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How can the public sector use AI ethically and responsibly for the benefit of people? The sustainable development and deployment of artificial intelligence (AI) in the public sector requires dialogue and deliberation between developers, decision makers, deployers, end users, and the public. This paper contributes to the debate on how to develop persuasive government approaches for steering the development and use of AI. We examine the ethical issues and the role of the public in the debate on developing public sector governance of socially and democratically sustainable and technology-intensive societies. To concretize this discussion, we study the co-development of a Finnish national AI program AuroraAI, which aims to provide citizens with tailored and timely services for different life situations, utilizing AI. With the help of this case study, we investigate the challenges posed by the development and use of AI in the service of public administration. We draw particular attention to the efforts made by the AuroraAI Ethics Board in deliberating the AuroraAI solution options and working toward a sustainable and inclusive AI society.

This paper argues that ethical AI cannot be fostered in a vacuum, challenging the perspective that AI ethics research should be isolated from technological advancements and industry collaborations. It refutes the argument presented by Gerdes (Discov Artif Intell. 2022;2(25)), which suggests that industry involvement inherently undermines the integrity of AI ethics research. Through an exploration of historical and contemporary examples of successful academia-industry collaborations, the paper advocates for a synergistic approach that harnesses industry resources and insights to advance ethical AI development. Emphasising the importance of diverse funding, the value of industry insights, and the impracticality of separating AI ethics from computer science, the paper contends that a collaborative, transparent, and inclusive model of AI ethics research is essential for developing practical, relevant, and ethically sound AI technologies aligned with societal values and norms. [ABSTRACT FROM AUTHOR]

Artificial intelligence (Al) is transforming how the federal government operates. Under the right conditions. AI systems can solve complex problems, reduce administrative burdens, improve human decisions, and optimize resources. Under the wrong conditions, Al systems can lead to widespread discrimination, invasions of privacy. and the erosion of democratic norms. A burgeoning literature has emerged to square algorithmic governance with the precepts of constitutional and administrative law. Federal procurement law, however, remains a dangerous blind spot in the reformist agenda. This Article pivots into that neglected space and emerges with comprehensive framework for acquiring ethical AI. Toward that end. the Article makes three main contributions. First, it provides an original account that yokes the ambitions of algorithmic governance, the imperative of ethical AI, and the levers of procurement law. Second. this Article argues that the procurement system is uniquely situated to check and enable algorithmic governance in ways that other legal frameworks miss. Third, the Article prescribes a set of concrete regulatory reforms to instantiate ethical Al throughout the procurement process: from acquisition planning to market solicitation, bid evaluation, source selection, and contract performance. Procurement law will not solve all the challenges of algorithmic governance. Just as surely. those challenges cannot be solved without procurement law. [ABSTRACT FROM AUTHOR]

There is growing expectation that artificial intelligence (AI) developers foresee and mitigate harms that might result from their creations; however, this is exceptionally difficult given the prevalence of emergent behaviors that occur when integrating AI into complex sociotechnical systems. We argue that Naturalistic Decision Making (NDM) principles, models, and tools are well-suited to tackling this challenge. Already applied in high-consequence domains, NDM tools such as the premortem, and others, have been shown to uncover a reasonable set of risks of underlying factors that would lead to ethical harms. Such NDM tools have already been used to develop AI that is more trustworthy and resilient, and can help avoid unintended consequences of AI built with noble intentions. We present predictive policing algorithms as a use case, highlighting various factors that led to ethical harms and how NDM tools could help foresee and mitigate such harms., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 The MITRE Corporation.)

Building and implementing ethical AI systems that benefit the whole society is cost-intensive and a multi-faceted task fraught with potential problems. While on one hand there are technical solutions to mitigate social problems, on the other hand, citizen perceptions can lead to social and political demands that influence the social implementation of AI systems. In this study, we explore the salience of AI issues in the public with an emphasis on ethical criteria to the likelihood that ethical AI is actively demanded by the public. Based on data from 15 surveys (N = 14,988 respondents), our results show that the majority of the German population was not concerned with AI at all. Thereby, the issue salience is dependent on general interest in AI and a higher educational level. Ethical issues are of concern for only a small subset of citizens. However, the salience of ethical issues affects the behavioral intentions of citizens in the way that they tend to avoid AI technology and engage in public discussions about AI. We conclude that the low level of ethical implications may pose a serious problem for the actual implementation of ethical AI for the Common Good. [ABSTRACT FROM AUTHOR]

Big Data and Artificial Intelligence have a symbiotic relationship. Artificial Intelligence needs to be trained on Big Data to be accurate, and Big Data's value is largely realized through its use by Artificial Intelligence. As a result, Big Data and Artificial Intelligence practices are tightly intertwined in real life settings, as are their impacts on society. Unethical uses of Artificial Intelligence are therefore a Big Data problem, at least to some degree. Efforts to address this problem have been dominated by the documentation of Ethical Artificial Intelligence principles and the creation of technical tools that address specific aspects of those principles. However, there is mounting evidence that Ethical Artificial Intelligence principles and technical tools have little impact on the Artificial Intelligence that is created in practice, sometimes in very public ways. The goal of this commentary is to highlight four interconnected areas society can invest in to close this Ethical Artificial Intelligence publication-to-practice gap, maximizing the positive impact Artificial Intelligence and Big Data have on society. For Ethical Artificial Intelligence to become a reality, I argue that these areas need to be addressed holistically in a way that acknowledges their interdependencies. Progress will require iteration, compromise, and transdisciplinary collaboration, but the result of our investments will be the realization of Artificial Intelligence's and Big Data's tremendous potential for social good, in practice rather than in just our hopes and aspirations.

This article focuses on individual lawyers’ responsible use of artificial intelligence (AI) in their practice. More specifically, it examines the ways in which a lawyer’s ethical capabilities and motivations are tested by the rapid growth of automated systems, both to identify the ethical risks posed by AI tools in legal services, and to uncover what is required of lawyers when they use this technology. To do so, we use psychologist James Rest’s Four-component Model of Morality (FCM), which represents the necessary elements for lawyers to engage in professional conduct when utilising AI. We examine issues associated with automation that most seriously challenge each component in context, as well as the skills and resolve lawyers need to adhere to their ethical duties. Importantly, this approach is grounded in social psychology. That is, by looking at human ‘thinking and doing’ (i.e., lawyers’ motivations and capacity when using AI), this offers a different, complementary perspective to the typical, legislative approach in which the law is analysed for regulatory gaps.