Contribution to the handbook
Priority · Assessed 2026-10-27
The evidence identifies a consequential tradeoff: resistance may be difficult as systems become common and less noticeable, while removing them may forgo possible practical or procedural benefits. It also raises meaningful alternatives, including review and accommodation, and limits on meaningful human participation. These concerns directly affect whether and how governance functions should rely on algorithms.
Does this challenge the goal?
In-scope gap
The question explores practical barriers to resisting algorithmic systems, including their prevalence, low visibility and possible benefits. These are implementation and alternatives questions within the stated goal, not a challenge to it.
The source presents these as arguments and possible barriers, not measured findings about every public-sector setting.
Why this category?
- Meaningful alternatives to use or resistance
- Consequential risks and limits to participation and understanding
- Practical dependencies and possible implementation barriers
The source does not quantify the prevalence, benefits, or barriers, and it does not establish that resistance is impossible or that the proposed responses work.
This is an AI assessment of contribution to the goal, separate from whether the question has an answer.
Authority, accountability and resilience →
Implementation and transition →
Tests, stop conditions and unresolved work →
Read the exploration goalExplain barriers to resisting their use, including their growing prevalence or unobtrusiveness and the costs of forgoing their benefits.
What we can say so far
Danaher’s account gives two reasons resisting algorithmic decision systems may be difficult: they are becoming more common and can be unobtrusive, so people may rely on them without noticing their role; and stopping their use can mean giving up practical and procedural benefits. He also argues that preserving meaningful human involvement may be difficult where systems are opaque or interconnected, and that interpretability requirements may reduce some benefits or meet resistance from organizations that benefit from the systems.
What remains open
- No quantified evidence is supplied about how quickly use is growing, how often systems go unnoticed, or how strong these barriers are.
- The material does not compare the benefits and costs of resistance across specific settings.
Conditions and limits
- The account is based on an AI interpretation of the author manuscript, not independent empirical evidence.
- The source says the trend and benefits are not quantified and does not identify which benefits justify continued use in particular settings.
- Interpretability requirements and organizational resistance are presented as reasons for doubt, not measured findings or proof that resistance cannot succeed.
Objections to consider
- The possibility of participatory, understandable systems qualifies the claim that resistance-related difficulties are unavoidable. The answer presents the author’s position as difficulty in practice, not impossibility.
Evidence and related ideas
Supporting evidence, qualifications and context play different roles in this answer.
supports
Resistance has costs The author argues that simply stopping the use of algorithmic decision systems may be difficult or undesirable because these systems are becoming more common and less noticeable, while also offering practical and procedural benefits.
Directly supports both required points: systems are becoming more common and less noticeable, and stopping use may mean forgoing practical and procedural benefits.
The Threat of Algocracy: Reality, Resistance and Accommodation
Read full source: The Threat of Algocracy: Reality, Resistance and Accommodation (opens in a new tab) ↗