Contribution to the handbook
Supporting · Assessed 2026-10-27
This can inform scrutiny of data dependence, monitoring, and unequal control of resources. The question is descriptive background and does not itself specify a governance design or tested mitigation.
Does this challenge the goal?
In-scope gap
It supplies background on data systems relevant to governance risks, without contesting the handbook’s goals.
The evidence describes large datasets and monitoring networks as features of some systems but does not measure their prevalence or establish their effects.
Why this category?
- relevant technical and institutional context
- data and monitoring risk
The supplied interpretations lack measures of prevalence, differences among systems, and effects.
This is an AI assessment of contribution to the goal, separate from whether the question has an answer.
- What data infrastructure does the author associate with modern data mining?
- Some AI systems may exceed human understanding
- Systems can learn rules from data
- Algorithms may help use large datasets
- Unequal access to data
- Complex systems are hard to understand
- Monitoring may become more widespread
- Monitoring can be hard to notice
- Large datasets and monitoring
Authority, accountability and resilience →
Read the exploration goalDescribe the datasets and data-gathering infrastructure the author associates with modern data mining.
What we can say so far
The author associates modern data mining with very large datasets gathered through expanding networks of data-monitoring technologies. He also describes machine-learning systems as using large datasets to develop rules rather than relying only on rules specified in advance by people.
What remains open
- The evidence does not quantify prevalence or differences among systems.
Conditions and limits
- This account is based on the readable author manuscript from an accessible mirror, not the publisher typeset version.
- The author’s description does not establish how widespread this infrastructure is or how systems vary; the passage offers no supporting study.
Evidence and related ideas
Supporting evidence, qualifications and context play different roles in this answer.
supports
Large datasets and monitoring Many modern data-mining systems use very large datasets gathered through expanding networks of data-monitoring technologies.
Directly describes very large datasets gathered through expanding networks of data-monitoring technologies.
The Threat of Algocracy: Reality, Resistance and Accommodation
Read full source: The Threat of Algocracy: Reality, Resistance and Accommodation (opens in a new tab) ↗Read source discussion: The Threat of Algocracy: Reality, Resistance and Accommodation →
supports
Systems can learn rules from data The paper says that machine-learning systems can use large datasets to develop rules rather than relying only on rules chosen in advance by people. It presents this approach as one response to the growth in the amount of data that needs to be examined.
Adds that machine-learning systems can use large datasets to develop rules.
The Threat of Algocracy: Reality, Resistance and Accommodation
Read full source: The Threat of Algocracy: Reality, Resistance and Accommodation (opens in a new tab) ↗