ReviewPatterns (New York, N.Y.)2022
Obligations to assess: Recent trends in AI accountability regulations.
Review in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed, 21 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Policymakers are increasingly turning toward assessments of social, economic, and ethical impacts as a governance model for automated decision systems in sensitive or regulated domains. In both the United States and the European Union, recently proposed legislation would require developers to assess the impacts of their systems for individuals, communities, and society, a notable step beyond the technical assessments that are familiar to the industry. This paper analyzes four examples of such legislation in order to illustrate how AI regulations are moving toward using accountability documentation to address common AI accountability concerns: identifying and documenting harms, public transparency, and anti-discrimination rules. We then offer some insights into how designers of automated decisions systems might prepare for and respond to such rules.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.