Towards A Unified Utilitarian Ethics Framework for Healthcare Artificial
Intelligence
- URL: http://arxiv.org/abs/2309.14617v1
- Date: Tue, 26 Sep 2023 02:10:58 GMT
- Title: Towards A Unified Utilitarian Ethics Framework for Healthcare Artificial
Intelligence
- Authors: Forhan Bin Emdad, Shuyuan Mary Ho, Benhur Ravuri, Shezin Hussain
- Abstract summary: This study attempts to identify the major ethical principles influencing the utility performance of AI at different technological levels.
Justice, privacy, bias, lack of regulations, risks, and interpretability are the most important principles to consider for ethical AI.
We propose a new utilitarian ethics-based theoretical framework for designing ethical AI for the healthcare domain.
- Score: 0.08192907805418582
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Artificial Intelligence (AI) aims to elevate healthcare to a pinnacle by
aiding clinical decision support. Overcoming the challenges related to the
design of ethical AI will enable clinicians, physicians, healthcare
professionals, and other stakeholders to use and trust AI in healthcare
settings. This study attempts to identify the major ethical principles
influencing the utility performance of AI at different technological levels
such as data access, algorithms, and systems through a thematic analysis. We
observed that justice, privacy, bias, lack of regulations, risks, and
interpretability are the most important principles to consider for ethical AI.
This data-driven study has analyzed secondary survey data from the Pew Research
Center (2020) of 36 AI experts to categorize the top ethical principles of AI
design. To resolve the ethical issues identified by the meta-analysis and
domain experts, we propose a new utilitarian ethics-based theoretical framework
for designing ethical AI for the healthcare domain.
Related papers
- FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare [73.78776682247187]
Concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI.
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
arXiv Detail & Related papers (2023-08-11T10:49:05Z) - Ethics in the Age of AI: An Analysis of AI Practitioners' Awareness and
Challenges [11.656193349991609]
We conducted a survey aimed at understanding AI practitioners' awareness of AI ethics and their challenges in incorporating ethics.
Based on 100 AI practitioners' responses, our findings indicate that majority of AI practitioners had a reasonable familiarity with the concept of AI ethics.
Formal education/training was considered somewhat helpful in preparing practitioners to incorporate AI ethics.
arXiv Detail & Related papers (2023-07-14T02:50:46Z) - Ethics in conversation: Building an ethics assurance case for autonomous
AI-enabled voice agents in healthcare [1.8964739087256175]
The principles-based ethics assurance argument pattern is one proposal in the AI ethics landscape.
This paper presents the interim findings of a case study applying this ethics assurance framework to the use of Dora, an AI-based telemedicine system.
arXiv Detail & Related papers (2023-05-23T16:04:59Z) - Ensuring Trustworthy Medical Artificial Intelligence through Ethical and
Philosophical Principles [4.705984758887425]
AI-based computer-assisted diagnosis and treatment tools can democratize healthcare by matching the clinical level or surpassing clinical experts.
The democratization of such AI tools can reduce the cost of care, optimize resource allocation, and improve the quality of care.
integrating AI into healthcare raises several ethical and philosophical concerns, such as bias, transparency, autonomy, responsibility, and accountability.
arXiv Detail & Related papers (2023-04-23T04:14:18Z) - 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) - AI Ethics in Smart Healthcare [4.226118870861363]
This article reviews the landscape of ethical challenges of integrating artificial intelligence into smart healthcare products.
Ethical challenges relate to transparency, bias, privacy, safety, responsibility, justice, and autonomy.
Open challenges and recommendations are outlined to enable the integration of ethical principles into the design, validation, clinical trials, deployment, monitoring, repair, and retirement of AI-based smart healthcare products.
arXiv Detail & Related papers (2022-11-02T15:30:40Z) - AI Ethics: An Empirical Study on the Views of Practitioners and
Lawmakers [8.82540441326446]
Transparency, accountability, and privacy are the most critical AI ethics principles.
Lack of ethical knowledge, no legal frameworks, and lacking monitoring bodies are the most common AI ethics challenges.
arXiv Detail & Related papers (2022-06-30T17:24:29Z) - 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) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - 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)
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.