Human Resource Management and AI: A Contextual Transparency Database

TL;DR

This paper introduces the TARAI Index, a social practice-oriented database that assesses AI transparency in HR recruitment, emphasizing the dynamic and contextual nature of transparency shaped by professional practices.

Contribution

It presents a novel, participatory database framework that contextualizes AI transparency within social practices of recruiting, moving beyond technical definitions.

Findings

Transparency is shaped by social practices and interactions.

The TARAI Index provides a grounded approach to AI transparency.

Participatory design enhances contextual transparency understanding.

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.