Human in the Loop

Picture this: You open your favourite AI image generator, type “show me a CEO,” and hit enter. What appears? If you've used DALL-E 2, you already know the answer. Ninety-seven per cent of the time, it generates images of white men. Not because you asked for white men. Not because you specified male. But because somewhere in the algorithmic depths, someone's unexamined assumptions became your default reality.

Now imagine a different scenario. Before you can type anything, a dialogue box appears: “Please specify: What is this person's identity? Their culture? Their ability status? Their expression?” No bypass button. No “skip for now” option. No escape hatch.

Would you rage-quit? Call it unnecessary friction? Wonder why you're being forced to think about things that should “just work”?

That discomfort you're feeling? That's the point.

Every time AI generates a “default” human, it's making a choice. It's just not your choice. It's not neutral. And it certainly doesn't represent the actual diversity of human existence. It's a choice baked into training data, embedded in algorithmic assumptions, and reinforced every time we accept it without question.

The real question isn't whether AI should force us to specify identity, culture, ability, and expression. The real question is: why are we so comfortable letting AI make those choices for us?

The Invisible Default

Let's talk numbers, because the data is damning.

When researchers tested Stable Diffusion with the prompt “software developer,” the results were stark: one hundred per cent male, ninety-nine per cent light-skinned. The reality in the United States? One in five software developers identify as female, only about half identify as white. The AI didn't just miss the mark. It erased entire populations from professional existence.

The Bloomberg investigation into generative AI bias found similar patterns across platforms. “An attractive person” consistently generated light-skinned, light-eyed, thin people with European features. “A happy family”? Mostly smiling, white, heterosexual couples with kids. The tools even amplified stereotypes beyond real-world proportions, portraying almost all housekeepers as people of colour and all flight attendants as women.

A 2024 study examining medical professions found that Midjourney and Stable Diffusion depicted ninety-eight per cent of surgeons as white men. DALL-E 3 generated eighty-six per cent of cardiologists as male and ninety-three per cent with light skin tone. These aren't edge cases. These are systematic patterns.

The under-representation is equally stark. Female representations in occupational imagery fell significantly below real-world benchmarks: twenty-three per cent for Midjourney, thirty-five per cent for Stable Diffusion, forty-two per cent for DALL-E 2, compared to women making up 46.8 per cent of the actual U.S. labour force. Black individuals showed only two per cent representation in DALL-E 2, five per cent in Stable Diffusion, nine per cent in Midjourney, against a real-world baseline of 12.6 per cent.

But the bias extends to socioeconomic representations in disturbing ways. Ask Stable Diffusion for photos of an attractive person? Results were uniformly light-skinned. Ask for a poor person? Usually dark-skinned. While in 2020, sixty-three per cent of food stamp recipients were white and twenty-seven per cent were Black, AI asked to generate someone receiving social services generated only non-white, primarily darker-skinned people.

This is the “default human” in AI: white, male, able-bodied, thin, young, hetero-normative, and depending on context, either wealthy and professional or poor and marginalised based on skin colour alone.

The algorithms aren't neutral. They're just hiding their choices better than we are.

The Developer's Dilemma

Here's the thought experiment: would you ship an AI product that refused to generate anything until users specified identity, culture, ability, and expression?

Be honest. Your first instinct is probably no. And that instinct reveals everything.

You're already thinking about user friction. Abandonment rates. Competitor advantage. Endless complaints. One-star reviews, angry posts, journalists asking why you're making AI harder to use.

But flip that question: why is convenience more important than representation? Why is speed more valuable than accuracy? Why is frictionless more critical than ethical?

We've optimised for the wrong things. Built systems that prioritise efficiency over equity, called it progress. Designed for the path of least resistance, then acted surprised when that path runs straight through the same biases we've always had.

UNESCO's 2024 study found that major language models associate women with “home” and “family” four times more often than men, whilst linking male-sounding names to “business,” “career,” and “executive” roles. Women were depicted as younger with more smiles, men as older with neutral expressions and anger. These aren't bugs. They're features of systems trained on a world that already has these biases.

