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Audience-dependent explanations

  • Host institution: University of Naples Federico II, Italy (UNA)

  • Starting month: M3

  • Duration: 36 months

  • Pillar 1: The need for eXplainable AI: methods and applications in finance (Bern University of Applied Sciences, 4 ECTs), Work Package 3

  • Work Packages: WP3, WP6, WP7, WP8


To address the issue of explainability in complex models, the literature has proposed an ever-expanding list of post-hoc explainability methods that can be used to gain some understanding of the inner workings of complex models. However, explaining the inner workings of algorithms and their interpretation is entirely dependent on the target audience. The existing literature fails to match the growing number of explainable AI (XAI) methods with the varying explainability requirements of stakeholders. To promote the widespread adoption of AI-based systems in finance, additional research is required to map the requirements of explainable systems across the various stakeholders in the finance industry

Expected Results

Finance decision-makers and AI model builders don't understand XAI's capabilities or ESG's impact on society and economy. This project promotes dialogue and knowledge transfer between those camps. It facilitates AI, Sustainable Finance, and ESG Technology innovation and collaboration. The following channels will disseminate expected results: 1) technical reviews, newspapers, and magazines, 2) public events (workshops for results presentation), and 3) knowledge exchange with stakeholders and project partners.

Planned Secondments

  • Swedbank AB (SWE), Prof. Dr. Tadas Gudaitis, M6, 18 months, policies for asset, sustainable fund management

  • Fraunhofer Institute (FRA), Prof. Dr. Ralf Korn, M27, 4 months, improve know-how transfer by using and implementing advanced financial models

Planned Timetable

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