Human Resource Management and AI: A Contextual Transparency Database

2025-11-05

AI Summary

The widespread deployment of AI in recruitment has intensified concerns regarding algorithmic opacity (“black-box” problems), while existing transparency initiatives fail to accommodate the situated, dynamic nature of human resource (HR) professional practice. This study proposes a practice-centered framework for AI transparency, conceptualizing transparency as a generative process embedded within professional interactions, capability development, and institutional contexts. Employing an iterative mixed-methods approach—including functional analysis of AI recruitment tools, textual analysis of corporate transparency statements, elicitation of practitioner assumptions, and multi-dimensional clarity assessments—we construct TARAI Index, the first global, empirically grounded AI transparency database specific to HR. TARAI Index not only reveals how transparency is dynamically co-constructed across recruitment contexts but also establishes a participatory, extensible database design paradigm. It thereby advances both theoretical understanding and practical implementation of AI transparency in professional domains.

Technology Category

• Humans and AI: AI for Accessibility
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web crawling and indexing

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.

Problem

• Addressing AI transparency needs in human resources management systems
• Developing contextual transparency through social practices in recruiting
• Creating actionable framework for understanding AI in professional HR contexts

Innovation

• TARAI Index database for AI transparency in HR
• Analyzes product functionality and AI clarity claims
• Uses iterative mixed-methods participatory design approach