A University of Washington study in 2024 investigated bias in resume-screening AI. They tested identical resumes, varying only names to reflect different genders and races. The AI favoured names associated with white males. Resumes with Black male names were never ranked first. Never.

This is what happens when we don't force ourselves to think about who we're building for. We build for ghosts of patterns past and call it machine learning.

The developer who refuses to ship mandatory identity specification is making a choice. They're choosing to let algorithmic biases do the work, so they don't have to. Outsourcing discomfort to the AI, then blaming training data when someone points out the harm.

Every line of code is a decision. Every default value is a choice. Every time you let the model decide instead of the user, you're making an ethical judgement about whose representation matters.

Would you ship it? Maybe the better question is: can you justify not shipping it?

The Designer's Challenge

For designers, the question cuts deeper. Would you build the interface that forces identity specification? Would it feel like good design, or moral design? Is there a difference?

Design school taught you to reduce friction. Remove barriers. Make things intuitive, seamless, effortless. The fewer clicks, the better. Less thinking required, more successful the design. User experience measured in conversion rates and abandonment statistics.

But what if good design and moral design aren't the same thing? What if the thing that feels frictionless is actually perpetuating harm?

Research on intentional design friction suggests there's value in making users pause. Security researchers found that friction can reduce errors and support health behaviour change by disrupting automatic, “mindless” interactions. Agonistic design, an emerging framework, seeks to support agency over convenience. The core principle? Friction isn't always the enemy. Sometimes it's the intervention that creates space for better choices.

The Partnership on AI developed Participatory and Inclusive Demographic Data Guidelines for exactly this terrain. Their key recommendation: organisations should work with communities to understand their expectations of “fairness” when collecting demographic data. Consent processes must be clear, approachable, accessible, particularly for those most at risk of harm.

This is where moral design diverges from conventional good design. Good design makes things easy. Moral design makes things right. Sometimes those overlap. Often they don't.

Consider what mandatory identity specification would actually look like as interface. Thoughtful categories reflecting real human diversity, not limited demographic checkboxes. Language respecting how people actually identify, not administrative convenience. Options for multiplicity, intersectionality, the reality that identity isn't a simple dropdown menu.

This requires input from communities historically marginalised by technology. Understanding that “ability” isn't binary, “culture” isn't nationality, “expression” encompasses more than presentation. It requires, fundamentally, that designers acknowledge they don't have all the answers.

The European Union's ethics guidelines specify that personal and group data should account for diversity in gender, race, age, sexual orientation, national origin, religion, health and disability, without prejudiced, stereotyping, or discriminatory assumptions.

But here's the uncomfortable truth: neutrality is a myth. Every design choice carries assumptions. The question is whether those assumptions are examined or invisible.

When Stable Diffusion defaulted to depicting a stereotypical suburban U.S. home for general prompts, it wasn't being neutral. It revealed that North America was the system's default setting despite more than ninety per cent of people living outside North America. That's not a technical limitation. That's a design failure.

The designer who builds an interface for mandatory identity specification isn't adding unnecessary friction. They're making visible a choice that was always being made. Refusing to hide behind the convenience of defaults. Saying: this matters enough to slow down for.

Would it feel like good design? Maybe not at first. Would it be moral design? Absolutely. Maybe it's time we redefined “good” to include “moral” as prerequisite.

The User's Resistance

Let's address the elephant: most users would absolutely hate this.

“Why do I have to specify all this just to generate an image?” “I just want a picture of a doctor, why are you making this complicated?” “This is ridiculous, I'm using the other tool.”

That resistance? It's real, predictable, and revealing.

We hate being asked to think about things we've been allowed to ignore. We resist friction because we've been conditioned to expect technology should adapt to us, not the other way round. We want tools that read our minds, not tools that make us examine assumptions.

But pause. Consider what that resistance actually means. When you're annoyed at being asked to specify identity, culture, ability, and expression, what you're really saying is: “I was fine with whatever default the AI was going to give me.”

That's the problem.

