Accountability
An essential feature of decision-making in humans, AI, and also HLI-based agents is accountability. Implementing this feature in machines is a difficult task because many challenges should be considered to organize an AI-based model that is accountable. It should be noted that this issue in human decision-making is not ideal, and many factors such as bias, diversity, fairness, paradox, and ambiguity may affect it. In addition, the human decision-making process is based on personal flexibility, context-sensitive paradigms, empathy, and complex moral judgments. Therefore, all of these challenges are inherent to designing algorithms for AI and also HLI models that consider accountability.
ENTITY
2 - AI
INTENT
2 - Unintentional
TIMING
3 - Other
Risk ID
mit602
Domain lineage
7. AI System Safety, Failures, & Limitations
7.4 > Lack of transparency or interpretability
Mitigation strategy
1. Implement a robust, cross-functional governance framework that formally designates human accountability for AI system outcomes across the entire lifecycle, defining clear roles, responsibilities, and oversight mandates for design, deployment, and redress. 2. Prioritize the development and use of Explainable AI (XAI) methods to ensure model decision-making processes are transparent and interpretable to both technical and non-technical stakeholders, thereby bridging the accountability gap created by 'black box' algorithms. 3. Establish continuous auditing and monitoring mechanisms, including MLOps and regular risk assessments, to identify, log, and provide a verifiable audit trail for systemic bias, performance anomalies, and deviations from prescribed ethical and legal compliance standards.