"AI ethics is much debated today and often attracts fears about superintelligence. This book, written by a philosopher of technology engaged in research and policy on the topic, moves away from science fiction fantasies and instead focuses on concrete ethical issues raised by AI and data science. After contextualizing nightmares about AI and sketching some philosophical issues, it looks at what the technology actually is and discusses problems such as responsibility, transparency, and bias. It also gives an overview of AI policy and discusses its challenges - also in the light of climate change. The book ends with a call for more wisdom next to intelligence"--
An accessible synthesis of ethical issues raised by artificial intelligence that moves beyond hype and nightmare scenarios to address concrete questions. Artificial intelligence powers Google's search engine, enables Facebook to target advertising, and allows Alexa and Siri to do their jobs. AI is also behind self-driving cars, predictive policing, and autonomous weapons that can kill without human intervention. These and other AI applications raise complex ethical issues that are the subject of ongoing debate. This volume in the MIT Press Essential Knowledge series offers an accessible synthesis of these issues. Written by a philosopher of technology, AI Ethics goes beyond the usual hype and nightmare scenarios to address concrete questions. Mark Coeckelbergh describes influential AI narratives, ranging from Frankenstein's monster to transhumanism and the technological singularity. He surveys relevant philosophical discussions: questions about the fundamental differences between humans and machines and debates over the moral status of AI. He explains the technology of AI, describing different approaches and focusing on machine learning and data science. He offers an overview of important ethical issues, including privacy concerns, responsibility and the delegation of decision making, transparency, and bias as it arises at all stages of data science processes. He also considers the future of work in an AI economy. Finally, he analyzes a range of policy proposals and discusses challenges for policymakers. He argues for ethical practices that embed values in design, translate democratic values into practices and include a vision of the good life and the good society.
Access options:
The following links lead to the full text from the respective local libraries:
AbstractThis paper critically discusses blind spots in AI ethics. AI ethics discourses typically stick to a certain set of topics concerning principles evolving mainly around explainability, fairness, and privacy. All these principles can be framed in a way that enables their operationalization by technical means. However, this requires stripping down the multidimensionality of very complex social constructs to something that is idealized, measurable, and calculable. Consequently, rather conservative, mainstream notions of the mentioned principles are conveyed, whereas critical research, alternative perspectives, and non-ideal approaches are largely neglected. Hence, one part of the paper considers specific blind spots regarding the very topics AI ethics focusses on. The other part, then, critically discusses blind spots regarding to topics that hold significant ethical importance but are hardly or not discussed at all in AI ethics. Here, the paper focuses on negative externalities of AI systems, exemplarily discussing the casualization of clickwork, AI ethics' strict anthropocentrism, and AI's environmental impact. Ultimately, the paper is intended to be a critical commentary on the ongoing development of the field of AI ethics. It makes the case for a rediscovery of the strength of ethics in the AI field, namely its sensitivity to suffering and harms that are caused by and connected to AI technologies.
AbstractThis paper proposes to generate awareness for developing Artificial intelligence (AI) ethics by transferring knowledge from other fields of applied ethics, particularly from business ethics, stressing the role of organizations and processes of institutionalization. With the rapid development of AI systems in recent years, a new and thriving discourse on AI ethics has (re-)emerged, dealing primarily with ethical concepts, theories, and application contexts. We argue that business ethics insights may generate positive knowledge spillovers for AI ethics, given that debates on ethical and social responsibilities have been adopted as voluntary or mandatory regulations for organizations in both national and transnational contexts. Thus, business ethics may transfer knowledge from five core topics and concepts researched and institutionalized to AI ethics: (1) stakeholder management, (2) standardized reporting, (3) corporate governance and regulation, (4) curriculum accreditation, and as a unified topic (5) AI ethics washing derived from greenwashing. In outlining each of these five knowledge bridges, we illustrate current challenges in AI ethics and potential insights from business ethics that may advance the current debate. At the same time, we hold that business ethics can learn from AI ethics in catching up with the digital transformation, allowing for cross-fertilization between the two fields. Future debates in both disciplines of applied ethics may benefit from dialog and cross-fertilization, meant to strengthen the ethical depth and prevent ethics washing or, even worse, ethics bashing.
