Explainability's Gain is Optimality's Loss? -- How Explanations Bias Decision-making

• URL: http://arxiv.org/abs/2206.08705v1
• Date: Fri, 17 Jun 2022 11:43:42 GMT
• Title: Explainability's Gain is Optimality's Loss? -- How Explanations Bias Decision-making
• Authors: Charles Wan, Rodrigo Belo, Leid Zejnilovi\'c
• Abstract summary: Explanations help to facilitate communication between the algorithm and the human decision-maker. Feature-based explanations' semantics of causal models induce leakage from the decision-maker's prior beliefs. Such differences can lead to sub-optimal and biased decision outcomes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decisions in organizations are about evaluating alternatives and choosing the one that would best serve organizational goals. To the extent that the evaluation of alternatives could be formulated as a predictive task with appropriate metrics, machine learning algorithms are increasingly being used to improve the efficiency of the process. Explanations help to facilitate communication between the algorithm and the human decision-maker, making it easier for the latter to interpret and make decisions on the basis of predictions by the former. Feature-based explanations' semantics of causal models, however, induce leakage from the decision-maker's prior beliefs. Our findings from a field experiment demonstrate empirically how this leads to confirmation bias and disparate impact on the decision-maker's confidence in the predictions. Such differences can lead to sub-optimal and biased decision outcomes.

Related papers

• Towards Objective and Unbiased Decision Assessments with LLM-Enhanced Hierarchical Attention Networks [6.520709313101523]
  This work investigates cognitive bias identification in high-stake decision making process by human experts. We propose bias-aware AI-augmented workflow that surpass human judgment. In our experiments, both the proposed model and the agentic workflow significantly improves on both human judgment and alternative models.
  arXiv  Detail & Related papers  (2024-11-13T10:42:11Z)
• Decision Theoretic Foundations for Experiments Evaluating Human Decisions [18.27590643693167]
  We argue that to attribute loss in human performance to forms of bias, an experiment must provide participants with the information that a rational agent would need to identify the utility-maximizing decision. As a demonstration, we evaluate the extent to which recent evaluations of decision-making from the literature on AI-assisted decisions achieve these criteria.
  arXiv  Detail & Related papers  (2024-01-25T16:21:37Z)
• Online Decision Mediation [72.80902932543474]
  Consider learning a decision support assistant to serve as an intermediary between (oracle) expert behavior and (imperfect) human behavior. In clinical diagnosis, fully-autonomous machine behavior is often beyond ethical affordances.
  arXiv  Detail & Related papers  (2023-10-28T05:59:43Z)
• Rational Decision-Making Agent with Internalized Utility Judgment [91.80700126895927]
  Large language models (LLMs) have demonstrated remarkable advancements and have attracted significant efforts to develop LLMs into agents capable of executing intricate multi-step decision-making tasks beyond traditional NLP applications. This paper proposes RadAgent, which fosters the development of its rationality through an iterative framework involving Experience Exploration and Utility Learning. Experimental results on the ToolBench dataset demonstrate RadAgent's superiority over baselines, achieving over 10% improvement in Pass Rate on diverse tasks.
  arXiv  Detail & Related papers  (2023-08-24T03:11:45Z)
• Explainable Data-Driven Optimization: From Context to Decision and Back Again [76.84947521482631]
  Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. We introduce a counterfactual explanation methodology tailored to explain solutions to data-driven problems. We demonstrate our approach by explaining key problems in operations management such as inventory management and routing.
  arXiv  Detail & Related papers  (2023-01-24T15:25:16Z)
• Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding [2.8498944632323755]
  We propose a unified framework for the robust design and evaluation of predictive algorithms in selectively observed data. We impose general assumptions on how much the outcome may vary on average between unselected and selected units. We develop debiased machine learning estimators for the bounds on a large class of predictive performance estimands.
  arXiv  Detail & Related papers  (2022-12-19T20:41:44Z)
• Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies [79.60322329952453]
  We show how to develop interpretable representations of how agents make decisions. By understanding the decision-making processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem. We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them. Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time.
  arXiv  Detail & Related papers  (2022-03-14T17:40:42Z)
• On the Fairness of Machine-Assisted Human Decisions [3.4069627091757178]
  We show that the inclusion of a biased human decision-maker can revert common relationships between the structure of the algorithm and the qualities of resulting decisions. In the lab experiment, we demonstrate how predictions informed by gender-specific information can reduce average gender disparities in decisions.
  arXiv  Detail & Related papers  (2021-10-28T17:24:45Z)
• 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)
• Inverse Active Sensing: Modeling and Understanding Timely Decision-Making [111.07204912245841]
  We develop a framework for the general setting of evidence-based decision-making under endogenous, context-dependent time pressure. We demonstrate how it enables modeling intuitive notions of surprise, suspense, and optimality in decision strategies.
  arXiv  Detail & Related papers  (2020-06-25T02:30:45Z)
• Causal Strategic Linear Regression [5.672132510411465]
  In many predictive decision-making scenarios, such as credit scoring and academic testing, a decision-maker must construct a model that accounts for agents' propensity to "game" the decision rule. We join concurrent work in modeling agents' outcomes as a function of their changeable attributes. We provide efficient algorithms for learning decision rules that optimize three distinct decision-maker objectives.
  arXiv  Detail & Related papers  (2020-02-24T03:57:22Z)

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.