Governance
Generative AI can create new risks as well as unintended consequences. Different entities such as corporations (Mäntymäki et al., 2022), universities, and governments (Taeihagh, 2021) are facing the challenge of creating and deploying AI governance. To ensure that generative AI functions in a way that benefits society, appropriate governance is crucial. However, AI governance is challenging to implement. First, machine learning systems have opaque algorithms and unpredictable outcomes, which can impede human controllability over AI behavior and create difficulties in assigning liability and accountability for AI defects. Second, data fragmentation and the lack of interoperability between systems challenge data governance within and across organizations (Taeihagh, 2021). Third, information asymmetries between technology giants and regulators create challenges to the legislation process, as the government lacks information resources for regulating AI (Taeihagh et al., 2021). For the same reasons, lawmakers are not able to design specific rules and duties for programmers (Kroll, 2015).
ENTITY
1 - Human
INTENT
3 - Other
TIMING
3 - Other
Risk ID
mit548
Domain lineage
6. Socioeconomic and Environmental
6.5 > Governance failure
Mitigation strategy
1. Establish a Formal AI Governance Framework and Accountability Structure: Designate an empowered AI Governance Officer or an AI Risk Committee to centralize oversight, define clear roles and responsibilities across the AI lifecycle, and establish documented processes for assigning explicit liability and accountability for system outputs, errors, and defects. 2. Mandate Explainable AI (XAI) and Algorithmic Impact Assessments (AIAs): Implement XAI techniques to enhance model transparency and demystify 'black box' decision-making. Conduct regular Algorithmic Impact Assessments (AIAs) to evaluate risks, document decision logic, and ensure mechanisms for human intervention and controllability are effective and auditable. 3. Integrate with Compliance and Continuous Monitoring: Align the AI governance framework with established Enterprise Risk Management (ERM) and regulatory compliance programs (e.g., NIST AI RMF, EU AI Act). Institute continuous monitoring, auditing, and vulnerability management processes to ensure ongoing policy adherence, data quality, and resilience against evolving ethical and legal requirements.