AI bias remains a significant concern, as algorithms trained on historical data may reinforce existing inequalities or inaccuracies

The gap

Evidence profile

Sourced from the stated research gap and future-work section and stated challenges of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 3 journals. Those papers have been cited 120 times in total.

Supporting evidence — 4 representative gaps

• Ethics Before Algorithms: A Framework for AI-Driven Corporate Transparency (2026) · AI · doi

  The literature underscores that transparency often operates as a normative metaphor rather than an operational concept. National AI strategies continue to rely heavily on soft coordination and ethics-oriented guidance rather than enforceable rule-based structures. The study identifies a gap in the literature by highlighting the need for a systematic and replicable framework for synthesizing the rapidly expanding literature on AI-enabled corporate governance.

  generalstated research gap
  Keywords: literature underscores transparency often operates normative metaphor rather
• Financial Reporting Integrity in the Digital and Sustainability Era: A Systematic Literature Review on Governance, ESG, and Technological Transparency (2026) · Inkubis : Jurnal Ekonomi dan Bisnis · doi

  Future studies should examine the impact of ESG disclosure, blockchain technology, and artificial intelligence on financial reporting integrity. Research should investigate the role of governance mechanisms in promoting transparency and accountability in corporate reporting. The study suggests that future research should prioritize cross-disciplinary approaches, emerging market contexts, and ethical risk assessments in digital reporting practices.

  generalfuture-work sectionevidence 5/5
  Keywords: future studies examine impact esg disclosure blockchain technology
• AI-Driven Financial Transparency and Corporate Governance: Enhancing Accounting Practices with Evidence from Jordan (2025) · Sustainability · cited 60× · doi

  Developing economies like Jordan face unique vulnerabilities that hinder effective oversight and regulatory enforcement. Regulatory frameworks in Jordan have undergone continuous evolution, yet challenges remain in ensuring full compliance with international financial reporting standards and corporate governance principles. There is a need for studies that investigate the impact of AI-driven financial transparency on corporate governance in developing economies like Jordan.

  generalstated research gapevidence 5/5
  Keywords: developing economies like jordan face unique vulnerabilities hinder
• AI-Driven Financial Transparency and Corporate Governance: Enhancing Accounting Practices with Evidence from Jordan (2025) · Sustainability · cited 60× · doi

  AI bias remains a significant concern, as algorithms trained on historical data may reinforce existing inequalities or inaccuracies. Data privacy is another critical challenge, particularly in jurisdictions like Jordan, where AI regulations are still evolving. The need for regulatory adaptation persists, as AI-driven financial transparency requires alignment with international compliance standards and regulatory frameworks.

  generalstated challengesevidence 5/5
  Keywords: bias remains significant concern algorithms trained historical data

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