arXiv AI August 11, 2026 By Sundaraparipurnan Narayanan

Designing for Ethical AI: HCI Feature Considerations to Improve Fairness and User Experience in AutoML use for Human Resources

arXiv:2608. 07477v1 Announce Type: cross Abstract: This thesis examines the fairness of Automated Machine Learning (AutoML) tools in human resource hiring systems through the combined lenses of regulation, business strategy, and Human-Computer Interaction (HCI).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 21

Equality, Equity, and Causality in Fairness Research: A Commentary on Cheng (2026)

arXiv:2607. 17679v1 Announce Type: cross Abstract: This is an invited commentary on the Psychometrika focus article "Fairness Issues and Evaluation in Psychometrics and AI/ML: What Can We Learn from Each Field?

By Youmi Suk

safety

arXiv AI

Jul 15

Vertical Standardisation for High-Risk AI Systems under the EU AI Act: A Domain-Specific Framework for Algorithmic Hiring

arXiv:2607. 12588v1 Announce Type: new Abstract: According to the recent European legislation, high-risk AI systems will have to adapt in order to comply with requirements related to specific areas, like risk management, data quality and governance, logging and traceability, technical documentation, transparency, human oversight, and accuracy, as outlined in the European Artificial Intelligence (AI) Act.

By Anna Gatzioura, Vrettos Moulos, Nina Baranowska

safety data-engineering

arXiv Machine Learning

Jun 4

Be Fair! Can Machine Learning Engineering Agents Adhere to Fairness Constraints?

arXiv:2606. 04971v1 Announce Type: new Abstract: Machine learning engineering (MLE) agents promise to automate end-to-end ML pipeline development from raw data and natural language instructions, potentially making ML accessible to non-technical domain experts.

By Anna Richter, Julia Stoyanovich, Sebastian Schelter

agents benchmarks safety

arXiv AI

Jun 8

Automatic Causal Fairness Analysis with LLM-Generated Reporting

arXiv:2604. 27011v2 Announce Type: replace-cross Abstract: AutoML, intended as the process of automating the application of machine learning to real-world problems, is a key step for AI popularisation.

By Alessia Berarducci, Eric Rossetto, Alessandro Antonucci, Marco Zaffalon

llms safety

arXiv AI

Jul 29

Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening

arXiv:2507. 11548v3 Announce Type: replace-cross Abstract: The use of publicly available generative AI systems for resume evaluation is often justified by the assumption that these tools reduce bias relative to human judgment.

By Kevin T Webster

robotics safety

arXiv AI

Jun 26

Statistical and Structural Approaches to Algorithmic Fairness

arXiv:2606. 26200v1 Announce Type: cross Abstract: Modern machine learning systems have outgrown their origins as isolated predictive constructs, evolving into complex socio-technical architectures that actively mediate human opportunity.

By Antonio Ferrara

safety

Related stories

Equality, Equity, and Causality in Fairness Research: A Commentary on Cheng (2026)

Vertical Standardisation for High-Risk AI Systems under the EU AI Act: A Domain-Specific Framework for Algorithmic Hiring

Be Fair! Can Machine Learning Engineering Agents Adhere to Fairness Constraints?

Automatic Causal Fairness Analysis with LLM-Generated Reporting

Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening

Statistical and Structural Approaches to Algorithmic Fairness