Advancing Explainable and Fair AI

We propose three subthemes within this area, with the common goal of elevating AI to new standards of transparency, fairness, and sustainability.  Application areas include healthcare and industrial applications, with a research focus ranging from foundations via implementation to work practices. 

NB! Applicants are asked to apply for one of these sub-themes. Please indicate clearly which of the sub-themes you have chosen for your proposal by using one of the codes ASR1, ASR2, ASR3.

Mentoring and internship will be offered by a relevant external partner.

Theme ASR1. Explainable AI for Digital and Green Transition

  • Contact person: Ingrid Chieh Yu
  • Keywords: Explainable AI, Transparency and Trust in AI, Circular Economy, Sustainable Product Lifecycle
  • Research group: Analytical Solutions and Reasoning (ASR)

This research theme explores the intersection of explainable AI (XAI) with sustainable digital and green industry practices. Focusing on circular product lifecycle management, this project aims to enhance transparency in AI-driven systems, allowing stakeholders to understand, trust, and engage with these technologies. Emphasis will be placed on assessing and evolving XAI methods to meet diverse stakeholder needs, fostering innovation that aligns with environmental and social goals. By advancing models for explainability and lifecycle adaptability, the project aspires to promote both sustainable production and informed, transparent AI usage.

Relevant topics from methodological research:

  1. Circular Product Lifecycle Management with XAI and Model Evolution (application of XAI for industry): Integrating XAI into circular product lifecycle management, focusing on adaptive models that evolve alongside product stages, from design and production to recycling. Explore AI methodologies that offer transparent decision-making insights, driving sustainable practices within the lifecycle framework.
  2. Framework of XAI Methods with Industrial Domain Emphasis (method and prototype development): how external knowledge from various sources, such as text, datasheets, time series, graphs, etc., can contribute to achieving unified explanations; applicability to diverse industrial scenarios; and identification of methods and prototypes that best support the transparency and accountability demands of the digital and green transition.
  3. Human-Centric Explainability for Stakeholders (evaluation framework): Define metrics and criteria for explainability from the perspective of various stakeholders, including designers, manufacturers, consumers, and regulators. Develop evaluation frameworks that gauge how XAI m