After the Conference

A Short Story on the Ethics of Using AI Datasets with Unknown Provenance

This is fiction, but the ethical positions are loosely shaped by these thinkers’ public work on consent, bias, accountability, human character, sustainability, and long-term AI risk.  Jump toTL:DR

We ended up at a bar because that’s where these conversations go when the conference version of them runs out of oxygen.

Not a cool bar. Not a bad bar either. Just one of those in-between places near a convention centre where the lighting is too dim for networking and too bright for lying to yourself. Sticky wood tables. Half-heard conversations. Somebody laughing too hard near the back. Hockey on a muted screen.

I was there with a friend of mine who is smart enough to follow almost anything, but only if you stop talking like a brochure. Curious guy. Fast mind. A little overloaded by default. The kind of person who asks good questions right when everyone else is pretending they understood the answer.

He had that look on his face I’ve seen a lot lately.

Not anti-AI. Not pro-AI. More like: I can tell something important is happening, and I don’t trust the people selling it.

Fair.

By some weird post-conference gravity, the table filled up with Alice Xiang, Yoshua Bengio, Shannon Vallor, Abeba Birhane, and Aimee van Wynsberghe. Which sounds improbable, and maybe it was, but it also felt like one of those moments where reality briefly gets its act together.

My friend looked around at all of them, took a sip of his beer, and just asked it straight.

“Okay. Simple version. Is it ethical to use AI datasets when we don’t really know where the data came from?”

Good question.

Actually, better than good. Clean. No fluff. Cuts right past the branding.

Alice was the first to answer, and she answered the way people do when they’ve spent enough time around bad systems that they no longer bother decorating the problem.

“If you don’t know where the data came from,” she said, “you probably don’t know who got pulled into the system without meaning to be. And if you don’t know that, then you don’t really know what you built.”

She didn’t say it dramatically. That was the thing. No big performance. Just matter-of-fact.

She talked about how even the basic act of checking for bias becomes slippery when the underlying data was collected in ways nobody can really account for. If the dataset itself is a mess, then all the fairness language stacked on top of it starts to feel a bit fake. Like quality control on stolen parts. Her public work has pushed hard on ethically sourced, consent-based benchmarking because otherwise even your “measurement” of fairness can be contaminated from the start. 

My friend nodded slowly.

“So that’s the rules argument?”

“Partly,” I said.

Alice shrugged. “Call it duty, call it legitimacy, call it basic respect. If people didn’t agree to be in the system, and you can’t explain how their data got there, you’ve got a moral problem before you even get to model performance.”

That landed.

Then Bengio leaned in, hands around his glass like he was trying to warm them.

“I agree,” he said. “But even if you put consent aside for a second, you still have to ask what the system is doing in the world. What scale of harm are we enabling? What future are we normalizing?”

That was his lane. Not in the cartoon way people sometimes frame him, but in the sober way of somebody who has spent years looking at capability curves and not liking where they point. His recent work has focused heavily on advanced AI risk, safety-by-design, and the need to take longer-term consequences seriously instead of treating them like science fiction until it is too late. 

He kept going.

“Bad data is not just an origin problem. It becomes an outcome problem. Healthcare systems. Hiring tools. Decision support. Misinformation. Surveillance. If the system scales, the harm scales.”

My friend looked at me. “So that’s more like consequences.”

“Yeah,” I said. “Not just ‘was the input clean?’ but ‘what happens when this thing leaves the lab and starts deciding who gets seen, hired, flagged, priced, diagnosed, trusted.’”

Abeba gave this tiny nod, like the word trusted had annoyed her a bit.

“I think people jump too fast to abstract ethics,” she said. “The issue is also power. Whose data gets taken. Whose labour gets hidden. Whose communities get misrepresented. Whose harm gets treated as acceptable collateral.”

There it was.

The room didn’t go quiet exactly, but the table did.

Because that’s the part a lot of tech conversations try to skip past. They want to talk about bias like it’s a technical imperfection. As if we are all standing the same distance from the blast radius.

We are not.

