Cover Image

Explanatory Debiasing: Involving Domain Experts in the Data Generation Process to Mitigate Representation Bias in AI Systems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bhattacharya, Aditya, Stumpf, Simone, De Croon, Robin, Verbert, Katrien
Format:	Preprint
Published:	2024
Subjects:	Human-Computer Interaction Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Representation Debiasing of Generated Data Involving Domain Experts
by: Bhattacharya, Aditya, et al.
Published: (2024)

An Explanatory Model Steering System for Collaboration between Domain Experts and AI
by: Bhattacharya, Aditya, et al.
Published: (2024)

Importance of User Control in Data-Centric Steering for Healthcare Experts
by: Bhattacharya, Aditya, et al.
Published: (2025)

EXMOS: Explanatory Model Steering Through Multifaceted Explanations and Data Configurations
by: Bhattacharya, Aditya, et al.
Published: (2024)

Show Me How: Benefits and Challenges of Agent-Augmented Counterfactual Explanations for Non-Expert Users
by: Bhattacharya, Aditya, et al.
Published: (2025)

Lessons Learned from EXMOS User Studies: A Technical Report Summarizing Key Takeaways from User Studies Conducted to Evaluate The EXMOS Platform
by: Bhattacharya, Aditya, et al.
Published: (2023)

Visual-Conversational Interface for Evidence-Based Explanation of Diabetes Risk Prediction
by: Samimi, Reza, et al.
Published: (2025)

Towards a Comprehensive Human-Centred Evaluation Framework for Explainable AI
by: Donoso-Guzmán, Ivania, et al.
Published: (2023)

Designing Visual Explanations and Learner Controls to Engage Adolescents in AI-Supported Exercise Selection
by: Ooge, Jeroen, et al.
Published: (2024)

Petal-X: Human-Centered Visual Explanations to Improve Cardiovascular Risk Communication
by: Rojo, Diego, et al.
Published: (2024)

A Systematic Review of User-Centred Evaluation of Explainable AI in Healthcare
by: Donoso-Guzmán, Ivania, et al.
Published: (2025)

Towards Directive Explanations: Crafting Explainable AI Systems for Actionable Human-AI Interactions
by: Bhattacharya, Aditya
Published: (2023)

Let's Get You Hired: A Job Seeker's Perspective on Multi-Agent Recruitment Systems for Explaining Hiring Decisions
by: Bhattacharya, Aditya, et al.
Published: (2025)

"I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment
by: Luo, Lin, et al.
Published: (2025)

Leveraging Generative AI for Human Understanding: Meta-Requirements and Design Principles for Explanatory AI as a new Paradigm
by: Meske, Christian, et al.
Published: (2025)

Confirmation Bias in Generative AI Chatbots: Mechanisms, Risks, Mitigation Strategies, and Future Research Directions
by: Du, Yiran
Published: (2025)

Actionable AI: Enabling Non Experts to Understand and Configure AI Systems
by: Boulard, Cécile, et al.
Published: (2025)

From Symptoms to Systems: An Expert-Guided Approach to Understanding Risks of Generative AI for Eating Disorders
by: Winecoff, Amy, et al.
Published: (2025)

Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Tasks
by: Sivaraman, Venkatesh, et al.
Published: (2025)

Predicting Satisfaction of Counterfactual Explanations from Human Ratings of Explanatory Qualities
by: Domnich, Marharyta, et al.
Published: (2025)

Data Therapist: Eliciting Domain Knowledge from Subject Matter Experts Using Large Language Models
by: Shin, Sungbok, et al.
Published: (2025)

User Characteristics in Explainable AI: The Rabbit Hole of Personalization?
by: Nimmo, Robert, et al.
Published: (2024)

Transferring Domain Knowledge with (X)AI-Based Learning Systems
by: Spitzer, Philipp, et al.
Published: (2024)

Generative Confidants: How do People Experience Trust in Emotional Support from Generative AI?
by: Volpato, Riccardo, et al.
Published: (2026)

Promoting Critical Thinking With Domain-Specific Generative AI Provocations
by: von Davier, Thomas Şerban, et al.
Published: (2026)

A Generative AI System for Biomedical Data Discovery with Grammar-Based Visualizations
by: Lange, Devin, et al.
Published: (2025)

Conversational Explanations: Discussing Explainable AI with Non-AI Experts
by: Zhang, Tong, et al.
Published: (2025)

From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems
by: Rismani, Shalaleh, et al.
Published: (2024)

EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders
by: Luo, Lin, et al.
Published: (2024)

AI Expert Twin: Capturing Expert Cognition for Human-Centred, Practice-Based Learning
by: Yuan, Annie, et al.
Published: (2026)

Dehumanizing Machines: Mitigating Anthropomorphic Behaviors in Text Generation Systems
by: Cheng, Myra, et al.
Published: (2025)

Data Analysis in the Era of Generative AI
by: Inala, Jeevana Priya, et al.
Published: (2024)

Towards Understanding the Impact of Guidance in Data Visualization Systems for Domain Experts
by: Qiu, Sherry, et al.
Published: (2024)

HearHere: Mitigating Echo Chambers in News Consumption through an AI-based Web System
by: Jeon, Youngseung, et al.
Published: (2024)

Key Considerations for Domain Expert Involvement in LLM Design and Evaluation: An Ethnographic Study
by: Szymanski, Annalisa, et al.
Published: (2026)

HARMONIC: A Framework for Explanatory Cognitive Robots
by: Oruganti, Sanjay, et al.
Published: (2024)

Ensuring Fairness with Transparent Auditing of Quantitative Bias in AI Systems
by: Yuan, Chih-Cheng Rex, et al.
Published: (2024)

A LLM-based Controllable, Scalable, Human-Involved User Simulator Framework for Conversational Recommender Systems
by: Zhu, Lixi, et al.
Published: (2024)

AI-Assisted Writing in Education: Ecosystem Risks and Mitigations
by: Shibani, Antonette, et al.
Published: (2024)

Guidance Source Matters: How Guidance from AI, Expert, or a Group of Analysts Impacts Visual Data Preparation and Analysis
by: Narechania, Arpit, et al.
Published: (2025)