AI in Capital Markets: Top 5 Ethical Risks

How can we ensure artificial intelligence is bringing real value to clients?


Published | 2 min read

AI in Capital Markets: Top 5 Ethical Risks As artifcial intelligence (AI) becomes more embedded in fnancial decision-making, how can we ensure it’s bringing real value to clients? These 5 key concerns must be addressed: Explainability - AI is often a black box – opaque and complex 64% of IT professionals to explain and understand. say that being able to explain how their AI arrives - But explainability is critical for trust, end-users at different decisions is must feel confdent engaging with predictions. important to their business.1 2 Bias - Data, and people, can be unintentionally biased and that can adversely infuence AI models. Between 3.4% and - There are documented cases where racial bias has 38.6% of data used been detected in AI systems3 , for example. to inform certain AI systems is biased.2 - Discriminatory bias can signifcantly erode trust. Competitive market dynamics - Automation and using AI to generate deeper trading insights is opening up new competitive dynamics in markets. - Greater competition could lead to issues stemming from collusion, market dominance, or mergers.5 Since 2017 at least 60 countries worldwide have enacted AIrelated guidelines.4 4 Robustness - Robustness, or reliability, is essential for AI used in capital markets. - Robustness refers to the AI model’s sensitivity Only 50% of people trust companies that use to any inputs that might result in incorrect AI as much as they trust other companies.6 predictions or results. 1 3 5 Data security and privacy - AI is heavily reliant on large quantities of data, and without proper controls, data can be corrupted. Almost 20% of IT experts - Addressing privacy concerns while leveraging believe data security is large datasets is also a challenge – risks include a major concern when it the exposure of personal identifable information. comes to AI systems.7 Building the future RBC Capital Markets is committed to building better AI. Discover more about our approach to responsible and explainable algorithms in our article: Responsible and Explainable AI: Exploring the Future of Trading. 1 https://www.ibm.com/downloads/cas/GVAGA3JP 2 https://viterbischool.usc.edu/news/2022/05/thats-just-common-sense-usc-researchers-fnd-bias-in-up-to-38-6-of-facts-used-by-ai/ 3 https://www.genpact.com/insight/responsible-ai-developing-a-framework-for-sustainable-innovation 4 https://www.brookings.edu/blog/techtank/2022/02/01/the-eu-and-u-s-are-starting-to-align-on-ai-regulation/ 5 https://www.oecd-ilibrary.org/sites/3acbe1cd-en/index.html?itemId=/content/component/3acbe1cd-en#section-d1e9039 6 https://www.ipsos.com/en/global-opinions-about-ai-january-2022 7 https://www.ibm.com/downloads/cas/GVAGA3JP

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