A Critical Examination of the Ethics of AI-Mediated Peer Review
- URL: http://arxiv.org/abs/2309.12356v1
- Date: Sat, 2 Sep 2023 18:14:10 GMT
- Title: A Critical Examination of the Ethics of AI-Mediated Peer Review
- Authors: Laurie A. Schintler, Connie L. McNeely, James Witte
- Abstract summary: Recent advancements in artificial intelligence (AI) systems offer promise and peril for scholarly peer review.
Human peer review systems are also fraught with related problems, such as biases, abuses, and a lack of transparency.
The legitimacy of AI-driven peer review hinges on the alignment with the scientific ethos.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advancements in artificial intelligence (AI) systems, including large
language models like ChatGPT, offer promise and peril for scholarly peer
review. On the one hand, AI can enhance efficiency by addressing issues like
long publication delays. On the other hand, it brings ethical and social
concerns that could compromise the integrity of the peer review process and
outcomes. However, human peer review systems are also fraught with related
problems, such as biases, abuses, and a lack of transparency, which already
diminish credibility. While there is increasing attention to the use of AI in
peer review, discussions revolve mainly around plagiarism and authorship in
academic journal publishing, ignoring the broader epistemic, social, cultural,
and societal epistemic in which peer review is positioned. The legitimacy of
AI-driven peer review hinges on the alignment with the scientific ethos,
encompassing moral and epistemic norms that define appropriate conduct in the
scholarly community. In this regard, there is a "norm-counternorm continuum,"
where the acceptability of AI in peer review is shaped by institutional logics,
ethical practices, and internal regulatory mechanisms. The discussion here
emphasizes the need to critically assess the legitimacy of AI-driven peer
review, addressing the benefits and downsides relative to the broader
epistemic, social, ethical, and regulatory factors that sculpt its
implementation and impact.
Related papers
- Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems [2.444630714797783]
We review and discuss the intricacies of AI biases, definitions, methods of detection and mitigation, and metrics for evaluating bias.
We also discuss open challenges with regard to the trustworthiness and widespread application of AI across diverse domains of human-centric decision making.
arXiv Detail & Related papers (2024-08-28T06:04:25Z) - Towards Responsible AI in Banking: Addressing Bias for Fair
Decision-Making [69.44075077934914]
"Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture.
This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias.
In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
arXiv Detail & Related papers (2024-01-13T14:07:09Z) - Factoring the Matrix of Domination: A Critical Review and Reimagination
of Intersectionality in AI Fairness [55.037030060643126]
Intersectionality is a critical framework that allows us to examine how social inequalities persist.
We argue that adopting intersectionality as an analytical framework is pivotal to effectively operationalizing fairness.
arXiv Detail & Related papers (2023-03-16T21:02:09Z) - A Systematic Literature Review of Human-Centered, Ethical, and
Responsible AI [12.456385305888341]
We review and analyze 164 research papers from leading conferences in ethical, social, and human factors of AI.
We find that the current emphasis on governance and fairness in AI research may not adequately address the potential unforeseen and unknown implications of AI.
arXiv Detail & Related papers (2023-02-10T14:47:33Z) - The Role of AI in Drug Discovery: Challenges, Opportunities, and
Strategies [97.5153823429076]
The benefits, challenges and drawbacks of AI in this field are reviewed.
The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
arXiv Detail & Related papers (2022-12-08T23:23:39Z) - Fairness in Agreement With European Values: An Interdisciplinary
Perspective on AI Regulation [61.77881142275982]
This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them.
We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives.
We identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.
arXiv Detail & Related papers (2022-06-08T12:32:08Z) - Metaethical Perspectives on 'Benchmarking' AI Ethics [81.65697003067841]
Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research.
An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system.
We argue that it makes more sense to talk about 'values' rather than 'ethics' when considering the possible actions of present and future AI systems.
arXiv Detail & Related papers (2022-04-11T14:36:39Z) - Automated scholarly paper review: Concepts, technologies, and challenges [5.431798850623952]
Recent years have seen the application of artificial intelligence (AI) in assisting the peer review process.
With the involvement of humans, such limitations remain inevitable.
arXiv Detail & Related papers (2021-11-15T04:44:57Z) - Ethics of AI: A Systematic Literature Review of Principles and
Challenges [3.7129018407842445]
Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles.
Lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI.
arXiv Detail & Related papers (2021-09-12T15:33:43Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - Descriptive AI Ethics: Collecting and Understanding the Public Opinion [10.26464021472619]
This work proposes a mixed AI ethics model that allows normative and descriptive research to complement each other.
We discuss its implications on bridging the gap between optimistic and pessimistic views towards AI systems' deployment.
arXiv Detail & Related papers (2021-01-15T03:46:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.