Biological Integrity Under AI Mediation

1. Hypothesis

A transparent, safety-bounded and person-centred approach to biological integrity under ai mediation, combining validated measurement, informed consent, privacy and security controls, human oversight and longitudinal monitoring, will improve benefit–risk outcomes compared with opaque or technology-centred approaches.

2. Experiment design

Design: longitudinal human–AI biological integrity study combining controlled exposure, real-world monitoring and recovery assessment focused on Biological Integrity Under AI Mediation Methods: physiological monitoring; controlled exposure studies; ecological momentary assessment; longitudinal cohort follow-up; stress and workload testing; embodiment studies; systems safety analysis; expert and affected-user review; reproducibility testing; methods adapted specifically to Biological Integrity Under AI Mediation Independent variables: technology type; exposure or intervention intensity; duration; assurance controls; human oversight; user characteristics; operating context Dependent variables: functional outcome; biological or neural safety; user agency; privacy; reliability; recovery; subtopic-specific outcome for preservation of bodily safety and function when AI shapes action, exposure or care Confounders: age; health; disability; medication; prior experience; baseline physiology; environment; device quality; clinician or operator expertise; socioeconomic access Measures: heart-rate variability; stress and arousal; sleep; fatigue; pain; cognitive workload; homeostatic recovery; dependency; identity continuity; validated subtopic measures for preservation of bodily safety and function when AI shapes action, exposure or care; subgroup effects; false-positive and false-negative rates; user-reported burden Success criteria: Statistically and clinically or practically meaningful benefit; acceptable adverse-event profile; preserved agency and privacy; no disproportionate subgroup harm; reproducible performance; explicit safety limits; effective recovery and human escalation. Failure conditions: No meaningful benefit; biological, neural, psychological, privacy or rights harm exceeds benefit; performance fails outside narrow conditions; unsafe dependency emerges; consent or refusal is compromised; incidents cannot be detected, reversed or remediated.

3. Seed result / current evidence

DEFENSIBLE SEED RESULT — NON-EMPIRICAL. The current evidence supports Biological Integrity Under AI Mediation as a testable research proposition. Problem basis: AI systems increasingly influence attention, arousal, behaviour, movement and physiology, but biological safety limits and long-term interaction effects are poorly defined. The specific unresolved issue is preservation of bodily safety and function when AI shapes action, exposure or care. Directional expectation: If supported, the proposed approach should improve functional outcome; biological or neural safety; user agency; privacy; reliability; recovery; subtopic-specific outcome for preservation of bodily safety and function when AI shapes action, exposure or care while reducing adverse effects, misuse, exclusion, dependency and recovery time. Proposed observations: heart-rate variability; stress and arousal; sleep; fatigue; pain; cognitive workload; homeostatic recovery; dependency; identity continuity; validated subtopic measures for preservation of bodily safety and function when AI shapes action, exposure or care; subgroup effects; false-positive and false-negative rates; user-reported burden. Seed data profile: Evidence Strength 10/100; Confidence 25/100; Maturity 20/100; Overall Health 35/100; Novelty 80/100; Strategic Importance 95/100. Evidence boundary: No validated results yet.; experiments 0, studies 0, participants 0. This is suitable for protocol formation and baseline comparison, not as a finding of effect.

4. Seed conclusion

DEFENSIBLE SEED CONCLUSION — PROVISIONAL. Biological Integrity Under AI Mediation warrants structured testing because the CSV identifies a defined problem, falsifiable hypothesis, measurable outcomes and relevant literature foundations. The present position is that “A transparent, safety-bounded and person-centred approach to biological integrity under ai mediation, combining validated measurement, informed consent, privacy and security controls, human oversight and longitudinal monitoring, will improve benefit–risk outcomes compared with opaque or technology-centred approaches.” is plausible and decision-relevant, but unvalidated. Proceed to controlled testing against the stated success and failure conditions. Confirm, narrow or reject this seed after effect sizes, uncertainty, subgroup outcomes, adverse effects, persistence and handback performance are observed.

Prior-art search performed before starting

PRIOR-ART SEED BASELINE — PARTIAL. The CSV records these literature domains: Neuroscience; neurotechnology; physiology; medical-device safety; rehabilitation; cybersecurity; bioethics; human rights; literature specific to Biological Integrity Under AI Mediation. It also records: WHO human health guidance — https://www.who.int/; NIOSH occupational health — https://www.cdc.gov/niosh/; ISO human-centred design standards — https://www.iso.org/; OECD AI Principles — https://oecd.ai/. Evidence register status: “Seeded; authoritative source register initiated; empirical evidence not yet ingested”. This is defensible as a starting prior-art inventory, but not as proof of a completed systematic search because search dates, databases, exact queries, reviewer, result counts, screening decisions, claim mapping and a replayable receipt are absent.

Prior-art material named: Existing literature: Neuroscience; neurotechnology; physiology; medical-device safety; rehabilitation; cybersecurity; bioethics; human rights; literature specific to Biological Integrity Under AI Mediation. References: WHO human health guidance — https://www.who.int/; NIOSH occupational health — https://www.cdc.gov/niosh/; ISO human-centred design standards — https://www.iso.org/; OECD AI Principles — https://oecd.ai/

Critical gap / next action

Create and attach a dated prior-art search log; lock the protocol; execute the proposed study; link raw data and analysis; then replace the results and conclusion placeholders with evidence-bounded findings.