Crossing the Trust Gap in Medical AI: Building an Abductive Bridge for xAI

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18460%2F24%3A50021645" target="_blank" >RIV/62690094:18460/24:50021645 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s13347-024-00790-4" target="_blank" >https://link.springer.com/article/10.1007/s13347-024-00790-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s13347-024-00790-4" target="_blank" >10.1007/s13347-024-00790-4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Crossing the Trust Gap in Medical AI: Building an Abductive Bridge for xAI

Popis výsledku v původním jazyce

In this paper, we argue that one way to approach what is known in the literature as the “Trust Gap” in Medical AI is to focus on explanations from an Explainable AI (xAI) perspective. Against the current framework on xAI – which does not offer a real solution – we argue for a pragmatist turn, one that focuses on understanding how we provide explanations in Traditional Medicine (TM), composed by human agents only. Following this, explanations have two specific relevant components: they are usually (i) social and (ii) abductive. Explanations, in this sense, ought to provide understanding by answering contrastive why-questions: “Why had P happened instead of Q?” (Miller in AI 267:1–38, 2019) (Sect. 1). In order to test the relevancy of this concept of explanation in medical xAI, we offer several reasons to argue that abductions are crucial for medical reasoning and provide a crucial tool to deal with trust gaps between human agents (Sect. 2). If abductions are relevant in TM, we can test the capability of Artificial Intelligence systems on this merit. Therefore, we provide an analysis of the capacity for social and abductive reasoning of different AI technologies. Accordingly, we posit that Large Language Models (LLMs) and transformer architectures exhibit a noteworthy potential for effective engagement in abductive reasoning. By leveraging the potential abductive capabilities of LLMs and transformers, we anticipate a paradigm shift in the integration of explanations within AI systems. This, in turn, has the potential to enhance the trustworthiness of AI-driven medical decisions, bridging the Trust Gap that has been a prominent challenge in the field of Medical AI (Sect. 3). This development holds the potential to not only improve the interpretability of AI-generated medical insights but also to guarantee that trust among practitioners, patients, and stakeholders in the healthcare domain is still present.

Název v anglickém jazyce

Crossing the Trust Gap in Medical AI: Building an Abductive Bridge for xAI

Popis výsledku anglicky

In this paper, we argue that one way to approach what is known in the literature as the “Trust Gap” in Medical AI is to focus on explanations from an Explainable AI (xAI) perspective. Against the current framework on xAI – which does not offer a real solution – we argue for a pragmatist turn, one that focuses on understanding how we provide explanations in Traditional Medicine (TM), composed by human agents only. Following this, explanations have two specific relevant components: they are usually (i) social and (ii) abductive. Explanations, in this sense, ought to provide understanding by answering contrastive why-questions: “Why had P happened instead of Q?” (Miller in AI 267:1–38, 2019) (Sect. 1). In order to test the relevancy of this concept of explanation in medical xAI, we offer several reasons to argue that abductions are crucial for medical reasoning and provide a crucial tool to deal with trust gaps between human agents (Sect. 2). If abductions are relevant in TM, we can test the capability of Artificial Intelligence systems on this merit. Therefore, we provide an analysis of the capacity for social and abductive reasoning of different AI technologies. Accordingly, we posit that Large Language Models (LLMs) and transformer architectures exhibit a noteworthy potential for effective engagement in abductive reasoning. By leveraging the potential abductive capabilities of LLMs and transformers, we anticipate a paradigm shift in the integration of explanations within AI systems. This, in turn, has the potential to enhance the trustworthiness of AI-driven medical decisions, bridging the Trust Gap that has been a prominent challenge in the field of Medical AI (Sect. 3). This development holds the potential to not only improve the interpretability of AI-generated medical insights but also to guarantee that trust among practitioners, patients, and stakeholders in the healthcare domain is still present.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

60301 - Philosophy, History and Philosophy of science and technology

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Philosophy and Technology

ISSN

2210-5433

e-ISSN

2210-5441

Svazek periodika

37

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

25

Strana od-do

"Article number: 105"

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85201531768