Bob Company or BobCo (or even EvilBobCo). The nature of this imbalance, combined with the dependence of these web sites on advertising for revenue, creates a natural motivation for designers to use 'dark patterns' to ensure that users stay on the sites by whatever means necessary.

As a result of this imbalance, facilitated by dark designs, BobCo is able to sell Alice’s data, or access to Alice’s web page when she is on Bob’s site, to Charlie who in turn can make Alice’s information available to as many organizations as are interested in that data. This uncontrolled trade in data and almost complete lack or respect for Alice’s privacy or Alice as a person is the basis for the current business model of the Internet and what is meant by the phrase, “If you’re not the customer, you are the product”. Thousands of companies are competing for the chance to process your data, probably unbeknownst to you.

And, by the way, individually you are not that valuable a product. In 2019, Facebook reported over $69 billion in global advertising revenue. But when calculated on a per user basis that translates to less than $30. So your entire years worth of Facebook viewing and clicking is worth $30 to Facebook. At that price, you have little or no leverage to influence any of Facebook’s policies or attitudes. You and Facebook are not equals in the relationship.

Personal Data Value Chain

So Alice and Bob are not equals. And the structure of the relationship between users and services encourages the extraction and monetization of personal data without meaningful consent. This is not a trade-off of service for a value that most people would make if they thought that they had a choice. This is the so-called “Tradeoff Fallacy”. It’s been known since at least 2009, for example, that Americans reject tailored advertising when it is explained to them. Until Alice can monitor and control what happens to her data, she cannot hold Bob accountable and the exploitation of her data will continue.

There is work being done to address this situation. In the Kantara Initiative, the User-Managed Access Work Group has released the UMA protocol extending OAuth’s capability to enable Alice to control who has access to her resources. Similarly, Kantara has produced a consent receipt specification to enable accountability and transparency in consent. The Vendor Relationship Management project (ProjectVRM) has sparked or inspired dozens of companies or initiatives to empower users in the commercial space, including the Me2B Alliance.  MyData Global is an NGO dedicated to the proposition that a person should be at the centre of their own data. The JLINC protocol is an open protocol that will enable Alice to do exactly the kind of monitoring and have the kind of control over her data that is missing.

In the meantime, the next time you read about Alice and Bob, think about the context and draw your own conclusion about the nature of that relationship. Most of the time, I suspect, you will find that it is Alice and BobCo/EvilBobCo and that whether Bob is evil or not, Alice has no power.

Bob is a bully and needs to be stopped.

This post was originally posted on Medium in 2017.

We live in an age of Three Letter Acronyms (TLA’s). It’s a function, perhaps, of increasing levels of specialization and jargon combined with a certain social impatience that devalues vocabulary (Can you distinguish between the correct usages of transform and transmogrify)? But if I set aside the snark it is still true that jargon and the use of TLA’s can make communication within a community more effective and efficient. The correct use of TLA’s also becomes a sign that you are part of the community, or can act as a barrier to entry to the community, but that’s another story (RTFM, LOL).

One acronym that gets tossed around in some communities is “BLT”. This refers to the parallel layers of Business, Legal, and Technical issues that often need to be resolved to move from problem to solution. It turns out to be a hard problem to have a Business relationship with Legal affect that can be easily represented in Technical artefacts.

For example, the business challenge of monetizing a website by processing personal information is currently addressed by the legal artifice of a TOU (Terms of Use) that we are magically supposed to have read and assented to before using a site. This is technically implemented by a readily available link to the Terms of Use on the bottom of the web page. But nobody reads those terms. The user experience is terrible (who actually likes listicles?). And, from a privacy and user autonomy perspective, the notice and consent provided by standard form contracts is weak at best, and does not work at Internet scale.

We have not yet come up with a good tool to connect the business, legal, and technical layers. More specifically, the problem is connecting the technical layer to the other two. There are centuries of development and tools for connecting the business and legal levels. The question becomes how to reliably connect the technical and legal layers in a way that simultaneously satisfies the requirements of programmers and lawyers?

One solution is to try and encapsulate everything in the technical layer and avoid the necessity of connecting to the legal or business layer. Who needs lawyers and contracts when you have programmers and code, right? (Some will, and some won’t, read that as ironic). This was a design goal of the dao on ethereum and is one way to look at smart contracts.

Another solution is to express the elements of a legal agreement in a file that can be processed by software. These agreements are called Ricardian contracts and CommonAccord is an initiative to create global codes along these lines. The result includes what can be called wise contracts. They can be processed by software, and produced as text. But the use of Ricardian contracts is built into no Internet protocol that I’m aware of.

Neither of these solution seem satisfactory in the long run.

This is where a new TLA comes in. I was talking to Mike Schwartz from GLUU at the Internet Identity Workshop (IIW) recently. We chair different Kantara Initiative Workgroups but the discussion reminded me that this is a common problem — pinning the technical to the legal. The generic phrase that popped into my head was:

Legal Reference Token (LRT).

An LRT is intended to be a generic term to indicate an object, or file, that can be managed in software but that refers to a legal artefact that can be reliably produced on demand and which can be relied upon in the event of a dispute. This meets and connects the demands of the two layers. The legal layer will have access to the text and content of an agreement or contract for lawyers as necessary, while the technical layer has a technically manageable object to use. If you really want to stretch the metaphor, a legal reference token is a toothpick that connects the legal and technical layers in a BLT.

