For smart cities, data-driven innovation promises societal benefits and increased well-being for residents and visitors. At the same time, the deployment of data-driven innovation poses significant ethical challenges. Although cities and other public-sector actors have increasingly adopted ethical principles, employing them in practice remains challenging. In this commentary, we use a virtue-based approach that bridges the gap between abstract principles and the daily work of practitioners who engage in and with data-driven innovation processes. Inspired by Aristotle, we describe practices of (...) data-driven innovation in a smart city applying the concepts of virtue and phronêsis, meaning good judgment of and sensitivity to ethical issues. We use a dialogic case-study approach to study two cases of data-driven innovation in the city of Helsinki. We then describe as an illustration of how our approach can help bridge the gap between concrete practices of data-driven innovation and high-level principles. Overall, we advance a theoretically grounded, virtue-based approach, which is practice oriented and linked to the daily work of data scientists and other practitioners of data-driven innovation. Further, this approach helps understand the need for and importance of individual application of phronêsis, which is particularly important in public-sector organizations that can experience gaps between principle and practice. This importance is further intensified in cases of data-driven innovation in which, by definition, novel and unknown contexts are explored. (shrink)

The use of AI in weapons systems raises numerous ethical issues. To date, work on weaponized AI has tended to be theoretical and normative in nature, consisting in critical policy analyses and ethical considerations, carried out by philosophers, legal scholars, and political scientists. However, adequately addressing the cultural and social dimensions of technology requires insights and methods from empirical moral and cultural psychology. To do so, this position piece describes the motivations for and sketches the nature of a normative, cultural (...) psychology of weaponized AI. The motivations for this project include the increasingly global, cross-cultural and international, nature of technologies, and counter-intuitive nature of normative thoughts and behaviors. The nature of this project consists in developing standardized measures of AI ethical reasoning and intuitions, coupled with questions exploring the development of norms, administered and validated across different cultural groups and disciplinary contexts. The goal of this piece is not to provide a comprehensive framework for understanding the cultural facets and psychological dimensions of weaponized AI but, rather, to outline in broad terms the contours of an emerging research agenda. (shrink)

Since Chinese scholars are playing an increasingly important role in shaping the national landscape of discussion on AI ethics, understanding their ethical concerns and preferred solutions is essential for global cooperation on governance of AI. This article, therefore, provides the first elaborated analysis on the discourse on AI ethics in Chinese academia, via a systematic literature review. This article has three main objectives. to identify the most discussed ethical issues of AI in Chinese academia and those being left out ; (...) to analyze the solutions proposed and preferred by Chinese scholars ; and to map out whose voices are dominating and whose are in the marginal. Findings suggest that in terms of short-term implications, Chinese scholars’ concerns over AI resemble predominantly the content of international ethical guidelines. Yet in terms of long-term implications, there are some significant differences needed to be further addressed in a cultural context. Further, among a wide range of solution proposals, Chinese scholars seem to prefer strong-binding regulations to those weak ethical guidelines. In addition, this article also found that the Chinese academic discourse was dominated by male scholars and those who are from elite universities, which arguably is not a unique phenomenon in China. (shrink)

As early-career neuroethicists, we come to the field of neuroethics at a unique moment: we are well-situated to consider nearly two decades of neuroethics scholarship and identify challenges that have persisted across time. But we are also looking squarely ahead, embarking on the next generation of exciting and productive neuroethics scholarship. In this article, we both reflect backwards and turn our gaze forward. First, we highlight criticisms of neuroethics, both from scholars within the field and outside it, that have focused (...) on speculation and lack of skepticism; the dearth of consideration of broader social issues such as justice and equality, both with regard to who speaks for neuroethics as a field and who benefits from its recommendations and findings; and the insufficient focus on the practical impact of our ethical work. Second, we embrace the concept of “translational neuroethics” to outline a vision for neuroethics that is integrated, inclusive, and impactful. Integration can help us identify more pertinent, real-world issues, and move away from speculation; inclusivity can help ensure that the questions we attend to are not merely relevant to a single subgroup but aim toward just distribution of benefits; and impact can help us think beyond guidelines and recommendations to focus on implementation. Our goal is for this call to action to help shape neuroethics into a discipline that develops rigorous research agendas through relationships with interdisciplinary partners, that is broadly inclusive and attends to issues beyond novel neurotechnologies, and that is devoted to the translation of scholarship into practice. (shrink)