Birhane’s work has repeatedly exposed how large internet-scraped datasets can scale hateful, racist, misogynistic, and otherwise harmful content rather than wash it out, and how those costs often fall hardest on already marginalized people. She has also been blunt about how opacity and exploitative labour get buried inside the AI supply chain. 

“So no,” she said. “It’s not just ‘we don’t know where the data came from.’ It’s that we often do know enough to know it’s dirty, and we keep going anyway because the incentives reward scale.”

My friend exhaled through his nose.

“Okay. That one I understand.”

Shannon Vallor smiled a little, but not because anything was funny.

“What interests me,” she said, “is what habits we are building in ourselves while we make these systems. What kind of people do we become when convenience matters more than wisdom, when efficiency matters more than care, when imitation matters more than understanding?”

That could have gone pretentious in someone else’s mouth. It didn’t.

She made it feel practical.

Like this wasn’t some seminar detour into the soul. It was about culture. Practice. Repetition. The moral grooves we carve by doing the same thing over and over and calling it innovation.

Her public work asks whether AI strengthens virtues like wisdom, care, and creativity, or instead reinforces less humane patterns. That’s a different kind of ethics problem. Not just “what rule was broken” or “what harm occurred,” but “what are we training ourselves to value?” 

My friend laughed once.

“So basically: if we build systems by acting like creeps, maybe that does something to us.”

Shannon lifted her glass. “That is one way to put it.”

Then Aimee jumped in, and she widened the frame.

“You also have to ask what it costs to sustain these systems,” she said. “Not just socially, but environmentally, institutionally, economically. People keep acting like ethics ends at the user interface.”

She talked about infrastructure, energy, extraction, regulatory design, and how “responsible AI” can become too narrow if it only looks at immediate user harms while ignoring the wider system. Her work at Bonn’s Sustainable AI Lab has been focused exactly there: the environmental, social, and economic costs of designing, developing, and using AI, and the need for green, proportionate, sustainable governance rather than ethics theatre. 

“An AI system can be consent-based and still be irresponsible,” she said. “It can still concentrate power. Still burn resources. Still create dependency. Still reward bad institutional behavior.”

That was the turn.

Because until then, the question had been: is it ethical to use data when you don’t understand where it came from?

And the table, more or less, had said: no. Or at minimum: not without serious moral debt attached.

But then my friend asked the better follow-up.

“Fine. Let’s do the fantasy version. Clean slate. Everybody consented. Everybody got paid. Full transparency on data collection. Now what? Is it ethical then?”

Nobody answered right away.

Which I appreciated.

Because if someone answers too fast there, they’re usually trying to sell you a framework, not tell you the truth.

Alice went first again.

“Then you need provenance that stays visible,” she said. “Not just once, not in a press release. Ongoing traceability. What data was used, under what terms, with what limitations, for which purpose.”

“Like chain of custody,” I said.

“Exactly.”

Not because transparency magically fixes everything, but because without it accountability becomes theatre. If the model causes harm and nobody can tell what went into it, who approved it, who profited from it, or what testing was done, then “responsible AI” is just branding with nicer fonts.

Bengio picked it up from there.

“And then rigorous evaluation,” he said. “Not benchmark worship. Real testing in real contexts, with uncertainty taken seriously.”

Healthcare came up first.

Say you build an AI tool to help detect disease from scans. Great. On paper. But who trained it? On what patient populations? Does it perform equally well across age groups, skin tones, equipment standards, hospitals, regions? What happens when doctors trust it too much? What happens when administrators use it to cut staff because the spreadsheet says the machine is good enough?

That’s the thing with AI. The model is never the whole story. The deployment context is part of the morality.

Same with hiring.

A system that filters candidates can be built on clean, consented data and still end up reproducing class bias, educational bias, language bias, disability bias, all kinds of bias. Because fairness is not just about whether the data was obtained properly. It’s also about what success variable you optimize for, what proxies you use, and who gets treated as a statistical inconvenience.

Abeba was sharp on that.

“People love saying ‘bias testing’ like it’s a final exam,” she said. “It isn’t. Fairness is ongoing. Representation is ongoing. Audit is ongoing. Communities affected by the system need meaningful ways to challenge it.”

Not symbolic consultation. Actual leverage.

In creative work, things got even messier.

