Tag: artificial intelligence

The Ethics of AI in Health Care: a Mapping Review

Josh Cowls

This article presents a mapping review of the literature concerning the ethics of artificial intelligence (AI) in health care. The goal of this review is to summarise current debates and identify open questions for future research. Our goal is to inform policymakers, regulators and developers of what they must consider if they are to enable health and care systems to capitalise on the dual advantage of ethical AI; maximising the opportunities to cut costs, improve care, and improve the efficiency of health and care systems, whilst proactively avoiding the potential harms.

I am a co-author on a new paper written with Jessica Morley, Caio Machado, Chris Burr, Indra Joshi, Rosaria Taddeo and Luciano Floridi, now published in Social Science and Medicine.

The Chinese approach to artificial intelligence: an analysis of policy, ethics, and regulation

Josh Cowls

 

In this article, we focus on the socio-political background and policy debates that are shaping China’s AI strategy. In particular, we analyse the main strategic areas in which China is investing in AI and the concurrent ethical debates that are delimiting its use. By focusing on the policy backdrop, we seek to provide a more comprehensive and critical understanding of China’s AI policy by bringing together debates and analyses of a wide array of policy documents.

A new paper by Huw Roberts, myself, Jess Morley, Vincent Wang, Rosaria Taddeo and Luciano Floridi has been published in AI & Society.

How to Design AI for Social Good: Seven Essential Factors

Josh Cowls

A new paper I co-authored with Luciano Floridi, Thomas C. King and Mariarosaria Taddeo has been published (open access) in Science and Engineering Ethics.

Abstract:

The idea of artificial intelligence for social good (henceforth AI4SG) is gaining traction within information societies in general and the AI community in particular. It has the potential to tackle social problems through the development of AI-based solutions. Yet, to date, there is only limited understanding of what makes AI socially good in theory, what counts as AI4SG in practice, and how to reproduce its initial successes in terms of policies. This article addresses this gap by identifying seven ethical factors that are essential for future AI4SG initiatives. The analysis is supported by 27 case examples of AI4SG projects. Some of these factors are almost entirely novel to AI, while the significance of other factors is heightened by the use of AI. From each of these factors, corresponding best practices are formulated which, subject to context and balance, may serve as preliminary guidelines to ensure that well-designed AI is more likely to serve the social good.

A Unified Framework of Five Principles for AI in Society

Josh Cowls

A new short paper by Luciano Floridi and I has been published, open access, in the inaugural issue of the Harvard Data Science Review. 2000px-Black_pencil.svg

Abstract:

Artificial Intelligence (AI) is already having a major impact on society. As a result, many organizations have launched a wide range of initiatives to establish ethical principles for the adoption of socially beneficial AI. Unfortunately, the sheer volume of proposed principles threatens to overwhelm and confuse. How might this problem of ‘principle proliferation’ be solved? In this paper, we report the results of a fine-grained analysis of several of the highest-profile sets of ethical principles for AI. We assess whether these principles converge upon a set of agreed-upon principles, or diverge, with significant disagreement over what constitutes ‘ethical AI.’ Our analysis finds a high degree of overlap among the sets of principles we analyze. We then identify an overarching framework consisting of five core principlesfor ethical AI. Four of them are core principles commonly used in bioethics: beneficence, non-maleficence, autonomy, and justice.On the basis of our comparative analysis, we argue that a new principle is needed in addition: explicability, understood as incorporating both the epistemological sense of intelligibility (as an answer to the question ‘how does it work?’) and in the ethical sense of accountability (as an answer to the question: ‘who is responsible for the way it works?’). In the ensuing discussion, we note the limitations and assess the implications of this ethical framework for future efforts to create laws, rules, technical standards, and best practices for ethical AI in a wide range of contexts.

Deciding how to decide: Six key questions for reducing AI’s democratic deficit

Josh Cowls

Artificial intelligence (AI) has a “democratic deficit” — and maybe that shouldn’t be a surprise. As Jonnie Penn and others have argued, AI, in conception and application, has long been bound up with the logic and operations of big business. Today, we find AI put to use in an increasing array of socially significant settings, from sifting through CVs to swerving through traffic, many of which continue to serve these corporate interests. (We also find “AI” the brand put to use in the absence of AI the technlogy: a recent study suggests that 40% of start-ups who claim to use AI do not in fact do so.) Nor are governments of all stripes lacking interest in the potential power of AI to patrol and cajole the movements and mindsets of citizens.

Read more at Medium.

AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations

Josh Cowls

I am a co-author on a new paper which appears in Minds and Machines (open access).2000px-Black_pencil.svgThis article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society.