A Tale of Two Deficits: Causality and Care in Medical AI

Chen, Melvin

doi:10.1007/s13347-019-00359-6

Cited by 6 publications

(2 citation statements)

References 45 publications

(38 reference statements)

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“…According to the figure, we have two groups of people for whom the explanations should be tailored, the data scientists and the healthcare professionals. Since the theories from psychology and cognitive science provide evidence that humans are able to make causal inferences [58], generating explanations from such models can help us enhance the interpretability of the explanations. Moreover, Miller [59] suggests that, to have interpretability, explanations should also explain in terms of contrasting events (why event X happened instead of Y).…”

Section: Towards Causal Interpretability For Accountabilitymentioning

confidence: 99%

Accountable AI for Healthcare IoT Systems

Bagave

Westberg

Dobbe

et al. 2022

2022 IEEE 4th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA)

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Various AI systems have taken a unique space in our daily lives, helping us in decision-making in critical as well as non-critical scenarios. Although these systems are widely adopted across different sectors, they have not been used to their full potential in critical domains such as the healthcare sector enabled by the Internet of Things (IoT). One of the important hindering factors for adoption is the implication for accountability of decisions and outcomes affected by an AI system, where the term accountability is understood as a means to ensure the performance of a system. However, this term is often interpreted differently in various sectors. Since the EU GDPR regulations and the US congress have emphasised the importance of enabling accountability in AI systems, there is a strong demand to understand and conceptualise this term. It is crucial to address various aspects integrated with accountability and understand how it affects the adoption of AI systems. In this paper, we conceptualise these factors affecting accountability and how it contributes to a trustworthy healthcare AI system. By focusing on healthcare IoT systems, our conceptual mapping will help the readers understand what system aspects those factors are contributing to and how they affect the system trustworthiness. Besides illustrating accountability in detail, we also share our vision towards causal interpretability as a means to enhance accountability for healthcare AI systems. The insights of this paper shall contribute to the knowledge of academic research on accountability, and benefit AI developers and practitioners in the healthcare sector.

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Section: Towards Causal Interpretability For Accountabilitymentioning

confidence: 99%

Accountable AI for Healthcare IoT Systems

Bagave

Westberg

Dobbe

et al. 2022

2022 IEEE 4th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA)

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“…Chen ( 2019 ) provides a very interesting discussion of AI and causality but not from the perspective of the RCT issue that I raise here but as a much broader but still relevant point of view. He advances the key question about whether AI technology should be adopted in the medical field.…”

Section: The Causality Conundrum: Do We Still Need Rcts?mentioning

confidence: 99%

Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health

Bickman

2020

Adm Policy Ment Health

101

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This conceptual paper describes the current state of mental health services, identifies critical problems, and suggests how to solve them. I focus on the potential contributions of artificial intelligence and precision mental health to improving mental health services. Toward that end, I draw upon my own research, which has changed over the last half century, to highlight the need to transform the way we conduct mental health services research. I identify exemplars from the emerging literature on artificial intelligence and precision approaches to treatment in which there is an attempt to personalize or fit the treatment to the client in order to produce more effective interventions. Keywords Mental health services • Artificial intelligence • Machine learning • Precision mental health • Randomized clinical trials (RCTs) • Precision medicine "AI will bring many wonders. It may also destabilize everything from nuclear détente to human friendships. We need to think much harder about how to adapt... it is changing human knowledge, perception and reality-and, in so doing, changing the course of human history. We seek to understand it and its consequences and encourage others across the disciplines to do the same."-Kissinger et al. 2019, pp. 24-26 "AI is one of the most profound things we're working on as humanity. It's more profound than fire or electricity."-Alphabet CEO Sundar Pichai, in Thompson and Bodoni 2020. This article is part of a special issue, Festschrift for Leonard Bickman: The Future of Children's Mental Health Services.

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Causal Reasoning and Meno’s Paradox

Chen

Chew

2020

AI & Soc

Self Cite

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Causal reasoning is an aspect of learning, reasoning, and decision-making that involves the cognitive ability to discover relationships between causal relata, learn and understand these causal relationships, and make use of this causal knowledge in prediction, explanation, decision-making, and reasoning in terms of counterfactuals. Can we fully automate causal reasoning? One might feel inclined, on the basis of certain groundbreaking advances in causal epistemology, to reply in the affirmative. The aim of this paper is to demonstrate that one still has good skeptical grounds for resisting any conclusions in favour of the automation of causal reasoning. If by causal reasoning is meant the entirety of the process through which we discover causal relationships and make use of this knowledge in prediction, explanation, decision-making, and reasoning in terms of counterfactuals, then one relies besides on tacit knowledge, as might be constituted by or derived from the epistemic faculty virtues and abilities of the causal reasoner, the value systems and character traits of the causal reasoner, the implicit knowledge base available to the causal reasoner, and the habits that sustain our causal reasoning practices. While certain aspects of causal reasoning may be axiomatized and formalized and algorithms may be implemented to approximate causal reasoning, one has to remain skeptical about whether causal reasoning may be fully automated. This demonstration will involve an engagement with Meno's Paradox.

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A Tale of Two Deficits: Causality and Care in Medical AI

Cited by 6 publications

References 45 publications

Accountable AI for Healthcare IoT Systems

Accountable AI for Healthcare IoT Systems

Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health

Causal Reasoning and Meno’s Paradox

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