AB: AI Ethics and Regulation: How Investors Can Navigate the Maze - Seite 3

Fundamental analysis should delve not only into AI risk factors at the company level but also along the business chain and across the regulatory environment, testing insights against core responsible-AI principles (Display).

Engagement conversations can be structured to cover AI issues not only as they affect business operations, but from environmental, social and governance perspectives, too. Questions for investors to ask boards and management include the following:

• AI integration: How has the company integrated AI into its overall business strategy? What are some specific examples of AI applications within the company?
• Board oversight and expertise: How does the board ensure it has sufficient expertise to effectively oversee the company's AI strategy and implementation? Are there any specific training programs or initiatives in place?
• Public commitment to responsible AI: Has the company published a formal policy or framework on responsible AI? How does this policy align with industry standards, ethical AI considerations, and AI regulation?
• Proactive transparency: Does the company have any proactive transparency measures in place to withstand future regulatory implications?
• Risk management and accountability: What risk management processes does the company have in place to identify and mitigate AI-related risks? Is there delegated responsibility for overseeing these risks?
• Data challenges in LLMs: How does the company address privacy and copyright challenges associated with the input data used to train large language models? What measures are in place to ensure input data is compliant with privacy regulations and copyright laws, and how does the company handle restrictions or requirements related to input data?
• Bias and fairness challenge in generative AI systems: What steps does the company take to prevent and/or mitigate biased or unfair outcomes from its AI systems? How does the company ensure that the output of any generative AI systems used are fair, unbiased, and do not perpetuate discrimination or harm to any individual or group?
• Incident tracking and reporting: How does the company track and report on incidents related to its development or use of AI, and what mechanisms are in place for addressing and learning from these incidents?
• Metrics and Reporting: What metrics does the company use to measure the performance and impact of its AI systems, and how are these metrics reported to external stakeholders? How does the company maintain due diligence in monitoring the regulatory compliance of its AI applications?

Ultimately, the best way for investors to find their way through the maze is to stay grounded and skeptical. AI is a complex and fast-moving technology. Investors should insist on clear answers and not be unduly impressed by elaborate or complicated explanations.