MétaCan
Menu
← Back to cohort
Record W3103633104 · doi:10.29173/cais1130

AI Governance Systems: Ontological Explorations in the Canadian Context

2020· article· en· W3103633104 on OpenAlexaffvenueabout
Robert Frost

Bibliographic record

VenueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEthics and Social Impacts of AI
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCorporate governanceContext (archaeology)Political scienceHumanitiesSociologyManagementPhilosophyGeographyEconomics

Abstract

fetched live from OpenAlex

AI governance is an area of research and practice which seeks to understand and control “the institutions and contexts in which AI is built and used” (Dafoe, 2018, p. 5). Despite the importance of institutions to AI governance, the influence that institutional dynamics play in the development of systems and strategies of AI governance has not yet been rigorously studied. Moreover, the cognitive ecology and evolutionary potential associated with AI practices are crucial aspects of AI governance systems, yet those factors have largely gone unconsidered in the research and practice of AI governance to date. This project attempts to bridge those gaps in research and practice through a four-phase research process involving a comparative analysis of AI governance strategies, a review and synthesis of the AI governance literature, the development of an ontology of AI governance systems, and an in-depth case study of an AI governance system. La gouvernance de l'IA est un domaine de recherche et de pratique qui cherche à comprendre et à contrôler «les institutions et les contextes dans lesquels l'IA est construite et utilisée» (Dafoe, 2018, p. 5, notre trad.). Malgré l'importance des institutions pour la gouvernance de l'IA, l'influence de la dynamique institutionnelle sur le développement des systèmes et des stratégies de gouvernance de l'IA n'a pas encore été rigoureusement étudiée. De plus, l'écologie cognitive et le potentiel évolutif associés aux pratiques de l'IA sont des aspects cruciaux des systèmes de gouvernance de l'IA, mais ces facteurs ont été largement ignorés dans la recherche et la pratique de la gouvernance de l'IA à ce jour. Ce projet tente de combler ces lacunes dans la recherche et la pratique grâce à un processus de recherche en quatre phases impliquant une analyse comparative des stratégies de gouvernance de l'IA, un examen et une synthèse de la littérature sur la gouvernance de l'IA, le développement d'une ontologie des systèmes de gouvernance de l'IA, et une étude de cas approfondie d'un système de gouvernance de l'IA.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.774
Threshold uncertainty score0.898

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.018
Science and technology studies0.0230.021
Scholarly communication0.0130.007
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0110.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.080
GPT teacher head0.313
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes3
Has abstractyes

Explore more

Same venueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI→Same topicEthics and Social Impacts of AI→French-language works237,207→