Human Resource Management and AI: A Contextual Transparency Database
Ellen Simpson, Ryan Ermovick, Mona Sloane
TL;DR
The paper addresses the problem of profession-specific AI transparency in HRM, particularly in recruiting, where opaque AI can shape access to the labor market. It introduces the TARAI Index, a participatory, context-aware HR Tech database built through mixed methods, including 100+ interviews, text analysis of 113 products, and iterative design workshops. The study reframes AI transparency as a social practice tied to material artifacts, competencies, and meanings in recruitment, offering a framework that catalogs AI functionality, marketing claims, underlying assumptions, and perceived clarity in two environments (Researcher and Recruiter). The contribution is a practical blueprint for contextual transparency databases and a methodological template that can extend to other professional domains.
Abstract
AI tools are proliferating in human resources management (HRM) and recruiting, helping to mediate access to the labor market. As these systems spread, profession-specific transparency needs emerging from black-boxed systems in HRM move into focus. Prior work often frames transparency technically or abstractly, but we contend AI transparency is a social project shaped by materials, meanings, and competencies of practice. This paper introduces the Talent Acquisition and Recruiting AI (TARAI) Index, situating AI systems within the social practice of recruiting by examining product functionality, claims, assumptions, and AI clarity. Built through an iterative, mixed-methods process, the database demonstrates how transparency emerges: not as a fixed property, but as a dynamic outcome shaped by professional practices, interactions, and competencies. By centering social practice, our work offers a grounded, actionable approach to understanding and articulating AI transparency in HR and provides a blueprint for participatory database design for contextual transparency in professional practice.