The Case for Selective Non-Transparency in AI-Mediated Work: A Workers Rights Framework

Abstract This article advances a labor rights argument for selective non-transparency in AI-mediated work, framed through educational labor yet generalizable across the United States. The central claim asserts that blanket requirements to disclose all AI-assisted methods reconfigure managerial power by converting process knowledge into a surveillance substrate, thereby diminishing autonomy, chilling professional judgment, and enabling rapid deskilling. Synthesizing U.S. labor and privacy frameworks—especially interpretations of the National Labor Relations Act regarding electronic monitoring—alongside emerging state initiatives on automated decision systems, the analysis demonstrates how expansive process transparency risks infringing protected concerted activity while normalizing function creep in algorithmic management. Empirical literature on electronic performance monitoring and mental health indicates consistent associations with increased stress, reduced trust, and lower perceived control, outcomes that are antithetical to high-quality professional practice in education and other knowledge sectors. Case studies from teaching, logistics, creative industries, and professional services illustrate that transparency asymmetries matter: workers benefit when transparency applies to employer systems and outcomes, not to every micro-step of worker method. The article proposes an accountability model centered on transparency of outcomes and systems with privacy of process: results-oriented evaluation, periodic audits rather than continuous telemetry, worker participation in AI governance, privacy-preserving assurance, and contractual or statutory limits on process capture. This normative and policy framework preserves public accountability and ethical AI use while safeguarding method discretion, intellectual capital, and human dignity—conditions under which AI functions as augmentation for labor rather than instrumentation of control.