Bibliographic record
Abstract
In July of 2019, at the Summer Institute on AI and Society in Edmonton, Canada (co-sponsored by CIFAR and the AI Pulse Project of UCLA Law), scholars from across disciplines came together in an intensive workshop. For the second half of the workshop, the cohort split into smaller working groups to delve into specific topics related to AI and Society.I proposed deeper exploration on the topic of “agency,” which is defined differently across domains and cultures, and relates to many of the topics of discussion in AI ethics, including responsibility and accountability. It is also the subject of an ongoing art and research project I’m producing. As a group, we looked at definitions of agency across fields, found paradoxes and incongruities, shared our own questions, and produced a visual map of the conceptual space. We decided that our disparate perspectives were better articulated through a collection of short written pieces, presented as a set, rather than a singular essay on the topic. The outputs of this work are shared here.This set of essays, many of which are framed as provocations, suggests that there remain many open questions, and inconsistent assumptions on the topic. Many of the writings include more questions than answers, encouraging readers to revisit their own beliefs about agency. As we further develop AI systems, and refer to humans and non-humans as “agents”– we will benefit from a better understanding of what we mean when we call something an “agent” or claim that an action involves “agency.” This work is under development and many of us will continue to explore this in our ongoing AI work. – Sarah Newman, Project Lead, August 2019
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.098 | 0.035 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".