Ample scholarship demonstrates that data-intensive technologies have the capacity to cause serious harm and that their developers are obliged to address ethics in their work. This ethnographic paper tells the story of data scientists attempting to instantiate a carefully considered ethical vision into a data infrastructure while balancing competing priorities, negotiating divergent interests, and wrestling with contrasting values. I use their story to develop the concept of “ethical abduction,” which I characterize as an exemplary process by which actors can intentionally (...) and systematically address ethical issues that arise during their day-to-day actions by making decisions with consideration for a foundational ethical worldview. It entails tacking back and forth between divergent but complementary ways of thinking: between establishing ideals and making decisions given practical constraints; between understanding historical context and anticipating future consequences; between acknowledging structural dependencies and accepting responsibility for moral agency. (shrink)

In the development of governmental policy for artificial intelligence that is informed by ethics, one avenue currently pursued is that of drawing on “AI Ethics Principles”. However, these AI Ethics Principles often fail to be actioned in governmental policy. This paper proposes a novel framework for the development of ‘Actionable Principles for AI’. The approach acknowledges the relevance of AI Ethics Principles and homes in on methodological elements to increase their practical implementability in policy processes. As a case study, elements (...) are extracted from the development process of the Ethics Guidelines for Trustworthy AI of the European Commission’s “High Level Expert Group on AI”. Subsequently, these elements are expanded on and evaluated in light of their ability to contribute to a prototype framework for the development of 'Actionable Principles for AI'. The paper proposes the following three propositions for the formation of such a prototype framework: preliminary landscape assessments; multi-stakeholder participation and cross-sectoral feedback; and, mechanisms to support implementation and operationalizability. (shrink)

This article proposes a conceptual mapping to outline salient properties and relations that allow for a knowledge transfer from the well-established greenwashing phenomenon to the more recent machinewashing. We account for relevant dissimilarities, indicating where conceptual boundaries may be drawn. Guided by a “reasoning by analogy” approach, the article addresses the structural analogy and machinewashing idiosyncrasies leading to a novel and theoretically informed model of machinewashing. Consequently, machinewashing is defined as a strategy that organizations adopt to engage in misleading behavior (...) about ethical Artificial Intelligence /algorithmic systems. Machinewashing involves misleading information about ethical AI communicated or omitted via words, visuals, or the underlying algorithm of AI itself. Furthermore, and going beyond greenwashing, machinewashing may be used for symbolic actions such as lobbying and prevention of stricter regulation. By outlining diverse theoretical foundations of the established greenwashing domain and their relation to specific research questions, the article proposes a machinewashing model and a set of theory-related research questions on the macro, meso, and micro-level for future machinewashing research. We conclude by stressing limitations and by outlining practical implications for organizations and policymakers. (shrink)

By mid-2019 there were more than 80 AI ethics guides available in the public domain. Despite this, 2020 saw numerous news stories break related to ethically questionable uses of AI. In part, this is because AI ethics theory remains highly abstract, and of limited practical applicability to those actually responsible for designing algorithms and AI systems. Our previous research sought to start closing this gap between the ‘what’ and the ‘how’ of AI ethics through the creation of a searchable typology (...) of tools and methods designed to translate between the five most common AI ethics principles and implementable design practices. Whilst a useful starting point, that research rested on the assumption that _all_ AI practitioners are aware of the ethical implications of AI, understand their importance, and are actively seeking to respond to them. In reality, it is unclear whether this is the case. It is this limitation that we seek to overcome here by conducting a mixed-methods qualitative analysis to answer the following four questions: what do AI practitioners understand about the need to translate ethical principles into practice? What motivates AI practitioners to embed ethical principles into design practices? What barriers do AI practitioners face when attempting to translate ethical principles into practice? And finally, what assistance do AI practitioners want and need when translating ethical principles into practice? (shrink)

