Logging Requirement for Continuous Auditing of Responsible Machine Learning-based Applications

Logging is essential for ML accountability: Traditional software logging practices can be adapted to create traceable records of ML system behavior, enabling organizations to audit whether their models comply with ethical and legal standards—a critical gap given current concerns about transparency and fairness in automated decision-making.

Current logging practices are insufficient: The paper identifies specific deficiencies in how ML applications are currently monitored, suggesting that existing logging approaches do not adequately capture the responsible AI metrics needed to demonstrate compliance or detect problematic model behavior.

Regulatory compliance depends on better auditing tools: As regulations around AI continue to grow, organizations need enhanced logging and tooling that systematically integrate responsible AI metrics into their monitoring practices—without these improvements, companies may struggle to prove their systems meet legal and ethical requirements.

Continuous auditing enables early detection of problems: By implementing comprehensive logging that tracks ML system decisions over time, organizations can continuously monitor for fairness issues, bias, and other ethical concerns rather than discovering problems only after harm occurs.

Actionable guidance for practitioners and developers: The paper provides specific recommendations for both companies deploying ML systems and tool developers, offering a roadmap for building more trustworthy and accountable AI applications that align with societal expectations and regulatory demands.