FID-038
Non-Calculability, Forgiveness, and Predictive Profiling
How should AI systems represent human change when they classify, score, rank, or predict people in contexts involving pastoral care, education, safeguarding, volunteer screening, hiring, discipline, membership, donor engagement, or community support?
Why this matters
The question behind the brief.
Predictive systems can identify people with past behavior, inferred traits, productivity, donor value, risk categories, or reputational labels. Christian accounts of repentance, forgiveness, conversion, vocation, accountability, and restoration resist reducing a person to a score or past pattern. Faith-facing AI needs ways to preserve both prudence and the possibility of real change.
Work advancing this call
From open question to cumulative evidence.
This directory links Fide AI research to the call it addresses. Relevant work from other organizations is listed separately and added through manual review.
No Fide AI work is linked yet.
This call remains open for research, implementation, review, or partnership.
External work is not presented as Fide AI research or endorsement. Each item must include a specific explanation of how it advances this call.
Suggest related work ↗Metadata
How to place this call.
Ways to help
Move this from question to evidence.
Draft scenarios involving discipline, safeguarding, education, hiring, and pastoral care.
Review rubrics for mercy, accountability, due process, and contestability.
Connect this work to algorithmic fairness and risk-assessment research.
Help design safeguards for sensitive institutional pilots.
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