BackgroundHealth data-driven activities have become central in diverse fields (research, AI development, wearables, etc.), and new ethical challenges have arisen with regards to privacy, integrity, and appropriateness of use. To ensure the protection of individuals’ fundamental rights and freedoms in a changing environment, including their right to the protection of personal data, we aim to identify the ethical approaches adopted by scientists during intensive data exploitation when collecting, using, or sharing peoples’ health data.MethodsTwelve scientists who were collecting, using, or sharing (...) health data in different contexts in Sweden, were interviewed. We used systematic expert interviews to access these scientists’ specialist knowledge, and analysed the interviews with thematic analysis. Phrases, sentences, or paragraphs through which ethical values and norms were expressed, were identified and coded. Codes that reflected similar concepts were grouped, subcategories were formulated, and categories were connected to traditional ethical approaches.ResultsThrough several examples, the respondents expressed four different ethical approaches, which formed the main conceptual categories: consideration of consequences, respect for rights, procedural compliance, and being professional.ConclusionsTo a large extent, the scientists’ ethical approaches were consistent with ethical and legal principles. Data sharing was considered important and worth pursuing, even though it is difficult. An awareness of the complex issues involved in data sharing was reflected from different perspectives, and the respondents commonly perceived a general lack of practical procedures that would by default ensure ethical and legally compliant data collection and sharing. We suggest that it is an opportune time to move on from policy discussions to practical technological ethics-by-design solutions that integrate these principles into practice. (shrink)

The article focuses on the inconsistency between the European Commission’s position on excluding military AI from its emerging AI policy, and at the same time EU policy initiatives targeted at supporting military and defence elements of AI on the EU level. It leads to the question, what, then, does the debate on military AI suggest to the EU’s actorness discussed in the light of Europe as a power debate with a particular focus on Normative Power Europe, Market Power Europe, and (...) Military Power Europe. By employing discourse analysis, the article examines the EU’s AI strategic discourse, consisting of selected AI-related policy documents from different EU institutions. As a result, the article proposes the Military Power Europe definition based on four categories proposed from the Europe as power debate: ways of action, self-definition, preferred international engagement, and the role of the military. It argues that alongside normative proposals for military AI governance, there are evident desires for militarization in the context of AI development, and a considerable role for the military in the future directions of the EU’s digital and security policies. Despite the inconsistency among EU institutions, military AI appears to be actively discussed within the selected discourse, and so is a part of the EU’s emerging AI policy. (shrink)

Artificial intelligence is changing healthcare and the practice of medicine as data-driven science and machine-learning technologies, in particular, are contributing to a variety of medical and clinical tasks. Such advancements have also raised many questions, especially about public trust. As a response to these concerns there has been a concentrated effort from public bodies, policy-makers and technology companies leading the way in AI to address what is identified as a "public trust deficit". This paper argues that a focus on trust (...) as the basis upon which a relationship between this new technology and the public is built is, at best, ineffective, at worst, inappropriate or even dangerous, as it diverts attention from what is actually needed to actively warrant trust. Instead of agonising about how to facilitate trust, a type of relationship which can leave those trusting vulnerable and exposed, we argue that efforts should be focused on the difficult and dynamic process of ensuring reliance underwritten by strong legal and regulatory frameworks. From there, trust could emerge but not merely as a means to an end. Instead, as something to work in practice towards; that is, the deserved result of an ongoing ethical relationship where there is the appropriate, enforceable and reliable regulatory infrastructure in place for problems, challenges and power asymmetries to be continuously accounted for and appropriately redressed. Data sharing not applicable as no datasets generated and/or analysed for this study. (shrink)

This study examines employee perceptions on the effective adoption of artificial intelligence principles in their organizations. 49 interviews were conducted with employees of 24 organizations across 11 countries. Participants worked directly with AI across a range of positions, from junior data scientist to Chief Analytics Officer. The study found that there are eleven components that could impact the effective adoption of AI principles in organizations: communication, management support, training, an ethics office, a reporting mechanism, enforcement, measurement, accompanying technical processes, a (...) sufficient technical infrastructure, organizational structure, and an interdisciplinary approach. The components are discussed in the context of business code adoption theory. The findings offer a first step in understanding potential methods for the effective adoption of AI principles in organizations. (shrink)

