From ethical risks that could damage brands to regulatory uncertainty, AI poses challenges for investors, but there is a way forward.
NORTHAMPTON, MASSACHUSETTS / ACCESSWIRE / June 18, 2024 / Alliance Bernstein
Authors: Saskia Court-Chick | Director of Social Research & Engagement, Jonathan Bercow | Director of Data Science, Equity
Artificial Intelligence (AI) raises many ethical issues that could pose risks for consumers, businesses and investors. And the uneven development of AI regulation across multiple jurisdictions adds further uncertainty. We believe a key focus for investors is a focus on transparency and explainability.
AI ethical issues and risks start with the developers who create the technology, then ripple through to their customers – the companies that are incorporating AI into their businesses – and then to consumers and society more broadly. Investors are exposed on both ends of the risk chain through their investments in AI developers and companies that use AI.
AI is developing rapidly and is much further ahead than most people realize. Trying to keep up are regulators and legislators around the world. At first glance, their activity in the AI field has expanded rapidly in the last few years, with many countries publishing related strategies and others getting closer to implementation (screen).
The reality is that progress is uneven and far from complete. There is no uniform approach to AI regulation across jurisdictions, and some countries have introduced regulations before ChatGPT launches in late 2022. As AI becomes more widespread, many regulators will need to update, and potentially expand, the work they have already done.
For investors, regulatory uncertainty amplifies other risks of AI. To understand and evaluate how to address these risks, it is helpful to have an overview of the AI business, ethics, and regulatory landscape.
Data risks can damage your brand
AI includes a range of technologies to perform tasks typically performed by humans in a human-like manner. AI and business can intersect through generative AI, which includes content generation in various forms such as video, audio, text, and music, and large language models (LLMs), a subset of generative AI that focuses on natural language processing. LLMs serve as the foundational models for a range of AI applications that businesses are increasingly using in customer engagement, including chatbots, automated content creation, and analyzing and summarizing large amounts of information.
But as many companies are finding out, AI innovation can come with risks that can damage a brand. These risks can arise from biases inherent in the data used to train Masters of Law (LLM) students, leading, for example, to banks unintentionally discriminating against minorities in mortgage approvals, and U.S. health insurers facing class action lawsuits alleging that their use of AI algorithms resulted in unfair denials of extended care claims for elderly patients.
Bias and discrimination are just two of the risks that regulators will target and investors should be aware of. Other risks include intellectual property rights and privacy considerations regarding data. Risk mitigation measures should also be scrutinized, such as developers testing AI models for performance, accuracy, and robustness, and providing transparency and support to companies in implementing AI solutions.
Deeper understanding of AI regulation
The AI regulatory environment is evolving in different ways and at different speeds across jurisdictions, with the most recent developments including the European Union's (EU) Artificial Intelligence Act, due to come into force in mid-2024, and the UK government's response to the consultation process triggered by the publication of the government's AI Regulation White Paper last year.
Both efforts show how the approaches to regulating AI are different: the UK has adopted a principles-based framework that existing regulators can apply to AI issues within their territories. In contrast, EU law introduces a comprehensive legal framework with risk-graded compliance obligations for developers, companies, importers and distributors of AI systems.
In our view, investors should not only dig into the details of each jurisdiction's AI regulations, but also understand how jurisdictions govern AI issues using laws that predate and fall outside the scope of AI-specific regulation, such as copyright laws to address data breaches or employment laws when AI impacts the labor market.
Fundamental analysis and engagement are key
For investors looking to assess AI risks, a useful rule of thumb is that companies that proactively and fully disclose their AI strategies and policies are more likely to be well prepared for new regulations.More generally, fundamental analysis and issuer engagement, which are fundamental to responsible investing, are crucial to this field of research.
The fundamental analysis not only examines AI risk factors at the enterprise level, but also delves into AI risk factors across the entire business chain and regulatory environment, and incorporates the core principles of responsible AI (screen).
Engagement conversations can be structured to cover AI issues from environmental, social and governance perspectives, as well as their impact on business operations. Questions investors should ask boards and management include:
- 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 ExpertiseHow does the board ensure there is sufficient expertise to effectively oversee the company's AI strategy and implementation? Are there specific training programs or initiatives?
- 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 regulations?
- Proactive Transparency: Has the company implemented proactive transparency measures to withstand the impact of future regulations?
- Risk Management and Accountability: What risk management processes does the company have in place to identify and mitigate AI-related risks? Has responsibility for monitoring these risks been delegated?
- Data Challenges in LLM: How does the company address privacy and copyright issues related to the input data used to train large-scale language models? What measures are taken to ensure that the input data complies with privacy regulations and copyright laws, and how does the company address any restrictions or requirements related to the input data?
- Bias and Fairness Challenges in Generative AI Systems: What steps has the company taken to prevent and/or mitigate biased or unfair outcomes arising from its AI systems? How does the company ensure that the output of any generative AI systems it uses is fair, unbiased, and does not perpetuate discrimination or harm against individuals or groups?
- Incident Tracking and Reporting: How do companies track and report incidents related to their development or use of AI, and what mechanisms exist for addressing and learning from these incidents?
- Metrics and Reports: 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 regulatory compliance of its AI applications?
Ultimately, the best way for investors to find their way through the maze is to keep their feet on the ground and maintain a skeptical attitude. AI is a complex and rapidly evolving technology. Investors should look for clear answers and avoid being overly impressed by fancy, complicated explanations.
The authors would like to acknowledge the contributions of Roxanne Low, ESG Analyst in AB’s Responsible Investment team, to the research.
The views expressed herein do not constitute research, investment advice or trading recommendations and do not necessarily represent the views of the AB portfolio management team as a whole. Views are subject to change over time.
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