Decoding HR Decisions: The Role of Explainable AI in Promoting Transparency

Clicks: 41
ID: 312026
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #2 of 395 articles by views in Social Sciences & Humanity Research Review

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 395 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
The integration of Artificial Intelligence (AI) into Human Resource (HR) processes—from recruitment and screening to performance evaluation and promotion promises efficiency and data-driven insights. However, the adoption of complex, 'black-box' AI models poses a significant challenge to accountability, fairness, and trust within organizations. When HR decisions cannot be easily interpreted or justified, they risk perpetuating bias, eroding employee trust, and exposing the organization to legal and ethical scrutiny. This paper demonstrates how Explainable AI (XAI) can not only enhance operational efficiency but also play a crucial role in ensuring fairness, reducing bias, and promoting transparency in AI-driven HR decisions. A recent survey found that while 40% of organizations have adopted AI in HR, only 34% have implemented XAI. Our findings indicate that XAI adoption boosts transparency, with 72% of employees reporting increased trust in AI-driven decisions. Furthermore, XAI supports regulatory compliance, particularly with frameworks like GDPR, by providing explainable decision-making processes. This study argues that transparency facilitated by Explainable AI is essential for harnessing the benefits of automation while upholding ethical standards, transforming HR from a gatekeeper of decisions into a steward of explainable, equitable, and accountable processes.
Reference Key
imported_1776675019_69e5e8cbdd8b2 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Muhammad Irfan Syed
Journal Social Sciences & Humanity Research Review
Year 2026
DOI
DOI not found
URL
https://sshrr.online/index.php/4/article/view/489
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.