Human-rights-based responsible AI: a harm-centred research lens

A normative framework proposing universal human rights as an explicit value-alignment basis for responsible AI research and civil-society participation, centring who is harmed, which harms they face and possible mitigation.

Workflow level n/a Source Mixed sources Outcome Not empirically evaluated here

Evidence & verification

Source level

Mixed sources
Grounded in a combination of peer-reviewed and non-peer-reviewed sources; see the individual sources.

Outcome verification

Not empirically evaluated here
A documented resource or intended workflow, not a demonstrated academic outcome. No empirical benefit is established by its inclusion.

Sources checked

2026-10-17 (UTC)

Primary scope: Human Rights-Based Approach — arXiv v1 abstract: abstract-only; EAAMO 2022 official conference poster — extracted text: paper-excerpt. Publication classification is separate from substantive read scope; see limitations and entry-audit.json.

Source-checked does not mean independently reproduced. See the Method page for how these labels are assigned.

Sources (2)

Research applicability

A conceptual synthesis for researchers examining the values and harms of AI-assisted interventions. The knowledge-synthesis label is an editorial mapping for this normative reference, not a claim of automated review or legal analysis.

Getting started

Editorial, untested: use the abstract's who/what/how harm questions to structure a research discussion, then read the official poster and full paper before relying on detailed legal or participation arguments.

Maintenance

Sources accessed 2026-10-17; service availability, maintenance and compatibility were not tested.

License

Reuse permissions for paper, data and code require separate assessment; no blanket licence or redistribution assurance.

Workflow & human oversight

Resource-level workflow level intentionally unspecified. Conceptual references and reusable process patterns do not themselves establish an autonomous workflow; researchers retain task-specific interpretation and oversight.

Workflow level is not an evidence score; higher is not better evidence.

Approach

The authors argue for shifting attention from machines and their biases toward humans and risks to their rights. Their conference poster connects the proposal to rights against discrimination, health and access to science, while raising universality and legal-enforceability limitations.

Limitations

ArXiv abstract-only substantive review plus an extracted official conference poster; paper body and visual poster elements were not reviewed. The non-archival EAAMO poster is verified, but archival peer-reviewed publication is not established. Mixed denotes abstract and conference-poster evidence, not a verified peer-review judgment. Universality, cultural relativism and legal enforceability remain issues, not solved properties. This is not legal advice, an operational compliance system or a validated harm-reduction intervention.

Reproducibility

AI-agent primary-source reading and provenance checks only. No software, experiment, hardware, outcome or installation reproduced. Source verification is not outcome verification.

Adoption requirements

Editorial, untested: identify affected communities, relevant rights and local legal context with appropriate domain and civil-society expertise. Participation, legal interpretation and harm measurement need project-specific work; no installation or automatic compliance capability is asserted.

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