UK FCA research note on AI's role in credit decisions

The UK Financial Conduct Authority (FCA) has published a research note on AI's role in credit decisions, exploring the issue of AI explainability in the context of algorithm-assisted decision-making, using consumer credit decisions as a case study to test out different approaches. The researchers used an online experiment to study whether different kinds, or 'genres', of explanation lead to better consumer outcomes such as consumers' ability to judge whether algorithm-assisted decisions are erroneous. Specifically, the researchers tested whether participants were able to identify errors caused either by incorrect data used by the algorithm or by flaws in the algorithm's decision logic itself.

The experiment found that additional information may make it more difficult to spot errors because there is simply more information to review, encouraging participants to focus on whether this decision logic was followed rather than if the decision logic was sound. However, participants who were given more information about the inner workings of the algorithm's decision-making reported feeling more confident in their ability to judge the algorithm's decisions—but their actual judgement was worse on average. The findings reiterate the value of testing accompanying materials that may be provided to consumers when explaining AI, machine learning and/or algorithmic decision-making to understand how effective they are.

The findings also underscore the importance of testing consumers' decision-making within the relevant context, rather than relying solely on self-reported attitudes. The researchers consider that further research could look to explore how best to explain AI assisted decisions in other contexts within financial services, the specific mechanisms for how explainability methods may impact consumers, alternative ways of presenting explanation genres, and the broader consumer journey beyond recognising errors.

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