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2020 BTLJ Comment. 1 (2020)

handle is hein.journals/btljc2020 and id is 1 raw text is: INNOVATING WITH CONFIDENCE: EMBEDDING Al GOVERNANCE AND FAIRNESS IN A FINANCIAL SERVICES RISK MANAGEMENT FRAMEWORK Michelle Seng Ah Lee,t Luciano FloridAi & Alexander Denevf ABSTRACT An increasing number of financial services (FS) companies are adopting solutions driven by artificial intelligence (Al) to gain operational efficiencies, derive strategic insights, and im- prove customer engagement. However, the rate of adoption has been low, in part due to the apprehension around its complexity and self-learning capability, which makes auditability a challenge in a highly regulated industry. There is limited literature on how FS companies can implement the governance and controls specific to AI-driven solutions. Al auditing cannot be performed in a vacuum; the risks are not confined to the algorithm itself, but rather permeates the entire organization. Using the risk of unfairness as an example, this paper will introduce the overarching governance strategy and control framework to address the practical challenges in mitigating risks Al introduces. With regulatory implications and industry use cases, this framework will enable leaders to innovate with confidence. 0 2020 Michelle Seng Ah Lee, Luciano Floridi & Alexander Denev. t University of Oxford, Deloitte UK; michelle.lee@oii.ox.ac.uk. ft University of Oxford, Alan Turing Institute; luciano.floridi@oii.ox.ac.uk. tt University of Oxford, Deloitte UK; adenev@deloitte.co.uk. This research was par- tially supported by Deloitte (MSAL and AD); and by Privacy and Trust Stream - Social lead of the PETRAS Internet of Things research hub - PETRAS is funded by the Engineering and Physical Sciences Research Council (EPSRC), grant agreement no. EP/N023013/1, and Google UK Limited (LF).

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