For people who match that default, the system works fine. White, male, able-bodied, hetero-normative users can type “show me a professional” and see themselves reflected back. The tool feels intuitive because it aligns with their reality. The friction is invisible because the bias works in their favour.

But for everyone else? Every default is a reminder the system wasn't built with them in mind. Every white CEO when they asked for a CEO, full stop, is a signal about whose leadership is considered normal. Every able-bodied athlete, every thin model, every heterosexual family is a message about whose existence is default and whose requires specification.

The resistance to mandatory identity specification is often loudest from people who benefit most from current defaults. That's not coincidence. It's how privilege works. When you're used to seeing yourself represented, representation feels like neutrality. When systems default to your identity, you don't notice they're making a choice at all.

Research on algorithmic fairness emphasises that involving not only data scientists and developers but also ethicists, sociologists, and representatives of affected groups is essential. But users are part of that equation. The choices we make, the resistance we offer, the friction we reject all shape what gets built and abandoned.

There's another layer worth examining: learnt helplessness. We've been told for so long that algorithms are neutral, that AI just reflects data, that these tools are objective. So when faced with a tool that makes those decisions visible, that forces us to participate in representation rather than accept it passively, we don't know what to do with that responsibility.

“I don't know how to answer these questions,” a user might say. “What if I get it wrong?” That discomfort, that uncertainty, that fear of getting representation wrong is actually closer to ethical engagement than the false confidence of defaults.

The U.S. Equal Employment Opportunity Commission's AI initiative acknowledges that fairness isn't something you can automate. It requires ongoing engagement, user input, and willingness to sit with discomfort.

Yes, users would resist. Yes, some would rage-quit. Yes, adoption rates might initially suffer. But the question isn't whether users would like it. The question is whether we're willing to build technology that asks more of us than passive acceptance of someone else's biases.

The Training Data Trap

The standard response to AI bias: we need better training data. More diverse data. More representative data. Fix the input, fix the output. Problem solved.

Except it's not that simple.

Yes, bias happens when training data isn't diverse enough. But the problem isn't just volume or variety. It's about what counts as data in the first place.

More data is gathered in Europe than in Africa, even though Africa has a larger population. Result? Algorithms that perform better for European faces than African faces. Free image databases for training AI to diagnose skin cancer contain very few images of darker skin. Researchers call this “Health Data Poverty,” where groups underrepresented in health datasets are less able to benefit from data-driven innovations.

You can't fix systematic exclusion with incremental inclusion. You can't balance a dataset built on imbalanced power structures and expect equity to emerge. The training data isn't just biased. It's a reflection of a biased world, captured through biased collection methods, labelled by biased people, and deployed in systems that amplify those biases.

Researchers at the University of Southern California have used quality-diversity algorithms to create diverse synthetic datasets that strategically “plug the gaps” in real-world training data. But synthetic data can only address representation gaps, not the deeper question of whose representation matters and how it gets defined.

Data augmentation techniques like rotation, scaling, flipping, and colour adjustments can create additional diverse examples. But if your original dataset assumes a “normal” body is able-bodied, augmentation just gives you more variations on that assumption.

The World Health Organisation's guidance on large multi-modal models recommends mandatory post-release auditing by independent third parties, with outcomes disaggregated by user type including age, race, or disability. This acknowledges that evaluating fairness isn't one-time data collection. It's ongoing measurement, accountability, and adjustment.

But here's what training data alone can't fix: the absence of intentionality. You can have the most diverse dataset in the world, but if your model defaults to the most statistically common representation for ambiguous prompts, you're back to the same problem. Frequency isn't fairness. Statistical likelihood isn't ethical representation.

This is why mandatory identity specification isn't about fixing training data. It's about refusing to let statistical patterns become normative defaults. Recognising that “most common” and “most important” aren't the same thing.

The Partnership on AI's guidelines emphasise that organisations should focus on the needs and risks of groups most at risk of harm throughout the demographic data lifecycle. This isn't something you can automate. It requires human judgement, community input, and willingness to prioritise equity over efficiency.

