Overview: AI Risk Assessment

AI Risk Assessment

AI systems are increasingly used across various sectors, where their failure ranges from minor inconveniences to significant societal impacts in areas like credit scoring, recruitment, medical diagnostics, and power distribution. The complexity of AI and its socio-technical nature, intertwined with societal and human behaviors, poses unique challenges in detecting failures and managing risks. The dynamic between technical, organizational, and societal factors requires robust AI governance and risk management to prevent and mitigate negative outcomes.

Effective AI risk management necessitates a consistent methodology for assessing AI system risks, focusing on legal, regulatory, industry, and academic perspectives. This involves understanding risk management practices, trustworthiness requirements, stakeholder roles in mitigating risks, and navigating legal and industry standards. The goal of this module is to equip you with the ability to conceptualize AI risks, differentiate risk management approaches, and grasp the complexities of AI Risk Assessments within the broader regulatory and standards landscape.