The practice of designing, developing, and deploying AI systems in a manner that aligns with ethical principles and values, ensuring fairness, accountability, and transparency.
A holistic approach that embeds ethical considerations - such as bias mitigation, user consent, privacy safeguards, and impact monitoring - into every phase of the AI lifecycle. Ethical AI requires multidisciplinary collaboration (philosophers, domain experts, technologists), clear governance processes (ethics boards, impact assessments), and measurable ethics KPIs (fairness metrics, transparency scores).
A healthcare startup implements Ethical AI by convening an ethics committee to oversee its symptom-checker bot. They require bias tests across age groups, user-consent dialogues before collecting symptoms, and post-deployment surveys to ensure recommendations respect patient autonomy and do not inadvertently favor certain demographics.
