Post-hoc vs ante-hoc explanations: xAI design guidelines for data scientists

The growing field of explainable Artificial Intelligence (xAI) has given rise to a multitude of techniques and methodologies, yet this expansion has created a growing gap between existing xAI approaches and their practical application. This poses a considerable obstacle for data scientists striving to identify the optimal xAI technique for their needs. To address this problem, our study presents a customized decision support framework to aid data scientists in choosing a suitable xAI approach for their use-case. Drawing from a literature survey and insights from interviews with five experienced data scientists, we introduce a decision tree based on the trade-offs inherent in various xAI approaches, guiding the…

• EC
  European Commission

  Award: 826078
• AS
  Austrian Science Fund

  Award: P-32554
• H2
  Horizon 2020 Framework Programme
• HE
  HORIZON EUROPE Framework Programme

  Award: 826078
• H2
  Horizon 2020