While algorithmic bias and discriminatory outcomes from machine learning models trained on incomplete or historically biased datasets are documented, there is no systematic empirical framework for aud

The gap

Consensus across the literature

Supporting evidence — 4 representative gaps

• Consensus and legitimation in global AI regulations: a sociosemiotic perspective (2026) · doi

  The paper identifies that bias discourse in AI regulatory documents concentrates on bias avoidance rather than articulating concrete operational criteria for unbiased AI systems. Developing specific, measurable definitions of algorithmic bias that account for the spatiotemporal dependency of bias across different legal cultures and regions is needed to move beyond vague modifiers like 'harmful' or 'unjust' toward enforceable standards in global AI regulations.

  Keywords: algorithmic bias operational criteria global AI regulations semantic uncertainty spatiotemporal dependency
• AI Governance and Ethical Accountability in Indian Corporates: A New Dimension of Corporate Governance (2026) · doi

  The paper identifies that India currently follows a policy-driven approach to AI governance without binding legal rules, but does not specify which existing Indian corporate governance statutes (Companies Act 2013, Information Technology Act 2000) require amendment or what specific regulatory mechanisms should replace voluntary compliance frameworks to enforce AI accountability in corporate decision-making.

  Keywords: AI governance India regulatory framework Companies Act Information Technology Act corporate accountability enforcement mechanisms
• AI Governance and Ethical Accountability in Indian Corporates: A New Dimension of Corporate Governance (2026) · doi

  The proposed Responsible AI Governance Model emphasizes human oversight, transparency, and ethical accountability in AI-driven corporate decisions, but lacks specification of how board-level committees should operationalize risk-based AI categorization (adapted from EU AI Act) within Indian corporate structures, including audit protocols for algorithmic bias detection in hiring, financial analysis, and customer insights applications.

  Keywords: AI governance model board-level oversight risk-based categorization algorithmic bias audit protocols corporate decision-making
• Strategic value driven by artificial intelligence in global businesses: a bibliometric and qualitative analysis of the most influential literature (2026) · doi

  While algorithmic bias and discriminatory outcomes from machine learning models trained on incomplete or historically biased datasets are documented, there is no systematic empirical framework for auditing and measuring bias severity across different industry sectors and organizational contexts in AI-supported decision systems.

  Keywords: algorithmic bias machine learning models discriminatory outcomes audit mechanisms bias measurement AI governance