Training data is important. Diversity matters. But data alone won't save us from the fundamental design choice we keep avoiding: who gets to be the default?

The Cost of Convenience

Let's be specific about who pays the price when we prioritise convenience over representation.

People with disabilities are routinely erased from AI-generated imagery unless explicitly specified. Even then, representation often falls into stereotypes: wheelchair users depicted in ways that centre the wheelchair rather than the person, prosthetics shown as inspirational rather than functional, neurodiversity rendered invisible because it lacks visual markers that satisfy algorithmic pattern recognition.

Cultural representation defaults to Western norms. When Stable Diffusion generates “a home,” it shows suburban North American architecture. “A meal” becomes Western food. For billions whose homes, meals, and traditions don't match these patterns, every default is a reminder the system considers their existence supplementary.

Gender representation extends beyond the binary in reality, but AI systems struggle with this. Non-binary, genderfluid, and trans identities are invisible in defaults or require specific prompting others don't need. The same UNESCO study that found women associated with home and family four times more often than men didn't even measure non-binary representation, because the training data and output categories didn't account for it.

Age discrimination appears through consistent skewing towards younger representations in positive contexts. “Successful entrepreneur” generates someone in their thirties. “Wise elder” generates seventies. The idea that older adults are entrepreneurs or younger people are wise doesn't compute in default outputs.

Body diversity is perhaps the most visually obvious absence. AI-generated humans are overwhelmingly thin, able-bodied, and conventionally attractive by narrow, Western-influenced standards. When asked to depict “an attractive person,” tools generate images that reinforce harmful beauty standards rather than reflect actual human diversity.

Socioeconomic representation maps onto racial lines in disturbing ways. Wealth and professionalism depicted as white. Poverty and social services depicted as dark-skinned. These patterns don't just reflect existing inequality. They reinforce it, creating a visual language that associates race with class in ways that become harder to challenge when automated.

The cost isn't just representational. It's material. When AI resume-screening tools favour white male names, that affects who gets job interviews. When medical AI is trained on datasets without diverse skin tones, that affects diagnostic accuracy. When facial recognition performs poorly on darker skin, that affects who gets falsely identified, arrested, or denied access.

Research shows algorithmic bias has real-world consequences across employment, healthcare, criminal justice, and financial services. These aren't abstract fairness questions. They're about who gets opportunities, care, surveillance, and exclusion.

Every time we choose convenience over mandatory specification, we're choosing to let those exclusions continue. We're saying the friction of thinking about identity is worse than the harm of invisible defaults. We're prioritising the comfort of users who match existing patterns over the dignity of those who don't.

Inclusive technology development requires respecting human diversity at stages of data collection, fairness decisions, and outcome explanations. But respect requires visibility. You can't include people you've made structurally invisible.

This is the cost of convenience: entire populations treated as edge cases, their existence acknowledged only when explicitly requested, their representation always contingent on someone remembering to ask for it.

The Ethics of Forcing Choice

We've established the problem, explored the resistance, counted the cost. But there's a harder question: is mandatory identity specification actually ethical?

Because forcing users to categorise people has its own history of harm. Census categories used for surveillance and discrimination. Demographic checkboxes reducing complex identities to administrative convenience. Identity specification weaponised against the very populations it claims to count.

There's real risk that mandatory specification could become another form of control rather than liberation. Imagine a system requiring you to choose from predetermined categories that don't reflect how you actually understand identity. Being forced to pick labels that don't fit, to quantify aspects of identity that resist quantification.

The Partnership on AI's guidelines acknowledge this tension. They emphasise that consent processes must be clear, approachable, accessible, particularly for those most at risk of harm. This suggests mandatory specification only works if the specification itself is co-designed with the communities being represented.

There's also the question of privacy. Requiring identity specification means collecting information that could be used for targeting, discrimination, or surveillance. In contexts where being identified as part of a marginalised group carries risk, mandatory disclosure could cause harm rather than prevent it.

But these concerns point to implementation challenges, not inherent failures. The fundamental question remains: should AI generate human representations at all without explicit user input about who those humans are?

