1,720,972 research outputs found
Instruments, Agents, and Artificial Intelligence: Novel Epistemic Categories of Reliability
Full text linkDeep learning (DL) has become increasingly central to science, primarily due to its capacity to quickly, efficiently, and accurately predict and classify phenomena of scientific interest. This paper seeks to understand the principles that underwrite scientists’ epistemic entitlement to rely on DL in the first place and argues that these principles are philosophically novel. The question of this paper is not whether scientists can be justified in trusting in the reliability of DL. While today's artificial intelligence exhibits characteristics common to both scientific instruments and scientific experts, this paper argues that the familiar epistemic categories that justify belief in the reliability of instruments and experts are distinct, and that belief in the reliability of DL cannot be reduced to either. Understanding what can justify belief in AI reliability represents an occasion and opportunity for exciting, new philosophy of science
Citation counts reinforce the influence of highly cited papers and nudge us towards undervaluing those with fewer.
No full textIn the context of everyday research assessment citation counts are often taken as a simple indicator of the influence of any particular paper. However, all citations are not the same and can be deployed to achieve different ends. Commenting on a recent study of how researchers across 15 academic fields understand the influence of the work cited in their research Eamon Duede shows how citation plays a role both in indicating and shaping the influence of research papers
AI and the Decentering of Disciplinary Creativity
Full text linkThis paper examines the role of artificial intelligence in scientific problem-solving, with a focus on its implications for disciplinary creativity. Drawing on recent work in the philosophy of creativity, I distinguish between creative approaches and creative products, and introduce the concept of disciplinary creativity—the creative application of discipline-specific expertise to a valued problem within that field. Through two cases in mathematics, I show that while computation can extend disciplinary creativity, certain approaches involving AI can serve to displace it. This displacement has the potential to alter (and, perhaps, diminish) the value of scientific pursuit
Wikipedia is significantly amplifying the impact of Open Access publications.
No full textWhen you edit Wikipedia to include a claim, you are required to substantiate that edit by referencing a reliable source. According to a recent study, the single biggest predictor of a journal’s appearance in Wikipedia is its impact factor. One of the exciting findings, writes Eamon Duede, is that it appears Wikipedia editors are putting a premium on open access content. When given a choice between journals of similar impact factors, editors are significantly more likely to select the “open access” option
Deep Learning Opacity in Scientific Discovery
Full text linkPhilosophers have recently focused on critical, epistemological challenges
that arise from the opacity of deep neural networks. One might conclude from
this literature that doing good science with opaque models is exceptionally
challenging, if not impossible. Yet, this is hard to square with the recent
boom in optimism for AI in science alongside a flood of recent scientific
breakthroughs driven by AI methods. In this paper, I argue that the disconnect
between philosophical pessimism and scientific optimism is driven by a failure
to examine how AI is actually used in science. I show that, in order to
understand the epistemic justification for AI-powered breakthroughs,
philosophers must examine the role played by deep learning as part of a wider
process of discovery. The philosophical distinction between the 'context of
discovery' and the 'context of justification' is helpful in this regard. I
demonstrate the importance of attending to this distinction with two cases
drawn from the scientific literature, and show that epistemic opacity need not
diminish AI's capacity to lead scientists to significant and justifiable
breakthroughs.Comment: Forthcoming at "Philosophy of Science" 12 pages, 1 figur
Amplifying the Impact of Open Access: Wikipedia and the Diffusion of Science
No full textTeplitskiy, Misha; Lu, Grace; Duede, Eamon. Amplifying the Impact of Open Access: Wikipedia and the Diffusion of Science. arXiv.org. arXiv:1506.07608. With the rise of Wikipedia as a first-stop source for scientific knowledge, it is important to compare its representation of that knowledge to that of the academic literature. This article approaches such a comparison through academic references made within the worlds 50 largest Wikipedias. Previous studies have raised concerns that Wikipedia e..
Amplifying the Impact of Open Access: Wikipedia and the Diffusion of Science
No full textTeplitskiy, Misha; Lu, Grace; Duede, Eamon. Amplifying the Impact of Open Access: Wikipedia and the Diffusion of Science. arXiv.org. arXiv:1506.07608. With the rise of Wikipedia as a first-stop source for scientific knowledge, it is important to compare its representation of that knowledge to that of the academic literature. This article approaches such a comparison through academic references made within the worlds 50 largest Wikipedias. Previous studies have raised concerns that Wikipedia e..
The Paradox of Collective Certainty in Science
Full text linkWe explore a paradox of collective action and certainty in science wherein
the more scientists research together, the less that work contributes to the
value of their collective certainty. When scientists address similar problems
and share data, methods, and collaborators, their understanding of and trust in
their colleagues' research rises, a quality required for scientific advance.
This increases the positive reinforcement scientists receive for shared beliefs
as they become more dependent on their colleagues' knowledge, interests, and
findings. This collective action increases the potential for scientists to
reside in epistemic ''bubbles'' that limit their capacity to make new
discoveries or have their discoveries generalize. In short, as scientists grow
closer, their experience of scientific validity rises as the likelihood of
genuine replication falls, creating a trade-off between certainty and truth
Apriori Knowledge in an Era of Computational Opacity: The Role of AI in Mathematical Discovery
Full text linkCan we acquire apriori knowledge of mathematical facts from the outputs of computer programs? People like Burge have argued (correctly in our opinion) that, for example, Appel and Haken acquired apriori knowledge of the Four Color Theorem from their computer program insofar as their program simply automated human forms of mathematical reasoning. However, unlike such programs, we argue that the opacity of modern LLMs and DNNs creates obstacles in obtaining apriori mathematical knowledge from them in similar ways. We claim though that if a proof-checker automating human forms of proof-checking is attached to such machines, then we can obtain apriori mathematical knowledge from them after all, even though the original machines are entirely opaque to us and the proofs they output may not, themselves, be human-surveyable
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