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The views expressed herein do not constitute research, investment advice or trade recommendations and do not necessarily represent the views of all AB portfolio-management teams. Views are subject to revision over time.
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For illustrative purposes only.
Through December 31, 2022
Source: AI, Algorithmic and Automation Incidents and Controversies (AIAAIC) Repository; and Nestor Maslej, Loredana Fattorini, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Helen Ngo, Juan Carlos Niebles, Vanessa Parli, Yoav Shoham, Russell Wald, Jack Clark and Raymond Perrault, The AI Index 2023 Annual Report, AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2023.
For illustrative purposes only.
Source: AllianceBernstein (AB)
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?
The views expressed herein do not constitute research, investment advice or trade recommendations and do not necessarily represent the views of all AB portfolio-management teams. Views are subject to revision over time.
Jonathan Berkow is a Senior Vice President and the Director of Quantitative Research and Data Science in the Equities division at AB. He leads the research and adoption of alternative data in equity research and systematic strategies. Prior to joining the firm in 2018, Berkow was a systematic portfolio manager and researcher at hedge funds Element Capital Management and Kepos Capital. He started his career at Goldman Sachs Asset Management, where he managed quantitative research and was a portfolio manager for global equity portfolios. Berkow's research has spanned equities and macro asset classes. He holds a BS in economics from the Massachusetts Institute of Technology. Location: New York