Artificial Intelligence (AI) risks are becoming more apparent as many organizations create substantial value with AI and recognize that this technology will shape the future. However, the organizations are also realizing that AI could expose them to a fast-changing landscape of risks that regulators are closely monitoring and may potentially penalize. The idea of protecting against a wide range of AI risks can feel overwhelming, but ignoring AI or pretending the risks do not exist is not a practical choice. So, where should companies begin?

Privacy and Data Protection

Artificial intelligence risks must be systematically identified and prioritized by organizations to effectively target their mitigation efforts. A structured approach helps delineate specific negative events that could arise from AI deployments, enabling companies to outline how to mitigate these risks in line with the appropriate standards.

Security Vulnerabilities in AI

AI models present complex vulnerabilities that create both traditional and new security risks. Issues such as model extraction and data poisoning can compromise the effectiveness and safety of AI systems, necessitating adherence to legal standards for security.

Fairness and Bias in AI Models

AI systems can unintentionally encode bias due to flawed data or model decisions, resulting in fairness risks. Organizations must be aware of the potential harm AI could cause to specific groups or classes. The organizations must also consider the associated liabilities and ensure fairness in AI applications.

Legal and Regulatory Compliance for AI Risks

Organizations must navigate a complex landscape of legal and regulatory standards related to AI risks. These risks include privacy, fairness, and security concerns. Awareness of applicable laws is crucial for compliance when deploying AI models across various industries and regions.

Read the full article, ‘Getting to know—and manage—your biggest AI risks’, on Quantum Black by McKinsey & Company.