An example would be a copy of a contract negotiated and signed in a JLINC transaction. An LRT would be any artefact pointing back to the draft contract, the signed contract, or the hashed & signed record of the contract during or subsequent to the negotiation, agreement, and signing of the contract.

With all due reference to Asimov's Three Laws of Robotics. This post was originally published on a prior version of this blog on 2015-08-13.

1 No web site may violate a human being's right to privacy, or through inaction, allow a human being's privacy to be violated.

Inaction includes not taking responsibility for what ad networks are doing with your users' information.

2 A web site must respect a human being's consent with respect to her personal information, unless it conflicts with the first law.

You can't use dark patterns or obfuscation to claim that you have consent, or claim consent where the information may not even be 'consentable'.

3 A web site must protect its own existence as long as such protection does not conflict with the 1st or 2nd law.

A consequence of this will mean complying with legal requests for access to your users' data by law enforcement.

Bonus Guidance: You know you have it right when you confirm that your average user will not be surprised or upset upon finding out ANYTHING about how you process their data or who who share it with.

Originally written and posted for a prior version of this blog on 2013/07/09

With the announcement of IOS 7 elements of the blogosphere have become awash in commentary back and forth about the new design. Does the fact that Apple has chosen Helvetica Ultra Light as the default font have implications for privacy? Not so much. But privacy and design are connected, and all the commentary that I’m seeing about Apple’s new mobile operating system are focussed on the immediate and and the transient. This makes me think about Privacy by Design (PbD).

The focus around IOS 7 is on what the immediate user experience (IUX if you will). Focussing on the IUX is, I would argue, what gets organizations in trouble and does not meet PbD principle #1 - Proactive & Preventative. This is because the user experience of privacy is not immediate, except in the obvious egregious cases such as where web sites demand personal information for registration. A user’s privacy experience with an organization is cumulative and evolves transaction by transaction. 

This is not to say that the IUX is not important. Of course it is, and it is the result of well thought through user interface choices, one of which is Privacy by Design principle #2 - Privacy as the default setting. But have designers fulfilled their PbD goals by making privacy options both available and the default? Again, not so much. On the face of it, by doing this designers will have met most of the PbD requirements:

1. Designers have proactively included privacy interface features

2. The system has privacy protective default settings

3. The system has embedded privacy protective options

4. Designer ensures that there is full functionality 

5. Architects ensure the site is designed with end to end security

6. Privacy officers ensure that the privacy is visible and transparent

7. By focussing on UI and UX, designers assume that they are user-centric

So what’s the problem? It’s a variant of the old saw in computer programming, when the programmer asks for a set of requirements and builds a prototype for their customer. When shown the prototype, the customer shakes their head and says, “You’ve given me everything I asked for, but that’s not what I wanted." Privacy, it seems to me, is the same thing. If designers focus on the immediate experience they are likely to encounter unintended consequences down the road. Data that is accumulated over time is called longitudinal data. This is the kind of data that is used for epidemiological studies, or changes in a population over time. So I propose to borrow the term and suggest that Privacy by Design requires an understanding of the Longitudinal User Experience (LUX).

Only when system designers study the long term impacts on user privacy will they be proactively addressing and preventing privacy issues. This includes checking back with users on a regular basis for privacy status checks and validation, proactively notifying users of changes impacting their privacy and not implementing changes that could reasonably be construed to be less privacy protective than existing design choices. Above all, it means recognizing that privacy is embodied in the relationship and transactions with the users, not in a series of policy statements.

Bear with me, but this reminds me about a joke about a couple. She says, “You haven’t told me you love me in a long time." He replies, “I told you once, and I’ll let you know if the situation changes". That attitude doesn’t work in relationships and saying, “We told you that we would protect your privacy when you signed on to the service, and will let you know if that changes" doesn’t work that well either.

Meeting the PbD Proactivity principle means regularly engaging with your users about privacy, without beating them over the head with policy statements. Their user experience, in every transaction, needs to reflect your ongoing commitment to giving them control over the information you collect about them. Sometimes that means sacrificing immediate gratification for long term satisfaction. That’s how adults behave, and that’s how you prevent the need for remedial action.

Definitions

User Experience: According to the Wikipedia entry on User Experience: ISO 9241-210[1] defines user experience as “a person’s perceptions and responses that result from the use or anticipated use of a product, system or service". According to the ISO definition user experience includes all the users’ emotions, beliefs, preferences, perceptions, physical and psychological responses, behaviors and accomplishments that occur before, during and after use. The ISO also list three factors that influence user experience: system, user and the context of use.

User Interface: According to the Wikipedia entry on User Interface: The user interface, in the industrial design field of human–machine interaction, is the space where interaction between humans and machines occurs. The goal of this interaction is effective operation and control of the machine on the user’s end, and feedback from the machine, which aids the operator in making operational decisions. Examples of this broad concept of user interfaces include the interactive aspects of computer operating systems, hand tools, heavy machinery operator controls, and process controls. The design considerations applicable when creating user interfaces are related to or involve such disciplines as ergonomics and psychology.