Ethics Principle Proliferation

AI ethics guidelines multiply faster than the field’s ability to operationalize them.

Meta-reviews have identified 11+ distinct “fundamental” ethical principles for AI, including transparency, fairness, accountability, beneficence, non-maleficence, autonomy, justice, equity, responsibility, privacy, trust, dignity, sustainability, and solidarity. Each framework adds its own variations. The result: a field that generates conceptual vocabulary more quickly than it generates implementation pathways.

This proliferation isn’t neutral. When everything is an ethical principle, nothing is prioritized. Teams face choice paralysis or selective adherence, picking the principles that align with existing incentives while neglecting the harder ones.

Related: 07-molecule—principles-to-practice-translation-problem, 05-atom—voluntary-compliance-gap