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Exploring Evidence and AI Influence
The chapter explores the concept of evidence in the context of AI and disinformation, discussing how different presentations can polarize individuals and how AIs manipulate evidence to influence beliefs. It also addresses combatting misinformation and the challenge of distinguishing true information from misleading content.
Episode 131
I spoke with Professor Kevin Dorst about:
* Subjective Bayesianism and epistemology foundations
* What happens when you’re uncertain about your evidence
* Why it’s rational for people to polarize on political matters
Enjoy—and let me know what you think!
Kevin is an Associate Professor in the Department of Linguistics and Philosophy at MIT. He works at the border between philosophy and social science, focusing on rationality.
Find me on Twitter for updates on new episodes, and reach me at editor@thegradient.pub for feedback, ideas, guest suggestions.
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Outline:
* (00:00) Intro
* (01:15) When do Bayesians need theorems?
* (05:52) Foundations of epistemology, metaethics, formal models, error theory
* (09:35) Extreme views and error theory, arguing for/against opposing positions
* (13:35) Changing focuses in philosophy — pragmatic pressures
* (19:00) Kevin’s goals through his research and work
* (25:10) Structural factors in coming to certain (political) beliefs
* (30:30) Acknowledging limited resources, heuristics, imperfect rationality
* (32:51) Hindsight Bias is Not a Bias
* (33:30) The argument
* (35:15) On eating cereal and symmetric properties of evidence
* (39:45) Colloquial notions of hindsight bias, time and evidential support
* (42:45) An example
* (48:02) Higher-order uncertainty
* (48:30) Explicitly modeling higher-order uncertainty
* (52:50) Another example (spoons)
* (54:55) Game theory, iterated knowledge, even higher order uncertainty
* (58:00) Uncertainty and philosophy of mind
* (1:01:20) Higher-order evidence about reliability and rationality
* (1:06:45) Being Rational and Being Wrong
* (1:09:00) Setup on calibration and overconfidence
* (1:12:30) The need for average rational credence — normative judgments about confidence and realism/anti-realism
* (1:15:25) Quasi-realism about average rational credence?
* (1:19:00) Classic epistemological paradoxes/problems — lottery paradox, epistemic luck
* (1:25:05) Deference in rational belief formation, uniqueness and permissivism
* (1:39:50) Rational Polarization
* (1:40:00) Setup
* (1:37:05) Epistemic nihilism, expanded confidence akrasia
* (1:40:55) Ambiguous evidence and confidence akrasia
* (1:46:25) Ambiguity in understanding and notions of rational belief
* (1:50:00) Claims about rational sensitivity — what stories we can tell given evidence
* (1:54:00) Evidence vs presentation of evidence
* (2:01:20) ChatGPT and the case for human irrationality
* (2:02:00) Is ChatGPT replicating human biases?
* (2:05:15) Simple instruction tuning and an alternate story
* (2:10:22) Kevin’s aspirations with his work
* (2:15:13) Outro
Links:
* Professor Dorst’s homepage and Twitter
* Papers
* Hedden: Hindsight bias is not a bias
* Higher-order evidence + (Almost) all evidence is higher-order evidence
* Being Rational and Being Wrong
* ChatGPT and human irrationality
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