arXiv subjects

Jay Barach

Publications and source records attributed to Jay Barach.

3 recordsLinked to original sources

arXiv2026

Counterfactual, Per-Decision Bias Auditing for Automated Hiring: Localizing and Explaining Disparate Impact in Applicant Tracking Systems

Automated applicant tracking systems increasingly decide who advances in hiring, and litigation and regulation now demand that those decisions be auditable. Existing tools sit at two extremes. Group fairness metrics such as the disparate impact ratio summarize a whole population but cannot say which individual decisions were unfair or why, while local explainers such as SHAP attribute a single prediction but are not connected to the legal standard by which hiring bias is judged. We present the AI Bias Firewall (AIBF), a method that audits an applicant tracking system one decision at a time. AIBF neutralizes a candidate's protected-attribute proxies, re-scores the decision, and measures the resulting counterfactual shift, which yields a signed per-decision bias in score points, a flag for decisions the protected attributes changed, and a plain-language explanation naming the responsible factors. We evaluate on two real public datasets, Adult and COMPAS, rather than on synthetic data. The per-decision counterfactual shift is faithful, aggregating to reproduce the known group level disparity, for example a mean shift of +7.5 points for the privileged group and -8.0 for the disadvantaged group on Adult, consistent with the measured statistical parity difference. AIBF identifies the decisions that protected attributes flipped with an area under the ROC curve of 0.963 on Adult, against 0.672 for a baseline that flags by group membership, and it identifies the harmed candidates so precisely that reviewing only five percent of decisions surfaces fifty-five percent of them, against six percent under group based review. We also report a limitation: correcting flagged decisions raises the disparate impact ratio substantially but not to legal parity, because features labeled as merit carry residual proxy correlation. AIBF is released under the Apache 2.0 license with code and experiments.

cs.CY↗

arXiv2026

AI-Increased Talent Retention Strategies: Fostering Long-Term Employee Engagement and Development in Talent Management

The integration of AI in Talent Management is a change in the way that organizations are designing their strategies for Talent Retention (TR), engagement, and future strategy. New and innovative tools such as predictive models, sentiment analysis, and personalized career planning have come up, and they offer better ways of addressing retention issues, workforce engagement, and, in general, sustainability. Through the application of predictive analytics, organizations can determine employees' likelihood of leaving the organization, who is likely to leave, and when to act, thus minimizing the costs and time associated with the recruitment process and improving performance. Furthermore, AI solutions help the development of individualized learning plans that help to define employees' professional goals and link them with the organization's strategy to encourage the employees' continuous growth. This paper explained how AI is impacting the retention process and how it can be used to decrease attrition rates, create a loyal workforce, and promote sustainable management of human and other resources. Furthermore, the author discusses the ethical issues, such as privacy and fairness of algorithms, that are involved in the implementation of AI systems. Thus, the above challenges can be solved by developing a sustainable and inclusive ecosystem that can help in the development of the future workforce. Based on a systematic review of literature, the study presents a framework that can help organizations improve their talent management practices with the help of AI to support long-term sustainable growth. It can be used to help industry professionals and decision-makers understand the new technological shifts that are occurring.

cs.CY↗

arXiv2026

AuthProbe: Specification-Driven, Multi-Identity Detection of Broken Object-Level Authorization in Recruitment API

Broken Object-Level Authorization (BOLA), also known as Insecure Direct Object Reference (IDOR), has topped the OWASP API Security ranking since 2019 and is the root cause of some of the largest exposures of applicant data in recruitment technology. The defining feature of this flaw class is that a malicious request is byte-for-byte indistinguishable from a legitimate one, which is precisely why web application firewalls and single identity scanners fail to catch it. We present AuthProbe, an open-source, black-box scanner that detects BOLA and IDOR in HTTP APIs by driving its tests from an OpenAPI specification and by acting under two or more identities that the operator controls. AuthProbe discovers, for each identity, the objects that identity legitimately owns, then attempts to read one identity's objects while authenticated as another and confirms a leak by comparing the response against a ground-truth fetch by the true owner. It also walks predictable identifiers to expose enumeration and reports missing authentication and existence oracles. The tool returns a severity-thresholded exit code and machine-readable reports so that it can gate a continuous integration build. On a synthetic recruitment API in which the McHire failure class is reproduced, AuthProbe detects every planted cross-identity read with no false positives on a hardened counterpart, and its running time grows linearly with the number of objects under test. AuthProbe is released under the Apache 2.0 license with an authorized-use guardrail.

cs.CR↗