How much informative is your XAI? A decision-making assessment task to objectively measure the goodness of explanations

2023-12-07 · Marco Matarese, Francesco Rea, Alessandra Sciutti

There is an increasing consensus about the effectiveness of user-centred approaches in the explainable artificial intelligence (XAI) field. Indeed, the number and complexity of personalised and user-centred approaches to XAI have rapidly grown in recent years. Often, these works have a two-fold objective: (1) proposing novel XAI techniques able to consider the users and (2) assessing the \textit{goodness} of such techniques with respect to others. From these new works, it emerged that user-centred approaches to XAI positively affect the interaction between users and systems. However, so far, the goodness of XAI systems has been measured through indirect measures, such as performance. In this paper, we propose an assessment task to objectively and quantitatively measure the goodness of XAI systems in terms of their \textit{information power}, which we intended as the amount of information the system provides to the users during the interaction. Moreover, we plan to use our task to objectively compare two XAI techniques in a human-robot decision-making task to understand deeper whether user-centred approaches are more informative than classical ones.

📄 PDF Abstract BibTeX arXiv:2312.04379

Code (0)

등록된 구현이 없습니다.

Tasks

Decision MakingExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)

Analytical Techniques to Support Hospital Case Mix Planning

2023-07-31 · Robert L Burdett, Paul corry, David Cook, Prasad Yarlagadda

This article introduces analytical techniques and a decision support tool to support capacity assessment and case mix planning (CMP) approaches previously created for hospitals. First, an optimization model is proposed t…

Decision Making

Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications

2018-05-20 · Daniel S. Brown, Scott Niekum

Inverse reinforcement learning (IRL) infers a reward function from demonstrations, allowing for policy improvement and generalization. However, despite much recent interest in IRL, little work has been done to understand…

Decision Making reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

Hospitality-VQA: Decision-Oriented Informativeness Evaluation for Vision-Language Models

2026-03-09 · Jeongwoo Lee, Baek Duhyeong, Eungyeol Han, Soyeon Shin 외 arxiv

Recent advances in Vision-Language Models (VLMs) have demonstrated impressive multimodal understanding in general domains. However, their applicability to decision-oriented domains such as hospitality remains largely une…

Visual Question Answering

Fatigue Assessment using ECG and Actigraphy Sensors

2020-08-06 · Yang Bai, Yu Guan, Wan-Fai Ng

Fatigue is one of the key factors in the loss of work efficiency and health-related quality of life, and most fatigue assessment methods were based on self-reporting, which may suffer from many factors such as recall bia…

Decision Making Deep Learning Feature Engineering feature selection

The Utility of “Even if” Semifactual Explanation to Optimise Positive Outcomes

2023-09-21 · NeurIPS 2023 11

When users receive either a positive or negative outcome from an automated system, Explainable AI (XAI) has almost exclusively focused on how to mutate negative outcomes into positive ones by crossing a decision boundary…