Data & AI Stewardship

Collection, consent, provenance, access, model use, bias, privacy, security, correction, and responsible retirement.

Why it matters

Collection, consent, provenance, access, model use, bias, privacy, security, correction, and responsible retirement.

Outside-in systems

Examine fiduciary law, ownership, governance, capital allocation, philanthropy, procurement, research, data systems, supply chains, disclosure, regulation, labor, and community-impact processes. For this path, track data lineage, model and access governance, and privacy and incident reporting.

Inside-out differences

Compare institutional type, ownership, beneficiary, stakeholder, geography, time horizon, donor or investor intent, employee voice, community participation, reviewer independence, and differing theories of responsibility. Use those differences to test the scope of Data & AI Stewardship; do not treat any group as monolithic.

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