Chatbots and Zero Sales Resistance
This addresses the problem of unsustainable and manipulative practices in ML for researchers and practitioners, but it is incremental as it builds on existing critiques without new empirical results.
The paper argues that increasing model weights in large-scale machine learning is unsustainable and leads to manipulative strategies, advocating for a shift towards more insight with fewer weights to prioritize science over business interests.
It is argued that the pursuit of an ever increasing number of weights in large-scale machine learning applications, besides being energetically unsustainable, is also conducive to manipulative strategies whereby Science is easily served as a strawman for economic and financial power. If machine learning is meant to serve science ahead of vested business interests, a paradigm shift is needed: from more weights and little insight to more insight and less weights.