Better than they know themselves? Algorithms and subjectivity

• Published in: Critical psychology (Lawrence & Wishart) Vol. 30; no. 4; pp. 394 - 416
• Main Author: Razinsky, Liran
• Format: Journal Article
• Language: English
• Published: London Palgrave Macmillan UK 01.12.2023; Palgrave Macmillan
• Subjects: Algorithms; Big Data; Critics; Human subjects; Knowledge; Original Article; Recommender systems; Social networks; Social Sciences; Subjectivity; Understanding; Self-knowledge; AI; Big data; Algorithms; Subjectivity
• ISSN: 1755-6341; 1755-635X
• DOI: 10.1057/s41286-023-00174-7

The paper explores the widely circulated idea that algorithms will soon be able to know people “better than they know themselves.” I address this idea from two perspectives. First I argue for the particular subjective qualities of experience and self-understanding issuing from our engagement with the world and the constitutive role of our reflexive relation to ourselves. These are not “known” by the algorithms. I then address our fundamental opacity to ourselves and the biased, partial, and limited nature of human self-understanding. Our failure to know ourselves is however essential to our subjectivity and therefore, to know a subject in a perfect way that bypasses these limitations is actually not to know them. Taken together, both directions show that while algorithmic knowledge of humans can be vast, and can outperform their own knowledge, it remains foreign to their subjectivity and cannot be said to be better than self-understanding.