Everyone thinks they know what fat means. Lazy. Lacking willpower. A risk. An inconvenience. But the truth is heavier, sharper, more intimate when you realize it isn’t just whispered in hallways or smirked across conference tables—it is embedded in the code that now decides your worth, your hireability, your future.
I applied for a role I was deeply qualified for, one that matched my experience, my values, my ambition. I tailored my cover letter, prepared for the interview, and felt a flicker of hope when I logged off. Weeks passed with silence, no rejection email, just an absence that grew heavy. Then a friend inside the company quietly told me the AI screening tool had flagged my video interview for 'low energy' and 'inactivity,' that the system had decided I was lazy before anyone knew my name, before anyone read the results I had delivered in past roles, before anyone saw me as a whole human being.
Similarly, Marley was praised for being the go-to fixer on her team, the one who stepped up when projects fell behind, the one who stayed late to ensure deadlines were met. Her boss once called her irreplaceable, a backbone of the department. But when AI cameras were installed under the banner of 'efficiency,' everything changed. The short bathroom breaks she took to manage chronic pain, the moments she stretched her back to keep working, were flagged as 'inactivity alerts.' The system didn’t see the late nights, the leadership, the quiet mentorship she offered to new team members. It saw a blip in movement and decided she was failing. Her performance review tanked, and with it, her trust in a system she had once believed valued her contributions.
In another instance, Kevin was a top performer in his department, a Black man in a predominantly white team who had built trust with clients and coached junior staff, positioning himself for a promotion he had worked years to earn. But after a mandatory wellness weigh-in—a policy framed as caring about 'employee health'—he watched his candidacy evaporate without explanation. Conversations with leadership that once included him turned cold, shifting toward vague talks about 'health culture' and 'fit.' The opportunity he had fought to secure became a ghost, leaving Kevin questioning whether the system had ever truly seen his value beyond the number on a scale.
Taken together, these are not glitches. They are the logical outcome of algorithms trained on data reflecting the world’s fatphobia, racism, and ableism—repackaged as 'efficiency' and 'fairness.' According to a 2025 study in The International Journal of Human Resource Management, appearance-based bias in hiring remains widespread, and AI systems often replicate these patterns when trained on biased data. Hiring systems measure eye movements, posture, tone, and 'culture fit,' using proxies that correlate with thinness, whiteness, and neurotypical patterns, punishing those who don’t conform while appearing neutral.
Moreover, data from Pub Med shows how fat people are overrepresented in low-wage, precarious work and underrepresented in leadership, not because we lack skill, but because systems built on bias keep us there. Workplace wellness programs, part of a $50 billion global industry, often become surveillance disguised as care, and weight is used as a proxy for ambition, discipline, and worth, reinforcing these inequities with a data-driven veneer.
These systems work by collecting and analyzing data that seems neutral: step counts, weigh-ins, facial analysis, and speech patterns. But behind the numbers are design choices, historical data sets, and hidden assumptions about what a 'good' worker looks like—choices that penalize those who do not fit narrow ideals of thinness, whiteness, and able-bodiedness. When wellness apps sync with HR systems, when video interview tools analyze facial expressions, when productivity trackers measure stillness as slacking, they build a case against fat workers without ever naming weight as the reason. This is how discrimination hides in plain sight, shaping careers while pretending not to exist.
Yet people are fighting back. Marley filed a grievance, refusing to let the flags against her breaks go uncontested. Kevin found legal support and began documenting each conversation and shift in tone. Across industries, workers are organizing, forming support groups and legal networks to demand that weight discrimination be recognized as the civil rights issue it truly is, and to ensure it is named explicitly in workplace policies and collective agreements. Tech ethicists are publishing papers and testifying before policymakers to expose the lie of algorithmic neutrality, showing exactly how 'objective' tools amplify harm and replicate bias. Advocates are filing lawsuits and lobbying legislators to push for weight to be a protected category in employment discrimination law. This movement is growing, demanding not only individual accountability but structural change to dismantle the bias coded into these systems.
Ultimately, this isn’t just about AI. It’s about who gets to work, who gets to lead, who gets to thrive—and who is erased by the quiet calculations of systems pretending to be fair.
If the algorithm says I’m lazy, I will ask: who programmed it to see me that way, and who benefits when it does?
