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Designing for Human Agency in AI-Augmented Software: A Quality Attribute Approach#

@Scale: AI & Data 2026 digest Program Wildcard pick 17 June 2026

Abstract

Rohan Vardhan

  • Why: Not infrastructure at all — but a genuinely novel framing: treating human-centeredness as a measurable software quality attribute (like performance or security) rather than a design philosophy, which changes how AI features would be specified and tested.
  • What: An argument that ISO/IEC 25010:2023 and similar quality models don't capture what makes AI-augmented software supportive rather than corrosive to human judgment, proposing the ACCL framework — Agency Preservation, Calibrated Trust, Contestability, Learnability of Limits — plus three previously undocumented failure modes: reflexive bypass, trust debt, and the right-sized explanation problem (from abstract only).
  • How: For each attribute, architectural implications and organizational patterns grounded in field observation across multiple organizations; the failure modes come from practitioner experience rather than formal study.
  • Where applicable: Conceptual/framework work rather than a deployed system — no empirical validation claimed. Useful as a vocabulary for anyone writing requirements for AI-assisted internal tools (including agent platforms of the kind the rest of this program deploys).