| >> I have to believe it most likely stems from a lack of basic understanding and competence on the authors part. That is unlikely, given that one of the authors is Timint Gebru. I'm quoting below select passages from her wikipedia page indicating her background: In 2001, Gebru was accepted at Stanford University.[2][5] There she earned her Bachelor of Science and Master of Science degrees in electrical engineering[8] and her PhD in computer vision[9] in 2017.[10] Gebru was advised during her PhD program by Fei-Fei Li.[10] Gebru presented her doctoral research at the 2017 LDV Capital Vision Summit competition, where computer vision scientists present their work to members of industry and venture capitalists. Gebru won the competition, starting a series of collaborations with other entrepreneurs and investors.[11][12] Gebru joined Apple as an intern while at Stanford, working in their hardware division making circuitry for audio components, and was offered a full-time position the following year. Of her work as an audio engineer, her manager told Wired she was "fearless," and well-liked by her colleagues https://en.wikipedia.org/wiki/Timnit_Gebru |
Regardless, my point still stands, they completely ignore (willingly or ignorantly) that human labeled data is not intrinsic to ML or even the algorithms themselves and in all likelihood is a small minority of datasets used by modern ML applications. To then apply that critique generally to ML shows ignorance and a misunderstanding of the ecosystem.