AI bias isn’t just an error in the algorithm. It’s a chain of human decisions

SDI Productions/Getty In the United States, leading HR software company Workday is currently facing a lawsuit over its use of job screening tools powered by AI which allegedly discriminated against applicants based on factors such as age, disability and race.

The company, whose hiring software is widely used by large employers around the world, has denied the allegations.

The case is one of many examples of AI systems alleged to have caused discriminatory harm .

When AI systems replicate and amplify discrimination, they blur the boundary between technical error and systemic injustice, turning bias into a digital harm.

And while our first instinct might be to blame the algorithms, they don’t decide what they can generate, what safeguards are built into them, or how a company responds when incidents of discrimination are reported.

People make those calls long before an AI produces any output.

That is why technical fixes to AI systems are not enough.

What is needed is an overhaul of AI ecosystems to ensure they are more inclusive.

A broader pattern AI systems quietly narrow who gets seen as competent, employable or fit to lead.

For example, in a 2025 study , we tested how two AI models, OpenAI’s GPT-4 (which has now been retired ) and Microsoft Copilot, represented software engineers in a simulated recruitment exercise: 300 candidate profiles for four job roles, followed by recommendations and generated images of each AI model’s preferred candidates.

Both models favoured male profiles, especially for senior roles.

Their images also skewed towards engineers who were younger, slimmer, and lighter-skinned.

The models were reproducing associations embedded in language, imagery, employment records and assumptions about who belongs in the profession.

These outputs don’t stay contained to a research study.