Reliabilism

Reliabilism is a theory about what makes a belief trustworthy or count as knowledge. Instead of focusing on whether a person can explain their reasons or evidence for believing something, reliabilism asks whether the way they formed the belief is usually reliable.[1] In other words, if the process used to form a belief generally leads to the truth most of the time, then the belief is considered justified or even counts as knowledge. For example, if someone sees a dog in a park and correctly believes it is a dog, their belief is reliable as long as their eyesight usually works well and helps them identify things accurately.[2] This theory became well known in the 1970s thanks to philosopher Alvin Goldman. He introduced reliabilism as a way to solve the Gettier problem, which showed that having a belief that is both true and well-supported might still not count as real knowledge if it turns out to be correct only by accident.[3][4] Reliabilism changes the focus from what is going on inside the person’s mind (like reasons or explanations) to how trustworthy the method of forming the belief is. The more often a method leads to the truth, the more reliable it is.[5]

There are different kinds of reliabilism. Process reliabilism looks at whether a belief was formed through a reliable method. For example, using your eyes in daylight is a reliable way to tell if a tree is in front of you.[1] On the other hand, truth-tracking reliabilism says that not only should the belief be true, but if it were false, the person would not believe it. This helps explain why some lucky guesses do not count as knowledge, because the person would still believe the same thing even if it were wrong.[6] One of the reasons reliabilism is popular is because it fits well with science and psychology. Scientists can study how accurate things like memory, sight, or tools like thermometers are, and use statistics to measure how often these methods give true results.[7] This makes reliabilism useful in real life, such as in court cases (where we want reliable witnesses), in education (where we want students to learn through good methods), or in technology (where we want computers and AI to use reliable data).[8]

However, reliabilism is not perfect. One challenge is called the generality problem. This happens when we try to describe the belief-forming process and are not sure how specific to be. For example, is someone using “vision” to spot a red barn, or are they using “vision while distracted in the fog”? Depending on how we describe it, the method might seem more or less reliable.[9] Another problem comes from clairvoyant examples. Imagine a person who somehow knows the president is in New York, and they are right, but they have no idea how they know or whether they should trust that feeling. Even if their method is weirdly correct, it does not feel like they really know the truth. This raises the question: is being right enough, or do people need to know that their method is trustworthy?[10]

To improve on basic reliabilism, some thinkers created virtue reliabilism. This version says that people should also have intellectual virtues, like being curious, careful, or fair-minded, that help them form reliable beliefs. Instead of just relying on lucky guesses or accident, knowledge should come from good habits of thinking and learning.[11][12] Reliabilist ideas show up in many parts of everyday life. For example, we trust things like weather forecasts, and online information not because we fully understand how they work, but because they usually turn out to be right. The same goes for trusting teachers, experts, and scientific tools, if they have a track record of being reliable, we believe what they tell us. Even though there are still debates and tricky cases, reliabilism remains one of the most important ideas in philosophy about how we come to know things.[13]

References

  1. ↑ 1.0 1.1 Goldman, Alvin I. (1979). "What is Justified Belief?". philpapers.org. Retrieved 2025-07-31.
  2. ↑ Turri, John; Alfano, Mark; Greco, John (2021), Zalta, Edward N. (ed.), "Virtue Epistemology", The Stanford Encyclopedia of Philosophy (Winter 2021 ed.), Metaphysics Research Lab, Stanford University, retrieved 2025-07-31
  3. ↑ Gettier, E. L. (1963-06-01). "Is Justified True Belief Knowledge?". Analysis. 23 (6): 121–123. doi:10.1093/analys/23.6.121. ISSN 0003-2638.
  4. ↑ Goldman, Alvin I. (1976-11-18). "Discrimination and Perceptual Knowledge". The Journal of Philosophy. 73 (20): 771. doi:10.2307/2025679.
  5. ↑ Hatfield, Gary; Goldman, Alvin I. (1989). "Epistemology and Cognition". The Philosophical Review. 98 (3): 386. doi:10.2307/2185025.
  6. ↑ Nozick, Robert (1981). Philosophical explanations. Cambridge, Mass: Belknap Press of Harvard Univ. Press. ISBN 978-0-674-66479-1.
  7. ↑ Pessin, Andrew; Goldman, Alvin (1994). "Liaisons: Philosophy Meets the Cognitive and Social Sciences". The Philosophical Quarterly. 44 (175): 255. doi:10.2307/2219749.
  8. ↑ Pritchard, Duncan; Whittington, Lee John (2015). The philosophy of luck. Chichester: Wiley-Blackwell. ISBN 978-1-119-03057-7.
  9. ↑ Conee, E.; Feldman, R. (1998). "The generality problem for reliabilism". Philosophical Studies. 89 (1): 1–29. doi:10.1023/a:1004243308503. ISSN 0031-8116.
  10. ↑ Bonjour, Laurence; Philosophy Documentation Center (1980). "Externalist Theories of Empirical Knowledge". Midwest Studies in Philosophy. 5: 53–73. doi:10.1111/j.1475-4975.1980.tb00396.x. ISSN 0363-6550.
  11. ↑ Greco, John (2010). Achieving knowledge: a virtue-theoretic account of epistemic normativity. Cambridge ; New York: Cambridge University Press. ISBN 978-0-521-19391-7.
  12. ↑ Battaly, H. (2009-04-01). "A Virtue Epistemology: Apt Belief and Reflective Knowledge, Volume I * By ERNEST SOSA". Analysis. 69 (2): 382–385. doi:10.1093/analys/anp035. ISSN 0003-2638.
  13. ↑ Fricker, Miranda (2011). Epistemic injustice: power and the ethics of knowing (Repr ed.). Oxford: Oxford University Press. ISBN 978-0-19-957052-2.
This article is issued from Wikipedia. The text is available under Creative Commons Attribution-Share Alike 4.0 unless otherwise noted. Additional terms may apply for the media files.