Rethinking AI Ethics: From Failing Frameworks to Reflexive Practice
How Ethical AI Literacy (EAIL)’s emphasis on competence for ethical practice can result in better ethical outcomes and strengthen professional integrity when goal-driven AI ethics miss the mark.
AI ethics initiatives often misses it’s mark; complexity, opacity and undue awe can often disrupt shared sense-making. It could be claimed that efforts have over-emphasized technical mitigations at the expense of exploring how people actually deliberate, negotiate, and act. EAIL reframes necessary competence as situated and reflexive: it integrates domain expertise with procedural, metacognitive, and collaborative practices so that practitioners can scrutinize trade-offs, surface power dynamics, and reason under uncertainty. By legitimizing practitioners’ know-how and reflexive capacity, EAIL can reduce the aura of the “untouchable” technology and strengthen self-reliance in relation to AI ethical issues. The framework provides parameters for measurement of methods related to the practice and pedagogics of AI ethics by introducing ethical, technical, and contextual dimensions across factual, conceptual, procedural, and metacognitive knowledge aiming to strengthen ethical outcomes and professional identity in an era of technological flourishment.