One alternative: refusing to generate without specification. Instead of defaults and instead of forcing choice, the tool simply doesn't produce output for ambiguous prompts. “Show me a CEO” returns: “Please specify which CEO you want to see, or provide characteristics that matter to your use case.”

This puts cognitive labour back on the user without forcing them through predetermined categories. It makes the absence of defaults explicit rather than invisible. It says: we won't assume, and we won't let you unknowingly accept our assumptions either.

Another approach is transparent randomisation. Instead of defaulting to the most statistically common representation, the AI randomly generates across documented dimensions of diversity. Every request for “a doctor” produces genuinely unpredictable representation. Over time, users would see the full range of who doctors actually are, rather than a single algorithmic assumption repeated infinitely.

The ethical frameworks emerging from UNESCO, the European Union, and the WHO emphasise transparency, accountability, inclusivity, and long-term societal impact. They stress that inclusivity must guide model development, actively engaging underrepresented communities to ensure equitable access to decision-making power.

The ethics of mandatory specification depend on who's doing the specifying and who's designing the specification process. A mandatory identity form designed by a homogeneous tech team would likely replicate existing harms. A co-designed specification process built with meaningful input from diverse communities might actually achieve equitable representation.

The question isn't whether mandatory specification is inherently ethical. The question is whether it can be designed ethically, and whether the alternative, continuing to accept invisible, biased defaults, is more harmful than the imperfect friction of being asked to choose.

What Comes After Default

What would it actually look like to build AI systems that refuse to generate humans without specified identity, culture, ability, and expression?

First, fundamental changes to how we think about user input. Instead of treating specification as friction to minimise, we'd design it as engagement to support. The interface wouldn't be a form. It would be a conversation about representation, guided by principles of dignity and accuracy rather than administrative efficiency.

This means investing in interface design that respects complexity. Drop-down menus don't capture how identity works. Checkboxes can't represent intersectionality. We'd need systems allowing for multiplicity, context-dependence, “it depends” and “all of the above” and “none of these categories fit.”

Research on value-sensitive design offers frameworks for this development. These approaches emphasise involving diverse stakeholders throughout the design process, not as afterthought but as core collaborators. They recognise that people are experts in their own experiences and that technology works better when built with rather than for.

Second, transparency about what specification actually does. Users need to understand how identity choices affect output, what data is collected, how it's used, what safeguards exist against misuse. The EU's AI Act and emerging ethics legislation mandate this transparency, but it needs to go beyond legal compliance to genuine user comprehension.

Third, ongoing iteration and accountability. Getting representation right isn't one-time achievement. It's continuous listening, adjusting, acknowledging when systems cause harm despite good intentions. This means building feedback mechanisms accessible to people historically excluded from tech development, and actually acting on that feedback.

The World Health Organisation's recommendation for mandatory post-release auditing by independent third parties provides a model. Regular evaluation disaggregated by user type, with results made public and used to drive improvement, creates accountability most current AI systems lack.

Fourth, accepting that some use cases shouldn't exist. If your business model depends on generating thousands of images quickly without thinking about representation, maybe that's not a business model we should enable. If your workflow requires producing human representations at scale without considering who those humans are, maybe that workflow is the problem.

This is where the developer question comes back with force: would you ship it? Because shipping a system that refuses to generate without specification means potentially losing market share to competitors who don't care. It means explaining to investors why you're adding friction when the market rewards removing it. Standing firm on ethics when pragmatism says compromise.

Some companies won't do it. Some markets will reward the race to the bottom. But that doesn't mean developers, designers, and users who care about equitable technology are powerless. It means building different systems, supporting different tools, creating demand for technology that reflects different values.

Fifth, acknowledging that AI-generated human representation might need constraints we haven't seriously considered. Should AI generate human faces at all, given deepfakes and identity theft risks? Should certain kinds of representation require human oversight rather than algorithmic automation?

These questions make technologists uncomfortable because they suggest limits on capability. But capability without accountability is just power. We've seen enough of what happens when power gets automated without asking who it serves.

