AI Ethics
The field asking not whether AI can do something but whether it should — and who bears the consequences when it does.
Reviewed July 16, 2026Stable
When not to use it
- (Not applicable — AI ethics is a lens applied throughout, not an optional module. The failure mode is treating it as a box to tick at the end.)
Reach for something else instead
- Regulatory compliance as the enforceable floor — necessary but not sufficient for ethics.
- Value-sensitive design as a methodology for building values in from the start.
Read more on the blog
- AI bias and fairness: why 'fair' has no single answerAI now helps decide who gets a loan, an interview, bail, or medical priority, and the fear is that it does so unfairly. The instinct is to remove the bias and make the model fair. But a mathematical result makes that impossible in a precise way: several reasonable definitions of fairness cannot all hold at once, so fairness is not a bug to fix but a choice among competing values.
- Will AI take my job? What the evidence showsThe frightening headline numbers and the reassuring ones are both real, because they measure different things. AI acts on tasks, not jobs, and a job is a bundle of tasks. That single distinction explains why the studies appear to contradict each other and what the evidence actually supports.
- AI and copyright: the three questions people confuseWhether training on protected work is lawful, whether AI output can be owned or infringes, and whether any of it is fair to creators are three separate questions with different rules and different answers. Most of the argument consists of people answering different ones at each other.
Sources & further reading
- Kleinberg, Mullainathan & Raghavan (2016), Inherent Trade-Offs in the Fair Determination of Risk Scores — the impossibility result for fairness definitions.
- Mitchell et al. (2019), Model Cards for Model Reporting — a practical accountability tool.
- Jobin, Ienca & Vayena (2019), The global landscape of AI ethics guidelines — a survey of the principles that recur worldwide.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Treating ethics as a final-stage checklist rather than a throughout-the-lifecycle question.
- Assuming "fair" is well-defined — several fairness metrics are provably incompatible.
- Mistaking algorithmic objectivity for neutrality — the choices in data and design carry values.
At a glance
FieldSafety & Ethics
Core questionnot can but should
Pillarsfairness, transparency, accountability, privacy, safety
Hard truthvalues genuinely conflict
DifficultyIntermediate