Many ethics initiatives have stipulated sets of principles and standards for good technology development in the AI sector. However, several AI ethics researchers have pointed out a lack of practical realization of these principles. Following that, AI ethics underwent a practical turn, but without deviating from the principled approach. This paper proposes a complementary to the principled approach that is based on virtue ethics. It defines four “basic AI virtues”, namely justice, honesty, responsibility and care, all of which represent specific (...) motivational settings that constitute the very precondition for ethical decision making in the AI field. Moreover, it defines two “second-order AI virtues”, prudence and fortitude, that bolster achieving the basic virtues by helping with overcoming bounded ethicality or hidden psychological forces that can impair ethical decision making and that are hitherto disregarded in AI ethics. Lastly, the paper describes measures for successfully cultivating the mentioned virtues in organizations dealing with AI research and development. (shrink)

The abundance of literature on ethical concerns regarding artificial intelligence (AI) highlights the need to systematize, integrate, and categorize existing efforts through a systematic literature review. The article aims to investigate prevalent concerns, proposed solutions, and prominent ethical approaches within the field. Considering 309 articles from the beginning of the publications in this field up until December 2021, this systematic literature review clarifies what the ethical concerns regarding AI are, and it charts them into two groups: (i) ethical concerns that (...) arise from the design of AI and (ii) ethical concerns that arise from human–AI interactions. The analysis of the obtained sample highlights the most recurrent ethical concerns. Finally, it exposes the main proposals of the literature to handle the ethical concerns according to the main ethical approaches. It interprets the findings to lay the foundations for future research on the ethics of AI. (shrink)

Contemporary ethical analysis of Artificial Intelligence is growing rapidly. One of its most recognizable outcomes is the publication of a number of ethics guidelines that, intended to guide governmental policy, address issues raised by AI design, development, and implementation and generally present a set of recommendations. Here we propose two things: first, regarding content, since some of the applied issues raised by AI are related to fundamental questions about topics like intelligence, consciousness, and the ontological and ethical status of humans, (...) among others, the treatment of these issues would benefit from interfacing with neuroethics that has been addressing those same issues in the context of brain research. Second, the identification and management of some of the practical ethical challenges raised by AI would be enriched by embracing the methodological resources used in neuroethics. In particular, we focus on the methodological distinction between conceptual and action-oriented neuroethical approaches. We argue that the normative discussion about AI will benefit from further integration of conceptual analysis, including analysis of some operative assumptions, their meaning in different contexts, and their mutual relevance in order to avoid misplaced or disproportionate concerns and achieve a more realistic and useful approach to identifying and managing the emerging ethical issues. (shrink)

Developers are often the engine behind the creation and implementation of new technologies, including in the artificial intelligence surge that is currently underway. In many cases these new technologies introduce significant risk to affected stakeholders; risks that can be reduced and mitigated by such a dominant party. This is fully recognized by texts that analyze risks in the current AI transformation, which suggest voluntary adoption of ethical standards and imposing ethical standards via regulation and oversight as tools to compel developers (...) to reduce such risks. However, what these texts usually sidestep is the question of how aware developers are to the risks they are creating with these new AI technologies, and what their attitudes are towards such risks. This paper asks to rectify this gap in research, by analyzing an ongoing case study. Focusing on six Israeli AI startups in the field of radiology, I carry out a content analysis of their online material in order to examine these companies’ stances towards the potential threat their automated tools pose to patient safety and to the work-standing of healthcare professionals. Results show that these developers are aware of the risks their AI products pose, but tend to deny their own role in the technological transformation and dismiss or downplay the risks to stakeholders. I conclude by tying these findings back to current risk-reduction recommendations with regards to advanced AI technologies, and suggest which of them hold more promise in light of developers’ attitudes. (shrink)

As if 2020 were not a peculiar enough year, its fifth month has seen the relatively quiet publication of a preprint describing the most powerful Natural Language Processing (NLP) system to date, GPT-3 (Generative Pre-trained Transformer-3), by Silicon Valley research firm OpenAI. Though the software implementation of GPT-3 is still in its initial Beta release phase, and its full capabilities are still unknown as of the time of this writing, it has been shown that this Artificial Intelligence can comprehend prompts (...) in natural language, on virtually any topic, and generate relevant, original text content that is indistinguishable from human writing. Moreover, access to these capabilities, in a limited yet worrisome enough extent, is available to the general public as of the time of this writing. This paper presents select examples of original content generated by the author using GPT-3. These examples illustrate some of the capabilities of GPT-3 in comprehending prompts in natural language and generating convincing content in response. We use these examples to raise specific, fundamental questions pertaining to the intellectual property of this content and the potential use of GPT-3 to facilitate plagiarism. Our goal is to instigate not just a sense of urgency, but of a present tardiness on the part of the academic community in addressing these questions. (shrink)