Photography. Writing. Illustration. Music.

The friend looked at me because he knew that one would hit home.

If creators are consented and compensated, good. Obviously better. Much better. But then new questions show up.

Who gets included and who gets left out?

What happens to the market when synthetic abundance drives down the value of human work anyway?

What gets rewarded, the original creator, the platform, the model company, the distributor, the prompt engineer, the client who now expects ten concepts for the price of one?

Consent matters. Payment matters. But economic displacement is not solved just because the intake form was legal.

Aimee made that point cleanly.

“You have to evaluate system effects, not just transaction ethics,” she said. “A tool can be fair at the point of data collection and still unfair in the world it helps create.”

That one stayed with me.

Because it cuts through a lot of fake moral certainty.

The bar had thinned out by then. Chairs upside down on some tables. Someone wiping down bottles. That end-of-night feeling where everybody’s voice gets a little more honest because there’s no point posturing anymore.

My friend looked around the table.

“So what does ethical AI actually look like?”

Shannon answered softly.

“Probably less like a product and more like a practice.”

Then, after a second:

“Transparency. Accountability. Human oversight. Contestability. Limits. Some uses should be slowed down. Some maybe should not exist. And all of it should be shaped by the kind of future we actually want to inhabit, not just the one we are technically capable of building.”

That felt right.

Not neat. Right.

Because the honest answer is that consent alone is not enough. Compensation alone is not enough. Clean data alone is not enough.

You can solve the origin problem and still build a system that centralizes power, erodes trust, automates discrimination, floods the public sphere with synthetic sludge, pressures human workers, and gives institutions one more excuse to avoid human judgment while pretending to be objective.

Ethical AI, if that phrase is going to mean anything at all, has to be ongoing.

Not a checkbox.

A living system of traceability, audit, restraint, governance, challenge, revision, and actual responsibility when things go wrong.

And even then, some tension remains.

Because there will still be tradeoffs.

Still grey zones.

Still people pretending uncertainty means permission.

Still companies treating ethics like a cost centre.

Still real benefits in medicine, accessibility, science, and education pulling in one direction, while concentration of power, labour disruption, environmental cost, and manipulation pull in the other.

No tidy ending.

Probably a good sign.

When we finally got up to leave, my friend put on his jacket and said, “So the answer is basically: if you don’t know where the data came from, you’re already in trouble. And if you do fix that, congratulations, now the real ethics work starts.”

Pretty much.

Outside, the street was wet and reflecting the city back at itself in broken pieces.

Which, now that I think about it, is maybe the closest I’ll get to a metaphor.

TL;DR

• If you do not know where AI training data came from, you have a legitimacy problem before you even get to performance.

• Consent and compensation matter, but they do not solve everything.

• Ethical AI also needs transparency, provenance, traceability, and clear accountability.

• Fairness is not a one-time benchmark. It needs ongoing testing, audit, and challenge.

• Real-world deployment matters as much as model design, especially in healthcare, hiring, media, and law.

• Creative industries are not “solved” just because creators were paid. Market effects and power concentration still matter.

• Ethical AI is not a badge. It is a system of ongoing human judgment, oversight, and restraint.

If you would like to see the reasoning behind this opinion, please read the white paper.

Prep Is Love: How I Work With People

A short version of how I think, how I work, and what tends to make collaboration go well.

The short version

I value truth, clarity, and respectful collaboration.

I work best in calm environments where people are clear with each other, roles make sense, and everyone is trying to solve the actual problem instead of dancing around it.

If you like good work, clean communication, and consistent results, we’ll likely work well together.

My north star

To live and build as a free person using truth, systems, and disciplined boundaries, guided by compassion and clear standards, to produce outcomes that matter.

Yes, it took me a while to write that last sentence. In plain English: I build structure so decisions stay clear and creativity can breathe.

That applies to photography, production, collaboration, and life in general.

The story underneath it

I learned early that you can’t control when storms arrive. You can, however, build a life that doesn’t fall apart every time one rolls in.

Later, I learned the second half of that lesson. Being strong is not the same as being sealed off. A strong house still needs windows. It still needs a door. Ideally one that opens for the right people.

