Audience-Dependent Explanations for AI-Based Risk Management Tools: A Survey - Hadji Misheva, Branka; Jaggi, David; Posth, Jan-Alexander; Gramespacher, Thomas; Osterrieder, Joerg (Frontiers in Artificial Intelligence, 2021)
Artificial Intelligence (AI) is one of the most sought-after innovations in the financial industry. However, with its growing popularity, there also is the call for AI-based models to be understandable and transparent. However, understandably explaining the inner mechanism of the algorithms and their interpretation is entirely audience-dependent. The established literature fails to match the increasing number of explainable AI (XAI) methods with the different stakeholders’ explainability needs. This study addresses this gap by exploring how various stakeholders within the Swiss financial industry view explainability in their respective contexts. Based on a series of interviews with practitioners within the financial industry, we provide an in-depth review and discussion of their view on the potential and limitation of current XAI techniques needed to address the different requirements for explanations.