As if 2020 was not a peculiar enough year, its fifth month saw the relatively quiet publication of a preprint describing the most powerful natural language processing (NLP) system to date—GPT-3 (Generative Pre-trained Transformer-3)—created by the Silicon Valley research firm OpenAI. Though the software implementation of GPT-3 is still in its initial beta release phase, and its full capabilities are still unknown as of the time of this writing, it has been shown that this artificial intelligence can comprehend prompts in (...) natural language, on virtually any topic, and generate relevant original text content that is indistinguishable from human writing. Moreover, access to these capabilities, in a limited yet worrisome enough extent, is available to the general public. This paper presents examples of original content generated by the author using GPT-3. These examples illustrate some of the capabilities of GPT-3 in comprehending prompts in natural language and generating convincing content in response. I use these examples to raise specific fundamental questions pertaining to the intellectual property of this content and the potential use of GPT-3 to facilitate plagiarism. The goal is to instigate a sense of urgency, as well as a sense of present tardiness on the part of the academic community in addressing these questions. (shrink)

A 4ª Revolução Industrial é o culminar da era digital. Atualmente, tecnologias como robótica, nanotecnologia, genética e inteligência artificial prometem transformar nosso mundo e a maneira como vivemos. O campo da Segurança e da Ética da Inteligência Artificial são áreas de pesquisa emergentes que vêm ganhando popularidade nos últimos anos. Diversas organizações de cunho privado, público e não governamentais têm publicado diretrizes propondo princípios éticos para a regulamentação do uso e desenvolvimento de sistemas inteligentes autônomos. Meta-análises do campo de pesquisa (...) em Ética da IA apontam uma convergência sobre certos princípios éticos que, supostamente, governam a indústria da IA. Entretanto, pouco se sabe sobre a eficiência desta forma de “Ética”. Neste estudo, gostaríamos de realizar uma análise crítica do atual estado da Ética da IA, e sugerir que essa forma de governança baseada em diretrizes éticas principialista não é suficiente para normatizar a indústria da IA e seus desenvolvedores. Acreditamos que drásticas mudanças sejam necessárias, tanto nos processos de formação de profissionais das áreas ligadas ao desenvolvimento de software e sistemas inteligentes quanto no aumento da regulamentação desses profissionais e sua indústria. Para tanto, sugerimos que o Direito se beneficie das contribuições recentes da Bioética, de forma a explicitar em termos legais as contribuições da Ética da IA para a governança. (shrink)

The European Union Commission’s whitepaper on Artificial Intelligence proposes shaping the emerging AI market so that it better reflects common European values. It is a master plan that builds upon the EU AI High-Level Expert Group guidelines. This article reviews the masterplan, from a culture cycle perspective, to reflect on its potential clashes with current societal, technical, and methodological constraints. We identify two main obstacles in the implementation of this plan: the lack of a coherent EU vision to drive future (...) decision-making processes at state and local levels and the lack of methods to support a sustainable diffusion of AI in our society. The lack of a coherent vision stems from not considering societal differences across the EU member states. We suggest that these differences may lead to a fractured market and an AI crisis in which different members of the EU will adopt nation-centric strategies to exploit AI, thus preventing the development of a frictionless market as envisaged by the EU. Moreover, the Commission aims at changing the AI development culture proposing a human-centred and safety-first perspective that is not supported by methodological advancements, thus taking the risks of unforeseen social and societal impacts of AI. We discuss potential societal, technical, and methodological gaps that should be filled to avoid the risks of developing AI systems at the expense of society. Our analysis results in the recommendation that the EU regulators and policymakers consider how to complement the EC programme with rules and compensatory mechanisms to avoid market fragmentation due to local and global ambitions. Moreover, regulators should go beyond the human-centred approach establishing a research agenda seeking answers to the technical and methodological open questions regarding the development and assessment of human-AI co-action aiming for a sustainable AI diffusion in the society. (shrink)

