DC9: Audience-dependent explanations

Objectives

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.

Secondments (2)

Recruitment & Hosting Details

DC 9 UNA University of Naples M9 36

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DC 9 UNA UNA Month 9 36 months D 3.1 - 3.2

Audience-dependent explanations (WP 3)

Objectives: 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, Prof. Dr. Tadas Gudaitis, M21, 18 months, policies for asset, sustainable fund management

Fraunhofer, Prof. Dr. Ralf Korn, M39, 6 months, improve know-how transfer by using and implementing advanced financial models

Fellow Host institution PhD enrolment Start date Duration Deliverables

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Deliverables