Revisiting the Performance-Explainability Trade-Off in Explainable Artificial Intelligence (XAI)

• URL: http://arxiv.org/abs/2307.14239v1
• Date: Wed, 26 Jul 2023 15:07:40 GMT
• Title: Revisiting the Performance-Explainability Trade-Off in Explainable Artificial Intelligence (XAI)
• Authors: Barnaby Crook, Maximilian Schl\"uter, Timo Speith
• Abstract summary: We argue that it is best approached in a nuanced way that incorporates resource availability, domain characteristics, and considerations of risk. This work aims to advance the field of Requirements Engineering for AI.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Within the field of Requirements Engineering (RE), the increasing significance of Explainable Artificial Intelligence (XAI) in aligning AI-supported systems with user needs, societal expectations, and regulatory standards has garnered recognition. In general, explainability has emerged as an important non-functional requirement that impacts system quality. However, the supposed trade-off between explainability and performance challenges the presumed positive influence of explainability. If meeting the requirement of explainability entails a reduction in system performance, then careful consideration must be given to which of these quality aspects takes precedence and how to compromise between them. In this paper, we critically examine the alleged trade-off. We argue that it is best approached in a nuanced way that incorporates resource availability, domain characteristics, and considerations of risk. By providing a foundation for future research and best practices, this work aims to advance the field of RE for AI.

Related papers

• Causal Responsibility Attribution for Human-AI Collaboration [62.474732677086855]
  This paper presents a causal framework using Structural Causal Models (SCMs) to systematically attribute responsibility in human-AI systems. Two case studies illustrate the framework's adaptability in diverse human-AI collaboration scenarios.
  arXiv  Detail & Related papers  (2024-11-05T17:17:45Z)
• Predictable Artificial Intelligence [77.1127726638209]
  This paper introduces the ideas and challenges of Predictable AI. It explores the ways in which we can anticipate key validity indicators of present and future AI ecosystems. We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems.
  arXiv  Detail & Related papers  (2023-10-09T21:36:21Z)
• A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI) [0.0]
  We argue that it is best approached in a nuanced way that incorporates resource availability, domain characteristics, and considerations of risk. This work aims to advance the field of Requirements Engineering for AI.
  arXiv  Detail & Related papers  (2023-07-26T15:15:44Z)
• Causal Fairness Analysis [68.12191782657437]
  We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings. The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms. Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
  arXiv  Detail & Related papers  (2022-07-23T01:06:34Z)
• Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements [9.625088778011717]
  We discuss synergies between requirements engineering (RE) and Explainable AI (XAI) We highlight challenges in the field of XAI, and propose a framework and research directions on how RE practices can help to mitigate these challenges.
  arXiv  Detail & Related papers  (2022-06-03T11:17:41Z)
• Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
  Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome. Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations. We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
  arXiv  Detail & Related papers  (2021-06-14T20:48:48Z)
• Trustworthy AI [75.99046162669997]
  Brittleness to minor adversarial changes in the input data, ability to explain the decisions, address the bias in their training data, are some of the most prominent limitations. We propose the tutorial on Trustworthy AI to address six critical issues in enhancing user and public trust in AI systems.
  arXiv  Detail & Related papers  (2020-11-02T20:04:18Z)
• Estimating the Brittleness of AI: Safety Integrity Levels and the Need for Testing Out-Of-Distribution Performance [0.0]
  Test, Evaluation, Verification, and Validation for Artificial Intelligence (AI) is a challenge that threatens to limit the economic and societal rewards that AI researchers have devoted themselves to producing. This paper argues that neither of those criteria are certain of Deep Neural Networks.
  arXiv  Detail & Related papers  (2020-09-02T03:33:40Z)
• The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies [1.2762298148425795]
  Lack of transparency is identified as one of the main barriers to implementation of AI systems in health care. We review the recent literature to provide guidance to researchers and practitioners on the design of explainable AI systems. We conclude that explainable modelling can contribute to trustworthy AI, but the benefits of explainability still need to be proven in practice.
  arXiv  Detail & Related papers  (2020-07-31T09:08:27Z)
• Learning from Learning Machines: Optimisation, Rules, and Social Norms [91.3755431537592]
  It appears that the area of AI that is most analogous to the behaviour of economic entities is that of morally good decision-making. Recent successes of deep learning for AI suggest that more implicit specifications work better than explicit ones for solving such problems.
  arXiv  Detail & Related papers  (2019-12-29T17:42:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.