Digital inclusion and algorithmic bias: auditing AI systems in HR for gender-neutral outcomes

Purpose This study examines a mediated structural model exploring how digital inclusion and algorithmic transparency shape gender-neutral outcomes and trust in AI, operating through perceived algorithmic fairness and bias mitigation practices within AI-driven HR decision-making. Design/methodology/approach A cross-sectional quantitative survey was conducted among 400 HR professionals across India's technology, finance, healthcare, and manufacturing sectors, using data gathered via an online questionnaire shared through HR networks and industry associations. Structural Equation Modeling (SPSS 28.0, AMOS 27.0) with bootstrapped mediation analysis (5,000 resamples) was used to test the hypothesized relationships, while full collinearity VIF assessed common method bias. Findings Digital inclusion and algorithmic transparency strongly predict gender-neutral outcomes but show negligible direct influence on trust in AI. Instead, trust is driven mainly through perceived fairness and bias mitigation, highlighting that governance mechanisms matter more than technological features alone. Originality/value The study contributes a validated socio-technical framework positioning fairness perceptions and bias mitigation as core mediators linking inclusion and transparency to equitable, trustworthy AI-based HR outcomes.