AI Ethics
Ethical challenges are widely discussed in the literature and are at the heart of the debate on how to govern and regulate AI technology in the future (Bostrom & Yudkowsky, 2014; IEEE, 2017; Wirtz et al., 2019). Lin et al. (2008, p. 25) formulate the problem as follows: “there is no clear task specification for general moral behavior, nor is there a single answer to the question of whose morality or what morality should be implemented in AI”. Ethical behavior mostly depends on an underlying value system. When AI systems interact in a public environment and influence citizens, they are expected to respect ethical and social norms and to take responsibility of their actions (IEEE, 2017; Lin et al., 2008).
ENTITY
3 - Other
INTENT
3 - Other
TIMING
3 - Other
Risk ID
mit325
Domain lineage
7. AI System Safety, Failures, & Limitations
7.3 > Lack of capability or robustness
Mitigation strategy
1. Establish a formal AI Ethics and Governance Framework that translates abstract moral and societal values (e.g., fairness, accountability, non-maleficence) into measurable, actionable technical requirements and organizational policies, thereby providing a clear, pre-defined ethical specification for AI system design and deployment. 2. Implement robust Explainable AI (XAI) and transparency mechanisms to convert opaque decision-making processes into auditable and interpretable outputs, enabling human users and oversight bodies to assess the AI's alignment with ethical and social norms. 3. Mandate continuous Human-in-the-Loop (HITL) oversight for all high-stakes decisions, clearly designating human accountability for the AI's actions and ensuring that final authority rests with a human agent capable of exercising judgment over the system's compliance with dynamic ethical and legal requirements.