Integrating Artificial Intelligence in Scientific Practice: Explicable AI as an Interface

Ratti, Emanuele

doi:10.1007/s13347-022-00558-8

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Such a research question, which is developed along the lines of three case studies, is instrumentally used to display the effectiveness of our approach. In this process, we treat the AI model as a helping hand or research companion-in line with similar approaches in medical AI (Klauschen et al, 2018;Ratti, 2022) and cyber analysis (Holder & Wang, 2021)-that provides suggestions and reveals interesting data correlations that might have been overlooked by the domain expert.…”

Section: Introductionmentioning

confidence: 99%

Explainability and transparency in the realm of digital humanities: toward a historian XAI

El-Hajj,

Eberle,

Merklein

et al. 2023

Int J Digit Humanities

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The recent advancements in the field of Artificial Intelligence (AI) translated to an increased adoption of AI technology in the humanities, which is often challenged by the limited amount of annotated data, as well as its heterogeneity. Despite the scarcity of data it has become common practice to design increasingly complex AI models, usually at the expense of human readability, explainability, and trust. This in turn has led to an increased need for tools to help humanities scholars better explain and validate their models as well as their hypotheses. In this paper, we discuss the importance of employing Explainable AI (XAI) methods within the humanities to gain insights into historical processes as well as ensure model reproducibility and a trustworthy scientific result. To drive our point, we present several representative case studies from the Sphaera project where we analyze a large, well-curated corpus of early modern textbooks using an AI model, and rely on the XAI explanatory outputs to generate historical insights concerning their visual content. More specifically, we show that XAI can be used as a partner when investigating debated subjects in the history of science, such as what strategies were used in the early modern period to showcase mathematical instruments and machines.

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Section: Introductionmentioning

confidence: 99%

Explainability and transparency in the realm of digital humanities: toward a historian XAI

El-Hajj,

Eberle,

Merklein

et al. 2023

Int J Digit Humanities

View full text Add to dashboard Cite

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Explainability, Public Reason, and Medical Artificial Intelligence

Silva

2023

Ethic Theory Moral Prac

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The contention that medical artificial intelligence (AI) should be ‘explainable’ is widespread in contemporary philosophy and in legal and best practice documents. Yet critics argue that ‘explainability’ is not a stable concept; non-explainable AI is often more accurate; mechanisms intended to improve explainability do not improve understanding and introduce new epistemic concerns; and explainability requirements are ad hoc where human medical decision-making is often opaque. A recent ‘political response’ to these issues contends that AI used in high-stakes scenarios, including medical AI, must be explainable to meet basic standards of legitimacy: People are owed reasons for decisions that impact their vital interests, and this requires explainable AI. This article demonstrates why the political response fails. Attending to systemic considerations, as its proponents desire, suggests that the political response is subject to the same criticisms as other arguments for explainable AI and presents new issues. It also suggests that decision-making about non-explainable medical AI can meet public reason standards. The most plausible version of the response amounts to a simple claim that public reason demands reasons why AI is permitted. But that does not actually support explainable AI or respond to criticisms of strong requirements for explainable medical AI.

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Introducing the ethical-epistemic matrix: a principle-based tool for evaluating artificial intelligence in medicine

Adams

2024

AI Ethics

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While there has been much discussion of the ethical assessment of artificial intelligence (AI) in medicine, such work has rarely been combined with the parallel body of scholarship analyzing epistemic implications of AI. This paper proposes a method for joint evaluation of AI’s ethical and epistemic implications in medicine that draws on the principle-oriented tradition in bioethics and the consequent ‘ethical matrix’ approach to assessing novel technologies. It first introduces principle-based approaches as specific tools for ethical assessment of AI in medicine and other domains that are contrasted with the lack of comparable epistemic principles that would govern AI evaluation in medicine. In the next section, the ethical matrix is explained as a well-established principle-based tool in applied ethics that has had some limited applications to near-term implications of AI in medicine and elsewhere that can be strengthened, I suggest, using epistemic principles. To this end, the following section looks to the philosophy of science for relevant epistemic principles, identifying ‘accuracy’, ‘consistency’, ‘relevance’, and ‘instrumental efficacy’ as a provisional set for technology evaluation. The next section articulates the relevance of these epistemic principles to AI in medicine by highlighting conventional standards that have already been applied in AI, epistemology, and the medical sciences. Before concluding, the paper then defines and defends the possibility of an ‘ethical-epistemic matrix’ for the application of these epistemic principles alongside established ethical principles to a selection of stakeholder groups: patients, clinicians, developers, and the public.

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Integrating Artificial Intelligence in Scientific Practice: Explicable AI as an Interface

Cited by 3 publications

References 8 publications

Explainability and transparency in the realm of digital humanities: toward a historian XAI

Explainability and transparency in the realm of digital humanities: toward a historian XAI

Explainability, Public Reason, and Medical Artificial Intelligence

Introducing the ethical-epistemic matrix: a principle-based tool for evaluating artificial intelligence in medicine

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