Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives

• URL: http://arxiv.org/abs/2404.08721v1
• Date: Fri, 12 Apr 2024 13:11:55 GMT
• Title: Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives
• Authors: Orfeas Menis Mastromichalakis, Jason Liartis, Giorgos Stamou,
• Abstract summary: Counterfactual Explanations (CFEs) offer insights into the decision-making processes of machine learning algorithms. Existing literature often overlooks the diverse needs and objectives of users across different applications and domains. We advocate for a nuanced understanding of CFEs, recognizing the variability in desired properties based on user objectives and target applications.
• Score: 2.3369294168789203
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Explainable Artificial Intelligence (XAI) has emerged as a critical area of research aimed at enhancing the transparency and interpretability of AI systems. Counterfactual Explanations (CFEs) offer valuable insights into the decision-making processes of machine learning algorithms by exploring alternative scenarios where certain factors differ. Despite the growing popularity of CFEs in the XAI community, existing literature often overlooks the diverse needs and objectives of users across different applications and domains, leading to a lack of tailored explanations that adequately address the different use cases. In this paper, we advocate for a nuanced understanding of CFEs, recognizing the variability in desired properties based on user objectives and target applications. We identify three primary user objectives and explore the desired characteristics of CFEs in each case. By addressing these differences, we aim to design more effective and tailored explanations that meet the specific needs of users, thereby enhancing collaboration with AI systems.

Related papers

• Tell me more: Intent Fulfilment Framework for Enhancing User Experiences in Conversational XAI [0.6333053895057925]
  This paper explores how different types of explanations collaboratively meet users' XAI needs. We introduce the Intent Fulfilment Framework (IFF) for creating explanation experiences. The Explanation Experience Dialogue Model integrates the IFF and "Explanation Followups" to provide users with a conversational interface.
  arXiv  Detail & Related papers  (2024-05-16T21:13:43Z)
• Introducing User Feedback-based Counterfactual Explanations (UFCE) [49.1574468325115]
  Counterfactual explanations (CEs) have emerged as a viable solution for generating comprehensible explanations in XAI. UFCE allows for the inclusion of user constraints to determine the smallest modifications in the subset of actionable features. UFCE outperforms two well-known CE methods in terms of textitproximity, textitsparsity, and textitfeasibility.
  arXiv  Detail & Related papers  (2024-02-26T20:09:44Z)
• Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposal [0.0]
  This study conducts a thorough review of extant research in Explainable Machine Learning (XML) Our main objective is to offer a classification of XAI methods within the realm of XML. We propose a mapping function that take to account users and their desired properties and suggest an XAI method to them.
  arXiv  Detail & Related papers  (2023-02-07T01:06:38Z)
• Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization [73.89239820192894]
  We argue that automated counterfactual generation should regard several aspects of the produced adversarial instances. We present a novel framework for the generation of counterfactual examples.
  arXiv  Detail & Related papers  (2022-05-20T15:02:53Z)
• "There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making [0.0]
  Automated decision systems (ADS) are increasingly used for consequential decision-making. We conduct a human subject study to assess people's perceptions of informational fairness. A comprehensive analysis of qualitative feedback sheds light on people's desiderata for explanations.
  arXiv  Detail & Related papers  (2022-05-11T20:06:03Z)
• Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learning [6.883906273999368]
  Counterfactual explanations (CFEs) have gained traction as a psychologically grounded approach to generate post-hoc explanations. We introduce the Alien Zoo, an engaging, web-based and game-inspired experimental framework. As a proof of concept, we demonstrate the practical efficacy and feasibility of this approach in a user study.
  arXiv  Detail & Related papers  (2022-05-06T17:57:05Z)
• Confounder Identification-free Causal Visual Feature Learning [84.28462256571822]
  We propose a novel Confounder Identification-free Causal Visual Feature Learning (CICF) method, which obviates the need for identifying confounders. CICF models the interventions among different samples based on front-door criterion, and then approximates the global-scope intervening effect upon the instance-level interventions. We uncover the relation between CICF and the popular meta-learning strategy MAML, and provide an interpretation of why MAML works from the theoretical perspective.
  arXiv  Detail & Related papers  (2021-11-26T10:57:47Z)
• 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)
• User-Oriented Smart General AI System under Causal Inference [0.0]
  General AI system solves a wide range of tasks with high performance in an automated fashion. The best general AI algorithm designed by one individual is different from that devised by another. Tacit knowledge depends upon user-specific comprehension of task information and individual model design preferences.
  arXiv  Detail & Related papers  (2021-03-25T08:34:35Z)
• Leveraging Expert Consistency to Improve Algorithmic Decision Support [62.61153549123407]
  We explore the use of historical expert decisions as a rich source of information that can be combined with observed outcomes to narrow the construct gap. We propose an influence function-based methodology to estimate expert consistency indirectly when each case in the data is assessed by a single expert. Our empirical evaluation, using simulations in a clinical setting and real-world data from the child welfare domain, indicates that the proposed approach successfully narrows the construct gap.
  arXiv  Detail & Related papers  (2021-01-24T05:40:29Z)
• Optimizing Interactive Systems via Data-Driven Objectives [70.3578528542663]
  We propose an approach that infers the objective directly from observed user interactions. These inferences can be made regardless of prior knowledge and across different types of user behavior. We introduce Interactive System (ISO), a novel algorithm that uses these inferred objectives for optimization.
  arXiv  Detail & Related papers  (2020-06-19T20:49:14Z)

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.