Avoiding the iron cage of AI technocracy based on the principle of reversibility of harm

Digital Public Infrastructure comprises foundational layers for digital identity, payments, and data exchange, and constitutes a critical substrate on which Artificial Intelligence (AI) driven public and private services operate. AI integration offers considerable efficiency advantages within DPI. At the same time, AI can be rigid and impose an “iron-cage” on society with its members unable to opt out. Mistakes can result in denial of critical services and other issues. Matters are made even more complicated by ownership structures that span sovereign boundaries such that accountability is blurred. To attenuate the hazards associated with AI integration with DPI, this study introduces a harm-based principle to evaluate AI integration: the forgiveness factor. The forgiveness factor requires AI integration decisions to be considered from the lens of harm they might cause as well as considerations of reversibility, which is then linked to establishing safeguards, requiring audits, and mechanisms to challenge AI driven bureaucratic decisions, to ensure human accountability. The approach notes that questions of sovereignty and assignment of property rights play a key role in governance design. Attention is also paid to power asymmetries between individuals and governments (businesses) as well as variance in institutions across regions with a proposal to establish an oversight agency. Signatories to the agency’s’ charter enables transactions between actors in different regions including hosting data centers and other infrastructure related entities, thus providing an incentive to maintain collectively established standards.

Publication Details

Journal
Pure (University of Bath)
Published
2025-09-09
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Avoiding the iron cage of AI technocracy based on the principle of reversibility of harm

Pure (University of Bath)
Ethics and Social Impacts of AI
article

Avoiding the iron cage of AI technocracy based on the principle of reversibility of harm

article en

Abstract

Digital Public Infrastructure comprises foundational layers for digital identity, payments, and data exchange, and constitutes a critical substrate on which Artificial Intelligence (AI) driven public and private services operate. AI integration offers considerable efficiency advantages within DPI. At the same time, AI can be rigid and impose an “iron-cage” on society with its members unable to opt out. Mistakes can result in denial of critical services and other issues. Matters are made even more complicated by ownership structures that span sovereign boundaries such that accountability is blurred. To attenuate the hazards associated with AI integration with DPI, this study introduces a harm-based principle to evaluate AI integration: the forgiveness factor. The forgiveness factor requires AI integration decisions to be considered from the lens of harm they might cause as well as considerations of reversibility, which is then linked to establishing safeguards, requiring audits, and mechanisms to challenge AI driven bureaucratic decisions, to ensure human accountability. The approach notes that questions of sovereignty and assignment of property rights play a key role in governance design. Attention is also paid to power asymmetries between individuals and governments (businesses) as well as variance in institutions across regions with a proposal to establish an oversight agency. Signatories to the agency’s’ charter enables transactions between actors in different regions including hosting data centers and other infrastructure related entities, thus providing an incentive to maintain collectively established standards.

Pure (University of Bath)
Industry, innovation and infrastructure
Openalex Percentile: Top 31%
Ethics and Social Impacts of AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.