The illusion of objectivity.
We're told algorithms are pure, objective mathematics. But maths must be fed data to learn — and that data is human history. Ask a machine to imagine success, beauty or criminality and it doesn't calculate the truth. It calculates our past. This class confronts algorithmic bias before you deploy the skills from earlier modules.
Name the bias
Articulate how flawed historical data poisons modern outputs.
Spot a proxy
Define a proxy variable and how it masks discrimination.
Assess impact
Run a basic ethical impact assessment before deploying AI.
True or false?
"Because AI systems are built on mathematical formulas, they are inherently objective and cannot possess racist or homophobic biases." Decide, then confirm.
A mirror, not a mind
Teach a child what a "leader" looks like using only a book of 18th-century European kings, and they'll draw a man in a crown. They're not biased — their dataset is incomplete. AI works identically. We feed the probability engine billions of internet images and documents, but the internet over-represents Western, English-speaking, affluent, historically dominant groups.
Ask an AI to screen software-engineer résumés and it notes most historical tech résumés belong to men. Without context, it predicts that being male is a job requirement — and starts rejecting women.
If an AI uses historical data to predict future success, how can marginalised communities ever break historical cycles of exclusion?
Algorithms don't invent prejudice. They inherit systemic human prejudice from uncurated training data and automate it at massive scale.
The pipeline of amplification
A single flawed premise snowballs into systemic harm. Watch the current grow brighter as bias flows left to right.
1 · Biased history
Human records full of past exclusion.
2 · Uncurated data
Scraped wholesale, biases intact.
3 · The model learns
Statistics absorb and encode it.
4 · Real-world harm
Automated rejection at scale.
Each stage amplifies the last: a small historical skew becomes an automated, industrial-scale injustice — like a résumé screener that quietly rejects whole demographics.
The moderation dilemma
Safety filters are supposed to stop hate speech. But language is contextual — a slur in one mouth is a reclaimed term of power in another. Play the AI engineer trying to build a "perfectly safe" filter. (Spoiler: there's no clean win.)
The danger of the average
Facial recognition is the clearest example of training data enforcing societal norms. Historically, datasets were scraped and labelled into strict binary buckets — "Male" or "Female" — and models learned to predict gender from the geometry of cheekbones, jawlines and eyes.
For cisgender people these systems eventually got accurate. For transgender, non-binary and gender-nonconforming people, they fail catastrophically: the AI forces everyone into one of two rigid statistical buckets. Deployed at airport scanners or banking identity checks, gender-nonconforming people get flagged as "anomalies," "errors" or "fraud risks."
Machine-learning models are built to find the centre of the bell curve. By definition they optimise for the majority and penalise the margins. A technology that can't recognise a marginalised identity should not be a gatekeeper to public life.
The healthcare proxy
A hospital network deployed AI to flag patients needing extra care — hoping to remove human prejudice from triage. The AI couldn't examine patients, so engineers gave it a proxy variable: healthcare spending. The logic: sicker people spend more.
Cost = sickness
The AI found White patients historically spent more on healthcare than Black patients, and concluded White patients must be sicker.
Sicker patients deprioritised
It systematically prioritised healthier White patients over sicker Black patients for advanced care.
The AI never knew race and wasn't "racist." But due to systemic inequality and discrimination, marginalised communities often spend less even when sicker. Healthcare spending is a flawed proxy — and the algorithm blindly amplified a historical injustice because the engineers assumed maths equals objectivity.
Find a real failure
Algorithmic failures aren't theoretical — biased tools get pulled from the market constantly. Find one real example and log it below.
What to look up
"AI recruiting tool biased against women" · "algorithmic bias predictive policing" · "AI facial recognition wrongful arrest".
The impact assessment
A city council wants to buy an AI "predictive policing" tool that uses historical crime data to tell police which neighbourhoods to patrol. Write a 3-point impact assessment on why it's ethically dangerous — covering the flawed data, the proxy variables, and the feedback loop.
Success rubric — tap to expand
Needs work: trusts the tool and argues AI lowers crime objectively.
Getting there: says it might be biased but can't say why the data is flawed.
Solid: identifies the flawed historical data and the risk to marginalised neighbourhoods.
Excellent: articulates the feedback loop — biased data → biased patrols → more arrests in those areas → more biased data → communities trapped in a mathematical loop of over-policing.
Quiz — 5 questions
Common mistakes
The 'maths is objective' fallacy
Blindly trusting an AI to reject an applicant or deny a loan. Fix: treat outputs as statistical suggestions, not truth — keep a human in the loop for critical decisions.
The scale illusion
Assuming a model trained on "the whole internet" must be unbiased. Fix: the internet is dominated by a few demographics — it's not a mirror of global humanity.
Ignoring the margins
Designing for the "average user" with strict binary gender inputs that break for non-binary people. Fix: design for the edge cases first — if it works for the most marginalised user, it works for everyone.
Over-relying on safety filters
Trusting corporate moderation to be culturally competent — and watching it flag queer-history discussion as "inappropriate." Fix: advocate for contextual human review over rigid algorithmic banning.
Curate a fairer dataset
If you had total control over curating the dataset for a truly unbiased AI, what specific human experiences, archives and voices would you ensure are included that current systems actively ignore?
Optional: how do we balance AI safety filters against the danger of erasing reclaimed cultural language? Should algorithms ever decide criminal justice, healthcare triage or employment, given their reliance on biased historical data?
You've confronted the hardest truth in tech: algorithms are human flaws written in code. With this ethical framework secured, you can manipulate the machine safely. Next, we step inside the visual brain of neural networks.