That’s a big part of why I build systems.

Not because I want everything rigid.

Not because I want to turn human beings into checklists.

And definitely not because I enjoy process for its own sake.

I build systems so things stay clear, calm, and workable. Good structure protects the human side of the work. It keeps people from scrambling, guessing, overreacting, or burning time on things that could have been handled earlier with one decent conversation.

That’s true whether I’m photographing a leadership team, supporting a production workflow, planning an on-location shoot, or helping shape a more complex visual project.

What I believe

A few principles tend to guide how I work and how I relate to people.

1. Ideas shape outcomes

Teams, projects, and institutions usually grow in the direction of the thinking behind them. If the thinking is sloppy, the result often is too.

2. Systems beat blame

Most recurring problems are not character flaws. They come from weak structure, unclear ownership, bad timing, vague communication, or missing feedback loops. Usually, it’s simpler than it first appears.

3. Truth early

I’d rather see reality early than be surprised later. A difficult truth now is usually kinder than a preventable problem at the deadline.

4. Clarity is respect

Clear expectations, clear ownership, and clear next steps make work calmer and more humane. People do better when they’re not forced to read minds.

5. Freedom through structure

I care about freedom, but not the sloppy version. The kind I trust comes from preparation, boundaries, and good decisions made early.

6. Dignity is non-negotiable

Direct does not need to mean harsh. High standards do not require humiliation.

7. Compassion and clear standards belong together

Warmth without standards gets vague. Standards without warmth get cold. Good work usually needs both.

8. Honest mistakes are human

I’m very forgiving of skill gaps, learning curves, and real mistakes when there’s ownership and improvement. People are allowed to be human.

9. Accountability builds trust

If something slips, I want to name it early, fix it cleanly, and improve the system so it doesn’t keep happening.

10. Substance over signaling

I care more about behaviour than branding, more about follow-through than speeches, and more about reality than theater.

11. Clear agreements, shared visibility

Trust matters. So do transparency, defined review points, and the ability to adjust as the work evolves.

12. Direct collaboration, shared outcomes

I work best as an independent partner with clear roles, mutual respect, and a focus on delivering strong results together.

What you can expect from me

Here’s what people can generally expect when they work with me:

Clarity, quickly

You’ll usually know what’s happening, what’s next, and who owns what.

Calm delivery

I plan ahead so the work stays steady, even when the environment is busy or high-pressure.

Direct feedback

I’ll say the real thing early and respectfully.

Clean scope

If the ask changes, I’ll name it and adjust the plan, timeline, or quote accordingly.

Reliable follow-through

If I commit to something, I take that seriously.

In practice, that usually means:

• If something is at risk, I’ll tell you as soon as I see it, not at the deadline.

• If a decision is needed, I’ll usually give you a few options and the tradeoffs.

• If the plan is unclear, I’d rather pause and clarify than pretend and waste a day.

What I need from you

Good collaboration is mutual. I work best when the people around me bring the same kind of honesty and steadiness they want from me.

What I need most is:

Honesty and clarity

Tell me the real constraint, even if it’s uncomfortable.

Follow-through, or early warning

If something is not going to land, I’d much rather know early and re-plan cleanly.

Good-faith disagreement

Say it directly. We can work with direct. Silence and resentment are a much slower way to work.

Respect for roles, time, and standards

Good work gets easier when people know what they own and take that seriously.

In practical terms:

• If a deadline is slipping, tell me early.

• If priorities change, let’s update the scope and expectations.

• If there’s friction, let’s address it directly and respectfully before it turns into confusion or drift.

How I work best

Communication

I prefer clear writing and simple check-ins, with meetings used when they add value.

When we do connect live, I appreciate having a clear purpose, a light agenda, and a defined outcome so everyone’s time is well spent.

I welcome concerns early. Addressing things as they come up usually keeps everything moving smoothly.

And if I’m brief in a tense moment, it’s usually just me staying focused on keeping things calm and on track.

Decisions and execution

I like decisions tied to a goal, owned by someone specific, and reviewed afterward.

I move quickly when priorities are clear. When things slow down, it’s usually because the goal or scope needs to be clarified.

On set or in production environments

This is where one of my favourite lines applies:

Prep is love.

