Contribution to the handbook
Priority · Assessed 2026-10-27
Comparing possible outcome and procedural benefits against opacity, bias, unequal participation, and other costs bears directly on system choices, including whether to retain, limit, or augment automated decision-making. The packet preserves alternatives and cautions against treating potential benefits as established.
Does this challenge the goal?
In-scope gap
It supports the definition’s request to compare benefits, costs, and tradeoffs while keeping choices open.
The evidence identifies considerations and alternatives but does not supply a common method for weighing them or establish claimed benefits across settings.
Why this category?
- meaningful alternatives and tradeoffs
- consequential risks and limits
- citizen participation and accountability
- testing and evidence limits
The evidence includes source arguments, proposed safeguards, and claims with setting-specific limits. It does not establish overall net benefit, citizen consent, or a settled rule for accepting residual risks.
This is an AI assessment of contribution to the goal, separate from whether the question has an answer.
- Citizens authorize rules; AI acts within their mandate
- Why does the author favor judging both process and results?
- Could automated systems reduce bias while keeping profiling tied to legitimate grounds?
- Can an algorithmic system produce operational benefits even when its wider setting is contested?
- How might bias enter an automated decision system?
- What conditions does the author say decision-making procedures must meet to be legitimate?
- How should concerns about algorithmic systems be weighed against their benefits?
- Why might resisting algorithmic decision systems be difficult?
- What technical limits might prevent people from reviewing automated decisions?
- What benefits and costs should be compared?
- Could algorithmic systems improve how decisions are made, not just their results?
- Does the author favor rejecting these systems or finding ways to keep people involved?
- What could make rules requiring understandable AI systems difficult to adopt?
- What problems with in-person deliberation might the method reduce?
- Why might lower-cost AI facilitation matter for larger public discussions?
- What do online collective discussion systems aim to add to open participation?
- How can a discussion show both differing views and views that cross divisions?
- How does the chapter connect social-media algorithms with human deliberation?
- Weighing fairness against participation
- Bias can enter during design
- Fairness and legitimate grounds
- Resistance has costs
- When systems are favoured for knowledge
- Look for ways to preserve participation
- Rules may face resistance
- Decision procedures may also benefit
- The argument and its elite
- Weigh risks against benefits
- Warehouse algorithms may reduce waste
- Some systems resist review
- Considering process and results
- Possible gains over in-person discussion
- Lower costs could help scale discussion
- Representing intersecting identities
- Encouraging listening online
- Showing views across divisions
- Algorithms to support deliberation
Making and interpreting laws →
Judging situations and resolving disputes →
Authority, accountability and resilience →
Tests, stop conditions and unresolved work →
Read the exploration goalNew evidence is being assessed. Any answer below is provisional; parts whose evidence changed or was withdrawn are withheld.
There is no current reviewed answer to show yet.
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