The Choice We're Actually Making

Every time AI generates a default human, we're making a choice about whose existence is normal and whose requires explanation.

Every white CEO. Every thin model. Every able-bodied athlete. Every heterosexual family. Every young professional. Every Western context. These aren't neutral outputs. They're choices embedded in training data, encoded in algorithms, reinforced by our acceptance.

The developers who won't ship mandatory identity specification are choosing defaults over dignity. The designers who prioritise frictionless over fairness are choosing convenience over complexity. The users who rage-quit rather than specify identity are choosing comfort over consciousness.

And the rest of us, using these tools without questioning what they generate, we're choosing too. Choosing to accept that “a person” means a white person unless otherwise specified. That “a professional” means a man. That “attractive” means thin and young and able-bodied. That “normal” means matching a statistical pattern rather than reflecting human reality.

These choices have consequences. They shape what we consider possible, who we imagine in positions of power, which bodies we see as belonging in which spaces. They influence hiring decisions and casting choices and whose stories get told and whose get erased. They affect children growing up wondering why AI never generates people who look like them unless someone specifically asks for it.

Mandatory identity specification isn't a perfect solution. It carries risks. But it does something crucial: it makes the choice visible. It refuses to hide behind algorithmic neutrality. It says representation matters enough to slow down for, to think about, to get right.

The question posed at the start was whether developers would ship it, designers would build it, users would accept it. But underneath that question is more fundamental: are we willing to acknowledge that AI is already forcing us to make choices about identity, culture, ability, and expression? We just let the algorithm make those choices for us, then pretend they're not choices at all.

What if we stopped pretending?

What if we acknowledged there's no such thing as a default human, only humans in all our specific, particular, irreducible diversity? What if we built technology that reflected that truth instead of erasing it?

This isn't about making AI harder to use. It's about making AI honest about what it's doing. About refusing to optimise away the complexity of human existence in the name of user experience. About recognising that the real friction isn't being asked to specify identity. The real friction is living in a world where AI assumes you don't exist unless someone remembers to ask for you.

The technology we build reflects the world we think is possible. Right now, we're building technology that says defaults are inevitable, bias is baked in, equity is nice-to-have rather than foundational.

We could build differently. We could refuse to ship tools that generate humans without asking which humans. We could design interfaces that treat specification as respect rather than friction. We could use AI in ways that acknowledge rather than erase our responsibility for representation.

The question isn't whether AI should force us to specify identity, culture, ability, and expression. The question is why we're so resistant to admitting that AI is already making those specifications for us, badly, and we've been accepting it because it's convenient.

Convenience isn't ethics. Speed isn't justice. Frictionless isn't fair.

Maybe it's time we built technology that asks more of us. Maybe it's time we asked more of ourselves.

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Sources and References

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Currie, G., Currie, J., Anderson, S., & Hewis, J. (2024). “Gender bias in generative artificial intelligence text-to-image depiction of medical students.” https://journals.sagepub.com/doi/10.1177/00178969241274621

European Commission. (2024). “Ethics guidelines for trustworthy AI.” https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai

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MDPI. (2024). “Gender Bias in Text-to-Image Generative Artificial Intelligence When Representing Cardiologists.” Information, 15(10), 594. https://www.mdpi.com/2078-2489/15/10/594

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Taylor & Francis Online. (2024). “White Default: Examining Racialized Biases Behind AI-Generated Images.” https://www.tandfonline.com/doi/full/10.1080/00043125.2024.2330340

UNESCO. (2024). “Ethics of Artificial Intelligence.” https://www.unesco.org/en/artificial-intelligence/recommendation-ethics

University of Southern California Viterbi School of Engineering. (2024). “Diversifying Data to Beat Bias.” https://viterbischool.usc.edu/news/2024/02/diversifying-data-to-beat-bias/

Washington Post. (2023). “AI generated images are biased, showing the world through stereotypes.” https://www.washingtonpost.com/technology/interactive/2023/ai-generated-images-bias-racism-sexism-stereotypes/

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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