Responsible AI
Chapter 15 of Loop Engineering argues that the architecture is a tool and tools have obligations: disclosure, recourse, opt-out, dataset transparency, calibration support, human-in-the-loop at materiality thresholds, audit trail, and operator accountability.
This page extends Ch 15 in two directions: cross-disciplinary reading paths (psychology, philosophy, cognitive science, STS) and the operator obligations checklist mapped to the architectural layers.
Cross-disciplinary reading paths
- Psychology — cognitive load (Sweller), trust calibration (Lee & See 2004), automation bias (Parasuraman & Riley 1997; Parasuraman & Manzey 2010), attention (Wickens et al.).
- Philosophy — moral agency (Floridi), consent in AI partnerships, the boundary between operator obligation and architecture obligation.
- Cognitive science — unified cognitive architectures (Anderson's ACT-R, Newell's SOAR), dual-process models (Kahneman).
- STS / sociology of work — algorithmic harm (Eubanks), accountability voids (Mittelstadt), Ironies of Automation (Bainbridge 1983).
Operator obligations checklist
The Ch 15 obligations mapped to which architectural layer carries each one:
- Disclosure — dialog interface, presentation layer.
- Recourse — fleet layer (non-AI escalation), human-in-the-loop posture.
- Opt-out — dialog interface, consent capture.
- Dataset transparency — memory layer, training-data documentation.
- Calibration support — dialog interface (uncertainty signaling), prefrontal (visible markers).
- Human-in-the-loop — materiality dial, autonomy posture.
- Audit trail — defensibility bundle, action log, nervous-system traces.
- Operator accountability — deployment context, fleet governance.
Expanded reading paths and the obligations checklist ship as Tier 2 content. The print chapter carries the argument; this site carries the operator-side implementation guide.