Today humanity is in the midst of the massive expansion of new and fundamental technology, represented by advanced artificial intelligence (AI) systems. The ongoing revolution of these technologies and their profound impact across various sectors, has triggered discussions about the characteristics and values that should guide their use and development in a responsible manner. In this paper, we conduct a systematic literature review with the aim of pointing out existing challenges and required principles in AI-based systems in different industries. We (...) discuss our findings and provide general recommendations to be considered during AI deployment in production. The results have shown many gaps and concerns towards responsible AI and integration of complex AI models in the industry that the research community could address. (shrink)

The AI Robotics Ethics Society (AIRES) is a non-profit organization founded in 2018 by Aaron Hui to promote awareness and the importance of ethical implementation and regulation of AI. AIRES is now an organization with chapters at universities such as UCLA (Los Angeles), USC (University of Southern California), Caltech (California Institute of Technology), Stanford University, Cornell University, Brown University, and the Pontifical Catholic University of Rio Grande do Sul (Brazil). AIRES at PUCRS is the first international chapter of AIRES, and (...) as such, we are committed to promoting and enhancing the AIRES Mission. Our mission is to focus on educating the AI leaders of tomorrow in ethical principles to ensure that AI is created ethically and responsibly. As there are still few proposals for how we should implement ethical principles and normative guidelines in the practice of AI system development, the goal of this work is to try to bridge this gap between discourse and praxis. Between abstract principles and technical implementation. In this work, we seek to introduce the reader to the topic of AI Ethics and Safety. At the same time, we present several tools to help developers of intelligent systems develop "good" models. This work is a developing guide published in English and Portuguese. Contributions and suggestions are welcome. (shrink)

A AI Robotics Ethics Society (AIRES) é uma organização sem fins lucrativos fundada em 2018 por Aaron Hui, com o objetivo de se promover a conscientização e a importância da implementação e regulamentação ética da AI. A AIRES é hoje uma organização com capítulos em universidade como UCLA (Los Angeles), USC (University of Southern California), Caltech (California Institute of Technology), Stanford University, Cornell University, Brown University e a Pontifícia Universidade Católica do Rio Grande do Sul (Brasil). AIRES na PUCRS é (...) o primeiro capítulo internacional da AIRES, e como tal, estamos empenhados em promover e aprimorar a Missão AIRES. Nossa missão é focar na educação dos líderes da IA de amanhã em princípios éticos, para assegurar que a IA seja criada de forma ética e responsável. Como ainda existem poucas propostas de como devemos implementar princípios éticos e diretrizes normativas na prática do desenvolvimento de sistemas de IA, o objetivo deste trabalho é tentar diminuir esta lacuna entre o discurso e a práxis. Entre princípios abstratos e implementação técnica. Neste trabalho, buscamos introduzir o leitor ao tema da Ética e Segurança da IA. Ao mesmo tempo, apresentamos uma série de ferramentas para auxiliar desenvolvedores de sistemas inteligentes a desenvolver “bons” modelos. Este trabalho é um guia em desenvolvimento publicado em inglês e português. Contribuições e sugestões são bem vindas. (shrink)

What does Cyberpunk and AI Ethics have to do with each other? Cyberpunk is a sub-genre of science fiction that explores the post-human relationships between human experience and technology. One similarity between AI Ethics and Cyberpunk literature is that both seek a dialogue in which the reader may inquire about the future and the ethical and social problems that our technological advance may bring upon society. In recent years, an increasing number of ethical matters involving AI have been pointed and (...) debated, and several ethical principles and guides have been suggested as governance policies for the tech industry. However, would this be the role of AI Ethics? To serve as a soft and ambiguous version of the law? I would like to promote in this article a more Cyberpunk way of doing AI Ethics, whit a more anarchic way of governance. In this study, I will seek to expose some of the deficits of the underlying power structures of our society, and suggest that AI governance be subject to public opinion, so that ‘good AI’ can become ‘good AI for all’. (shrink)