AI Ethics: Definition and Examples

AI ethics is an interdisciplinary field that examines the moral, social, and legal implications of artificial intelligence. It aims to ensure that AI systems are developed and deployed responsibly, respecting individuals' fundamental rights, social justice, and the common good. This field covers various issues such as algorithmic transparency, privacy protection, accountability in case of error or harm, and the prevention of automated discrimination.

Ethical issues in AI have become central as these technologies have become widespread in sensitive areas: healthcare, justice, recruitment, finance, and surveillance. A biased algorithm in a recruitment process can reproduce and amplify existing discriminations. A poorly calibrated facial recognition system can unfairly target certain populations. AI ethics seeks to anticipate and prevent these abuses by setting safeguards from the design phase.

In prompt engineering, AI ethics translates into a direct responsibility for the user. The way a prompt is formulated influences the quality, fairness, and reliability of generated responses. A poorly designed prompt can induce bias, generate misinformation, or produce harmful content. Integrating ethical reflection into prompts means, for example, explicitly asking for balanced responses, verifying sources, or avoiding soliciting discriminatory or misleading content.

Several reference frameworks now structure this field: the OECD AI Principles, the European AI Act, guidelines from organizations like UNESCO or the Partnership on AI. These frameworks converge around common values — transparency, fairness, accountability, robustness, and privacy — which constitute the pillars of any ethical approach to artificial intelligence.