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Record STMT-WC7E53BVoluntary

An Operational Ethics Framework for Human– Humanoid Robot Interaction: Intent Confirmation, Consequence Grading, Capability Protection, and Event Traceability 人形机器人人机交互伦理操作框架:意图确认、后果分级、能力保护与事件追溯

Framework Institutional policyGlobaldraft

This operational ethics framework addresses human-humanoid robot interaction by focusing on input-side governance, specifically how an embodied humanoid robot should decline high-risk human requests. Grounded in five working hypotheses from neo-humanist theory, the framework derives engineering artifacts including an A/B/C/D consequence-grading model, a dual-source split-key black box, an adversarial test suite for sycophancy, and a subjectivity-contraction indicator set. It includes scenario-specific rules across home, eldercare, healthcare, industrial, and public-service settings alongside a proposed standardization path.

Summary and content fields are drawn from the document text and are pending editorial review.

Who issued it

Beijing Language and Culture UniversityAcademic
Lead issuer
ZenodoOther
Publisher
Legal forceVoluntary
Under whose authorityInstitutional policy
Judged fromfull text

Who it governs

AI developers and providersRegulatorsCompaniesAI deployers and usersResearchers and academics

AI developers, roboticists, researchers, and standards bodies should read this to implement input-side safety governance and ethical compliance mechanisms in humanoid systems.

Sectors
Cross-sector · humanoid roboticsHealthcare · eldercare and healthcare settingsManufacturing and industry · industrial settings

What the document contains

What's covered

This document covers operational ethics for human-humanoid robot interaction, focusing on input-side intent confirmation, consequence grading, capability protection, and event traceability. It details engineering artifacts, scenario-specific implementation rules for five sectors, and a structured path for standardization.

What's missing

It does not provide binding statutory legislation, final international ISO standards, or formal legal liability allocations.

Key takeaways
  • Existing robotics and AI safety standards predominantly constrain machine output rather than governing how robots should decline high-risk human inputs.
  • The framework introduces five working hypotheses covering intent readability, user request distributions, AI sycophancy, dual-source evidence attribution, and dynamic human subjectivity.
  • Four engineering artifacts are proposed: an A/B/C/D consequence-grading model, a dual-source split-key black box, an adversarial sycophancy test suite, and subjectivity-contraction indicators.
  • The text outlines sector-specific rules for home, eldercare, healthcare, industrial, and public-service applications.
  • A three-stage standardization path is proposed, moving from group standards to national pre-research and an ISO/TC 299 amendment annex.
Principles it states
Predictable behavior does not imply readable intent (Anti-Turing epistemological hypothesis).A structural rift exists between designers' rational user presumption and real human requests (Light-Dark Rift hypothesis).Systems optimized for user satisfaction may exhibit measurable sycophancy amplified by physical actuation.Incident evidence must be organized across human and machine sources without prejudging legal liability.Human subjectivity in long-term cohabitation contracts or expands dynamically.
Outline
· Abstract and Introduction to Input-Side Governance
· Five Working Hypotheses of Neo-Humanist Theory
· Four Core Engineering Artifacts
· Sector-Specific Rules for Home, Eldercare, Healthcare, Industrial, and Public-Service Settings
· Multi-Role Responsibility Allocation Table and Candidate Patent Topics
· Three-Stage Standardization Path
Issued12 August 2026
Length590 words
FormatNot recorded
Scopeglobal

How it scores

Pending review

Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.

Accountability

4/5

Clear responsibility for AI outcomes

We specify sector rules for home, eldercare, healthcare, industrial and public-service settings, a multi-role responsibility allocation table

Description

screening score · 2026-10-24

Fairness

0/5

Non-discrimination and equitable treatment

screening score · 2026-10-24

Human Dignity

4/5

Respect for the inherent worth of persons

a subjectivity-contraction indicator set anchored to established clinical instruments (ADL/IADL, MMSE/MoCA) plus a machine-loggable decision-initiation ratio

Description

screening score · 2026-10-24

Human Oversight

4/5

Humans remain in control of consequential decisions

when a human issues a high-risk request, how should an embodied humanoid robot decline to obey?

Description

screening score · 2026-10-24

Privacy

4/5

Protection of personal data and private life

an event-triggered, dual-source, split-key black box that reconciles incident traceability with privacy

Description

screening score · 2026-10-24

Transparency

4/5

Openness about how AI systems work and are used

event-triggered, dual-source, split-key black box that reconciles incident traceability with privacy

Description

screening score · 2026-10-24

Enforcement

4/5

Consequences and compliance mechanisms with real force

an A/B/C/D consequence-grading model in which irreversible-harm requests are blocked at the capability envelope rather than by refutable software policy

Description

screening score · 2026-10-24

Impact Assessment

3/5

Required assessment of an AI system's risks/impacts before or during use

a measurable sycophancy deviation rate with a four-category adversarial test suite

Description

screening score · 2026-10-24

Topics

humanoid_robots robot_ethics intent_governance safety_framework event_traceability