https://arxiv.org/abs/2206.00520

close this message
arXiv smileybones icon

Giving Week!

Show your support for Open Science by donating to arXiv during Giving
Week, April 25th-29th.

DONATE
Skip to main content
Cornell University
We gratefully acknowledge support from
the Simons Foundation and member institutions.
 
arxiv logo > cs > arXiv:2206.00520
[                    ]

Help | Advanced Search

[All fields        ]
Search
arXiv logo
Cornell University Logo
[                    ] GO
quick links

  * Login
  * Help Pages
  * About

Computer Science > Artificial Intelligence

arXiv:2206.00520 (cs)
[Submitted on 1 Jun 2022]

Title:Deep Learning Opacity in Scientific Discovery

Authors:Eamon Duede
Download PDF

    Abstract: Philosophers 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.

Comments: 12 pages, 1 figure
          Artificial Intelligence (cs.AI); Computers and Society
Subjects: (cs.CY); Machine Learning (cs.LG); Physics and Society
          (physics.soc-ph)
Cite as:  arXiv:2206.00520 [cs.AI]
          (or arXiv:2206.00520v1 [cs.AI] for this version)
          https://doi.org/10.48550/arXiv.2206.00520
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Eamon Duede [view email]
[v1] Wed, 1 Jun 2022 14:30:49 UTC (357 KB)
Full-text links:

Download:

  * PDF
  * Other formats

[by-sa-4]
Current browse context:
cs.AI
< prev   |   next >
new | recent | 2206
Change to browse by:
cs
cs.CY
cs.LG
physics
physics.soc-ph

References & Citations

  * NASA ADS
  * Google Scholar
  * Semantic Scholar

a export bibtex citation Loading...

Bibtex formatted citation

x
[loading...          ]
Data provided by:

Bookmark

BibSonomy logo Mendeley logo Reddit logo ScienceWISE logo
(*) Bibliographic Tools

Bibliographic and Citation Tools

[ ] Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
[ ] Litmaps Toggle
Litmaps (What is Litmaps?)
[ ] scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
( ) Code & Data

Code and Data Associated with this Article

[ ] arXiv Links to Code Toggle
arXiv Links to Code & Data (What is Links to Code & Data?)
( ) Demos

Demos

[ ] Replicate Toggle
Replicate (What is Replicate?)
( ) Related Papers

Recommenders and Search Tools

[ ] Connected Papers Toggle
Connected Papers (What is Connected Papers?)
[ ] Core recommender toggle
CORE Recommender (What is CORE?)
( ) About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and
share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have
embraced and accepted our values of openness, community, excellence,
and user data privacy. arXiv is committed to these values and only
works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?
Learn more about arXivLabs and how to get involved.

Which authors of this paper are endorsers? | Disable MathJax (What is
MathJax?)

  * About
  * Help

  * Click here to contact arXiv Contact
  * Click here to subscribe Subscribe

  * Copyright
  * Privacy Policy

  * Web Accessibility Assistance
  * arXiv Operational Status
    Get status notifications via email or slack