Machine Learning Cannot Be Theory-Free

Mel Andrews comments on their paper 'The Immortal Science of ML: Machine Learning & the Theory-Free Ideal' and argues that any scientific practice involving machine learning is NOT on a theory-free epistemic footing but is necessarily a theory-laden exercise. Watch the full podcast here: https://youtu.be/I0yil_3dvjs?si=zURvR8Oae-IF0y-1 --------------------------------------- {Podcast} Substack: https://rsampod.substack.com/ Spotify: https://open.spotify.com/show/4ryEqjut4r6SMtfxLdM1Le Anchor: https://anchor.fm/rahul-samaranayake Available on other platforms at https://link.chtbl.com/PDBAf9Zd {Website} https://rahulsam.me/ {Social Media} https://www.instagram.com/name_is_rahul/ https://substack.com/@trsam/ https://x.com/trsam97 https://www.linkedin.com/in/rahul-samaranayake-981a9315b/ {Reference Links} https://www.researchgate.net/publication/371982028_The_Immortal_Science_of_ML_Machine_Learning_the_Theory-Free_Ideal --------------------------------------- If the ideas I discuss in this channel evoke your interest, consider visiting https://theunhappyman.substack.com/ --------------------------------------- Copyright Disclaimer under section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, education and research. Fair use is a use permitted by copyright statutes that might otherwise be infringing. If you are or represent the copyright owner of materials used in this video and have a problem with the use of the related material, please email me at trahulsam@gmail.com, and we can sort it out. Thank you.