Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 394-416 |
| Number of pages | 23 |
| Journal | Subjectivity |
| Volume | 30 |
| Issue number | 4 |
| DOIs | |
| State | Published - Dec 2023 |
Bibliographical note
Publisher Copyright:© 2023, The Author(s), under exclusive licence to Springer Nature Limited.
Funding
I wish to thank Yoav Ronel, Omri Blum, Shira Shemi and Asaf Hazan for brainstorming and for painful, helpful feedback, and Mark Joseph, Yaron Wolf, Zohar Kaufman, Robert Rosenberger, Arnon Keren, Dror Yinon and most of all Hili Razinsky for helping push ideas further. This paper is part of a research project funded by the Israel Science Foundation (no. 1426/21).
| Funders | Funder number |
|---|---|
| Israel Science Foundation | 1426/21 |
Keywords
- AI
- Algorithms
- Big data
- Self-knowledge
- Subjectivity