Good prep is not bureaucracy. It’s care.

We confirm the plan, roles, timing, and constraints before we roll so nobody has to panic later and mistake urgency for process.

I care about calm execution. I care about people being treated well. I care about good work happening without avoidable chaos.

To me, respect is part of the workflow.

Fit check

A good fit usually looks like this:

• clear goals

• realistic timelines

• direct communication

• decisions made on time

• respect for craft and roles

• a shared interest in doing things properly

A poor fit usually looks like this:

• vague goals

• shifting scope without naming it

• preventable last-minute urgency

• indirect or avoidant communication

• wanting high standards without accountability

Not every mismatch is personal. Sometimes people just want different working styles. Better to know that early than force it and call it collaboration.

Repair and mercy

I’m human, and I work with humans.

I’m forgiving of honest mistakes and skill gaps when there’s ownership, learning, and visible improvement. I do not expect perfection. I do expect honesty.

What I’m not available for is repeated broken agreements without accountability, or performative accountability without changed behaviour.

Trust is rebuilt through changed behaviour, not speeches.

I’m not perfect, and I hope for the same compassion and accountability in return.

The point

The point of all this is not control for its own sake.

I build systems so I can stay free, tell the truth, do excellent work, and still leave room for real collaboration.

I care about outcomes. I care about dignity. I care about keeping the work steady enough that people can do their best inside it.

I build.

Strong enough for pressure.

Clear enough for trust.

Human first.

The Story Under the Story

Some projects arrive like a business plan.

Some arrive like a sentence you can’t shake.

Storm-proof started with an image: a child in bed during a thunderstorm, listening to the house shake, trying to make sense of the noise, the uncertainty, and the feeling that the world can change faster than you are ready for it.

From there, the story began to unfold. A boy named Tommy decides he does not want to be surprised by storms anymore. So he starts preparing. He organizes his room. He makes a shelf for the things that help. He learns routines. He learns where the flashlight goes, where the batteries are, what to check, what to do. In one sense, it is a story about preparedness. About steadiness. About the quiet comfort of knowing where things live.

But that is only the outer layer of the story.

The deeper part of Storm-proof is about something many children, and many adults, know well: the difference between being safe and feeling calm. You can build strong walls. You can make good plans. You can become the responsible one, the organized one, the one who has it together. And still, somewhere inside, the storm can keep going. That is the turn in this story. Tommy learns that preparation matters, but it is not the whole answer. The house can be strong without becoming a bunker. The windows can open. The door can let the right people in. Calm does not always come from control alone. Sometimes it comes from connection.

That idea is personal for me, which is probably why the project stayed with me. I have long been interested in the ways people learn to hold steady through uncertainty, and in the structures we build to protect ourselves. Sometimes those structures help. Sometimes they harden into armour. Storm-proof is my attempt to explore that honestly, but in a form simple enough for a child and meaningful enough for the adult reading beside them.

Right now, the project is still in development. I’m working on both a picture-book version and a longer written version, and I’ve been using AI-generated imagery as a visual development tool while I explore tone, character, setting, and pacing. Those images are helping me think through the world of the book, but they are part of the draft process, not necessarily the final destination. The goal is to keep refining the emotional core of the story and, in time, move toward a finished edition with a more fully realized visual identity.

So that’s where Storm-proof stands at the moment: part story, part sketch, part personal excavation, and part children’s book in the making. I’m sharing it now because some projects need to be seen while they are still becoming. Below, you’ll find a link to the current draft materials and visual explorations.

Biography

Cedric Swaneck is a Toronto-based photographer, visual storyteller, and workflow-minded creative whose work brings together portraiture, documentary observation, and the technical craft of photography. Over more than two decades, his path has moved through darkrooms, digital capture systems, corporate boardrooms, remote northern communities, television productions, and long-form personal projects, all tied together by a consistent interest in people, place, and truth.

Born in Toronto to a family with Chilean and South African roots, Cedric grew up between cultures and between countries. His life has been shaped by movement between Canada and Chile, and by the contrasts that come with that: stability and rupture, belonging and outsiderhood, structure and improvisation. Those experiences gave him an early sensitivity to the difference between surface and substance, and that sensitivity continues to shape both the way he photographs and the kinds of projects he is drawn to.

