SLIDES Public Controversies and the Future of AI
Bibliographic record
Abstract
Public Controversies and the Future of AI. Lecture at Milieux Institute, Concordia University, Montreal, September 22, 2020. These are the slides of a lecture I have given (remotely) at the Lecture Series "AI Governance and Governmentality" at Milieux Institue, Concordia University, kindly hosted by Fenwick McKelvey. https://milieux.concordia.ca/event/christian-katzenbach-public-controversies-and-the-future-of-ai/ Here's the announcement in place of an abstract: "Facial recognition, digital contact tracing and content moderation have all been major controversies involving AI in 2020. How do these controversies shape the global governance of AI? A leading global scholar on platform and AI governance, Dr. Katzenbach will introduce a framework for the contested, informal governances processes of AI unfolding across research, policy and media in Canada, France, the UK, and Germany. These controversies shape understanding of what kind of AI comes into being, which problems and challenges are to be addressed, and our expertise to shape its future developments for the public good. As scandal and outrage give way to public debate and regulation, Dr. Katzenbach will outline a way to understand a dominant concern for the future of media, social and technology policy. Dr. Katzenbach is a Senior Researcher at the Alexander von Humboldt Institute for Internet and Society (Berlin, Germany). He directs the interdisciplinary research program “The Evolving Digital Society”. He is Chair of the Section Digital Communication of the German Association for Media and Communication ResearchIn the past and has acted as interim professor for communication policy and media economics at the Institute for Media and Communication Research at Freie Universität Berlin. His research addresses the intersection of technology, communication, and governance. He is a co-initiator of the open access journal Internet Policy Review and co-editor of the open access book series Digital Communication Research."
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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; both teacher heads agree on what is shown here.
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".