The Data Fix with Dr. Mél Hogan

I invited Lee Vinsel to discuss with me a post he wrote from a workshop on "Politics of Controlling Powerful Technologies". In this episode we discuss how futures are (imagined to be) predicted through data modelling and crunching numbers, and how various alternatives to these statistical imaginaries also come short of knowing what awaits us. Can we stand to not know? And if we don't know what the future holds, how do we plan politically? Recorded April 19. Released June 10, 2024. How to Be a Better Reactionary: Time and Knowledge in Technology Regulationhttps://sts-news.medium.com/how-to-be-a-better-reactionary-1630b5098fbc

In this episode, Lisa Messeri and M. J. Crockett discuss how scientists are in danger of overlooking AI tools’ limitations, and how science is made stronger by questioning its obsession with objectivity. Recorded April 18, 2024. Released May 27, 2024.Artificial intelligence and illusions of understanding in scientific researchLisa Messeri & M. J. Crockett  Nature volume 627, pages49–58 (2024)Cite this articlehttps://www.nature.com/articles/s41586-024-07146-0

In this episode, Shaolei Ren and I discuss the relationship between water and generative AI. We delve into what happens to water in the (very thirsty) data center, what it's used for, and how much fresh water the AI revolution will ask of the planet in the future, and at what costs. Big Tech doesn't yet disclose its water withdrawal or consumption, so researcher like Shaolei Ren take up the work and propose solutions for a more sustainable future for AI. Recorded April 19, 2024. Released May 13, 2024.

With Catherine Stinson and Sophie Vlaad, we discuss what diversity means in the context of AI -- its applications, conceptualizations, teams, institutions, networks, members, and ideals. As they ask in a recent article, "diversity" is often proposed as a solution to ethical problems in artificial intelligence (AI), but what exactly is meant by "diversity" and how it can it solve those problems? Recorded March 22, 2024. Released April 22, 2024.A feeling for the algorithm: Diversity, expertise, and artificial intelligenceStinson, C., & Vlaad, S. (2024). A feeling for the algorithm: Diversity, expertise, and artificial intelligence. Big Data & Society, 11(1). https://doi.org/10.1177/20539517231224247

Listen to my conversation with Matthew Archer, author of Unsustainable: Measurement, Reporting, and the Limits of Corporate Sustainability. In his beautifully written book, Matthew makes a case for being highly skeptical of corporate sustainability initiatives, especially as they've become increasingly grounded in metrics of all kinds that measure just and exactly what the companies themselves determine to be worthy of measuring. Framing sustainability as a technical issue has been and continues to be a failure, and so we ask: what it might mean to take this criticism seriously? Recorded Feb 2, 2024. Released Apr 8, 2024.Unsustainable: Measurement, Reporting, and the Limits of Corporate Sustainability (Feb 2024, Published by NYU Press)https://nyupress.org/9781479822027/unsustainable/

Together, Sireesh Gururaja, Amanda Bertsch and Clara Na explain the paradigm shifts in Natural Language Processing that they've noticed themselves, observed in the community, and documented through a series of interviews with NLP researchers. They share their hopes for the NLP field -- as less focused on benchmarks, and as more self-reflexive and ethically-driven -- moving forward. Recorded Jan 19, 2024. Released March 25, 2024. To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processingby Sireesh Gururaja, Amanda Bertsch, Clara Na, David Gray Widder, Emma Strubellhttps://arxiv.org/abs/2310.07715