Photography first took hold in the darkroom. What began as fascination gradually became a discipline, then a vocation. He was drawn not only to the image itself, but to the balance photography offers between observation and interpretation, truth and construction, instinct and control. As the industry shifted from analog to digital, Cedric moved with it, developing a parallel strength in workflow, image systems, and the technical side of the medium. That dual background, one part visual and one part structural, remains central to the way he works today.

His professional experience spans executive portraiture, team and organizational photography, on-location production support, digital capture, metadata and asset workflows, and stills support within film and television environments. He has worked in settings where the image matters, but where so do trust, speed, logistics, and the ability to stay calm when there are many moving parts. Over time, he developed a reputation not only for making strong images, but for building the conditions that make good work possible: preparation, clarity, technical fluency, and steady execution under pressure.

A formative chapter of that path unfolded in northern Canada. Over several years of assignment work, Cedric travelled through remote communities across northern Ontario, Manitoba, Saskatchewan, the Northwest Territories, the Yukon, northern Quebec, and beyond. Those years gave him more than mileage. They sharpened his self-reliance, deepened his understanding of Canada beyond its southern urban centres, and left a lasting impression on the way he thinks about distance, place, community, and the emotional weight of geography. The North was not just a backdrop. It was a teacher: beautiful, demanding, solitary, and full of realities that do not reveal themselves quickly. That experience continues to inform both his portrait work and his longer-term documentary interests.

Portraiture has become one of the clearest expressions of his practice. Cedric is especially interested in portraits that feel composed without becoming stiff, polished without becoming artificial, and human without losing strength. Whether he is photographing an executive, a creative, or someone far from the public eye, he is interested in the same thing: making an image that feels credible, grounded, and alive. He approaches portraiture as both craft and encounter, with an emphasis on dignity, presence, and trust.

At the same time, his work has never been limited to conventional commercial lanes. Cedric has long been drawn to the broader cultural and emotional territory around photography: memory, identity, regional life, migration, class, work, and the relationship between people and the landscapes they inhabit. These interests continue to inform his independent projects, particularly those connected to Canada, the North, and the larger question of how people see one another across distance and difference.

Before photography became his full professional centre, Cedric studied theology and philosophy, an experience that left a lasting mark on his thinking. Those years deepened his interest in history, ethics, belief, and the structures that shape human behaviour. They also sharpened his instinct to look beneath appearances rather than settle for easy narratives. That intellectual background still travels with him. It shows up in the kinds of questions he asks, in the themes that recur in his work, and in the way he thinks about image-making not just as production, but as a form of attention.

This mix of visual craft and systems thinking has also made him unusually comfortable at the intersection of art and technology. Cedric has taught advanced workflow concepts, worked inside complex digital capture environments, and built practical systems for photographers and productions that need reliable handoffs, metadata discipline, and scalable image organization. He is not interested in technology as novelty. He is interested in tools insofar as they support authorship, accuracy, clarity, and better working conditions for creative people.

That practical side of his work sits naturally alongside a more reflective one. Cedric’s broader creative ambition is not only to produce strong commissioned work, but to build a body of photographs and projects that contribute to how people understand identity, place, and one another. He is especially interested in Canada as a lived and complicated idea, in portraiture as a form of witness, and in the possibility that images can still carry honesty in a time shaped by performance, speed, and distraction.

Across all of this, a few values remain constant. He cares about truth over polish, competence without unnecessary drama, and systems that serve the work instead of flattening it. He believes that structure, at its best, creates room for better seeing, better collaboration, and better outcomes. He also believes that the strongest photographs are rarely only about style. They are about attention, trust, and the discipline to stay present long enough for something real to emerge.

Cedric’s work is grounded in portraiture, visual storytelling, and the technical craft that supports both. Whether he is photographing people, building workflows, or developing independent projects, he is ultimately drawn to the same challenge: making work that feels clear, human, and true.

Accordion Man, TTC Finch Station, Toronto, around 2002. An old digital scan from the first roll of black-and-white film I developed and printed myself.