AI Ethics

AI ethics is the structured examination of the moral dimensions of artificial intelligence. As AI systems make or influence decisions that affect people's lives - who gets a loan, who gets a job interview, which content they see, how their health is assessed - profound ethical questions arise about who is responsible for these decisions, whether they are fair, and what values they encode.

Key areas of AI ethics include: algorithmic fairness, examining whether AI systems treat different groups equitably; privacy, examining how personal data is collected, used, and protected in AI systems; autonomy, examining how AI affects human agency and decision-making; transparency, examining who has the right to know how AI decisions are made; and labor ethics, examining how AI affects employment and economic equity.

AI ethics is also concerned with who gets to set the values that AI systems embody. AI systems trained on data from predominantly Western, English-speaking internet may encode cultural assumptions that are inappropriate or harmful in other contexts. The people who build AI systems are not demographically representative of the global populations that use them. This 'ethics gap' is a persistent concern in the field.

Practical AI ethics involves identifying ethical risks before deployment, not just responding to harms after the fact. Ethics review boards, red-teaming exercises (where teams try to find harmful failure modes), and pre-deployment impact assessments are tools organizations use to proactively identify and address ethical concerns. The emerging field of AI ethics as practice focuses on embedding ethical reasoning into everyday engineering decisions rather than treating ethics as a separate compliance checkbox.

AI ethics intersects with AI regulation as governments translate ethical principles into legal requirements. The EU AI Act, for example, requires high-risk AI systems to meet specific requirements around transparency, human oversight, and fairness - translating ethical principles into enforceable law. For organizations building AI, this means that responsible AI practices are increasingly not optional.