The steep cost of capture

157 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

GPT-NeoX-20B: An Open-Source Autoregressive Language Model (2022, background): “nology (Sadowski et al., 2021; Whittaker, 2021) that makes meaningful independent evaluation impossible.”
The Gradient of Generative AI Release: Methods and Considerations (2023, background): “are limited to people and resources working towards that company’s interests [101].”
Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes (2022, background): “While harm does originate from a concentration of AI power in companies [85], we show that significant and understudied harms originate from differing practices of transparency and accountability in the open source community.”
Artificial intelligence and illusions of understanding in scientific research (2024, unknown): “Assessing the credibility of these models is challenging because industry-developed models are often considered to be trade secrets and are thus less transparent and reproducible than models developed in academia 177,178 .”
Confronting Power and Corporate Capture at the FAccT Conference (2022, background): “Previous work has raised similar concerns about tech industry funding relationships [1, 50, 65].”
AI Art and its Impact on Artists (2023, background): “In her paper titled The Steep Cost of Capture, whistleblower Meredith Whittaker writes about the level to which academic AI research has been captured by corporate interests [132].”