arXiv subjects

Takuya Yokota

Publications and source records attributed to Takuya Yokota.

3 recordsLinked to original sources

arXiv2025

Towards Multi-Stakeholder Evaluation of ML Models: A Crowdsourcing Study on Metric Preferences in Job-matching System

While machine learning (ML) technology affects diverse stakeholders, there is no one-size-fits-all metric to evaluate the quality of outputs, including performance and fairness. Using predetermined metrics without soliciting stakeholder opinions is problematic because it leads to an unfair disregard for stakeholders in the ML pipeline. In this study, to establish practical ways to incorporate diverse stakeholder opinions into the selection of metrics for ML, we investigate participants' preferences for different metrics by using crowdsourcing. We ask 837 participants to choose a better model from two hypothetical ML models in a hypothetical job-matching system twenty times and calculate their utility values for seven metrics. To examine the participants' feedback in detail, we divide them into five clusters based on their utility values and analyze the tendencies of each cluster, including their preferences for metrics and common attributes. Based on the results, we discuss the points that should be considered when selecting appropriate metrics and evaluating ML models with multiple stakeholders.

cs.CY↗

arXiv2024

Human-in-the-loop Fairness: Integrating Stakeholder Feedback to Incorporate Fairness Perspectives in Responsible AI

Fairness is a growing concern for high-risk decision-making using Artificial Intelligence (AI) but ensuring it through purely technical means is challenging: there is no universally accepted fairness measure, fairness is context-dependent, and there might be conflicting perspectives on what is considered fair. Thus, involving stakeholders, often without a background in AI or fairness, is a promising avenue. Research to directly involve stakeholders is in its infancy, and many questions remain on how to support stakeholders to feedback on fairness, and how this feedback can be integrated into AI models. Our work follows an approach where stakeholders can give feedback on specific decision instances and their outcomes with respect to their fairness, and then to retrain an AI model. In order to investigate this approach, we conducted two studies of a complex AI model for credit rating used in loan applications. In study 1, we collected feedback from 58 lay users on loan application decisions, and conducted offline experiments to investigate the effects on accuracy and fairness metrics. In study 2, we deepened this investigation by showing 66 participants the results of their feedback with respect to fairness, and then conducted further offline analyses. Our work contributes two datasets and associated code frameworks to bootstrap further research, highlights the opportunities and challenges of employing lay user feedback for improving AI fairness, and discusses practical implications for developing AI applications that more closely reflect stakeholder views about fairness.

cs.AI↗

arXiv2023

Stakeholder-in-the-Loop Fair Decisions: A Framework to Design Decision Support Systems in Public and Private Organizations

Due to the opacity of machine learning technology, there is a need for explainability and fairness in the decision support systems used in public or private organizations. Although the criteria for appropriate explanations and fair decisions change depending on the values of those who are affected by the decisions, there is a lack of discussion framework to consider the appropriate outputs for each stakeholder. In this paper, we propose a discussion framework that we call "stakeholder-in-the-loop fair decisions." This is proposed to consider the requirements for appropriate explanations and fair decisions. We identified four stakeholders that need to be considered to design accountable decision support systems and discussed how to consider the appropriate outputs for each stakeholder by referring to our works. By clarifying the characteristics of specific stakeholders in each application domain and integrating the stakeholders' values into outputs that all stakeholders agree upon, decision support systems can be designed as systems that ensure accountable decision makings.

cs.CY↗