Notes:

• Algorithmic Bias: AI systems can perpetuate or amplify existing biases in data, leading to discriminatory outcomes in hiring, lending, and law enforcement.
  • Governance: Boards must ensure AI models are tested for bias and that diverse data sets are used.
• Transparency and Explainability: “Black Box” algorithms make it difficult to understand how decisions are made.
  • Governance: Companies must strive for “Explainable AI” (XAI) and be transparent with users about when and how AI is used.
• Data Privacy: AI requires vast amounts of data, raising concerns about privacy and consent.
  • Governance: Ensuring compliance with data privacy laws (GDPR, CCPA) and implementing strong data governance practices.
• Accountability: Who is responsible when an AI system makes a mistake or causes harm?
  • Governance: Establishing clear lines of accountability for AI decisions and ensuring human oversight of critical AI systems.
• California TFAIA (2026): As noted in Module 6, this law requires specific governance frameworks for “frontier AI” developers, including risk assessments, incident reporting, and whistleblower protections.