This book introduces readers to critical ethical concerns in the development and use of artificial intelligence. Offering clear and accessible information on central concepts and debates in AI ethics, it explores how related problems are now forcing us to address fundamental, age-old questions about human life, value, and meaning. In addition, the book shows how foundational and theoretical issues relate to concrete controversies, with an emphasis on understanding how ethical questions play out in practice. All topics are explored in depth, with clear explanations of relevant debates in ethics and philosophy, drawing on both historical and current sources. Questions in AI ethics are explored in the context of related issues in technology, regulation, society, religion, and culture, to help readers gain a nuanced understanding of the scope of AI ethics within broader debates and concerns. Written with both students and educators in mind, the book is easy to use, with key terms clearly explained, and numerous exercises designed to stretch and challenge. It offers readers essential insights into the evolving field of AI ethics. Moreover, it presents a range of methods and strategies that can be used to analyse and understand ethical questions, which are illustrated throughout with case studies
AbstractDue to the extensive progress of research in artificial intelligence (AI) as well as its deployment and application, the public debate on AI systems has also gained momentum in recent years. With the publication of the Ethics Guidelines for Trustworthy AI (2019), notions of trust and trustworthiness gained particular attention within AI ethics-debates; despite an apparent consensus that AI should be trustworthy, it is less clear what trust and trustworthiness entail in the field of AI. In this paper, I give a detailed overview on the notion of trust employed in AI Ethics Guidelines thus far. Based on that, I assess their overlaps and their omissions from the perspective of practical philosophy. I argue that, currently, AI ethics tends to overload the notion of trustworthiness. It thus runs the risk of becoming a buzzword that cannot be operationalized into a working concept for AI research. What is needed, however, is an approach that is also informed with findings of the research on trust in other fields, for instance, in social sciences and humanities, especially in the field of practical philosophy. This paper is intended as a step in this direction.
AbstractAs the awareness of AI's power and danger has risen, the dominant response has been a turn to ethical principles. A flood of AI guidelines and codes of ethics have been released in both the public and private sector in the last several years. However, these aremeaningless principleswhich are contested or incoherent, making them difficult to apply; they areisolated principlessituated in an industry and education system which largely ignores ethics; and they aretoothless principleswhich lack consequences and adhere to corporate agendas. For these reasons, I argue that AI ethical principles are useless, failing to mitigate the racial, social, and environmental damages of AI technologies in any meaningful sense. The result is a gap between high-minded principles and technological practice. Even when this gap is acknowledged and principles seek to be "operationalized," the translation from complex social concepts to technical rulesets is non-trivial. In a zero-sum world, the dominant turn to AI principles is not just fruitless but a dangerous distraction, diverting immense financial and human resources away from potentially more effective activity. I conclude by highlighting alternative approaches to AI justice that go beyond ethical principles: thinking more broadly about systems of oppression and more narrowly about accuracy and auditing.
AbstractThe paper suggests that AI ethics should pay attention to morally relevant systemic effects of AI use. It draws the attention of ethicists and practitioners to systemic risks that have been neglected so far in professional AI-related codes of conduct, industrial standards and ethical discussions more generally. The paper uses the financial industry as an example to ask: how can AI-enhanced systemic risks be ethically accounted for? Which specific issues does AI use raise for ethics that takes systemic effects into account? The paper (1) relates the literature about AI ethics to the ethics of systemic risks to clarify the moral relevance of AI use with respect to the imposition of systemic risks, (2) proposes a theoretical framework based on the ethics of complexity and (3) applies this framework to discuss implications for AI ethics concerned with AI-enhanced systemic risks.
AbstractThe development and deployment of artificial intelligence (AI) systems poses significant risks to society. To reduce these risks to an acceptable level, AI companies need an effective risk management process and sound risk governance. In this paper, we explore a particular way in which AI companies can improve their risk governance: by setting up an AI ethics board. We identify five key design choices: (1) What responsibilities should the board have? (2) What should its legal structure be? (3) Who should sit on the board? (4) How should it make decisions? (5) And what resources does it need? We break each of these questions down into more specific sub-questions, list options, and discuss how different design choices affect the board's ability to reduce societal risks from AI. Several failures have shown that designing an AI ethics board can be challenging. This paper provides a toolbox that can help AI companies to overcome these challenges.
In this chapter we argue that discourses on AI must transcend the language of 'ethics' and engage with power and political economy in order to constitute 'Good Data'. In particular, we must move beyond the depoliticised language of 'ethics' currently deployed (Wagner 2018) in determining whether AI is 'good' given the limitations of ethics as a frame through which AI issues can be viewed. In order to circumvent these limits, we use instead the language and conceptualisation of 'Good Data', as a more expansive term to elucidate the values, rights and interests at stake when it comes to AI's development and deployment, as well as that of other digital technologies. Good Data considerations move beyond recurring themes of data protection/privacy and the FAT (fairness, transparency and accountability) movement to include explicit political economy critiques of power. Instead of yet more ethics principles (that tend to say the same or similar things anyway), we offer four 'pillars' on which Good Data AI can be built: community, rights, usability and politics. Overall we view AI's 'goodness' as an explicly political (economy) question of power and one which is always related to the degree which AI is created and used to increase the wellbeing of society and especially to increase the power of the most marginalized and disenfranchised. We offer recommendations and remedies towards implementing 'better' approaches towards AI. Our strategies enable a different (but complementary) kind of evaluation of AI as part of the broader socio-technical systems in which AI is built and deployed.
The advantages of artificial intelligence are extensively discussed in specialized literature, which claim that technology has the power to fundamentally change society. However, rapid development of artificial intelligence does carry some serious risks, the most important of which is the spread of false and discriminatory information. Since artificial intelligence is "fed" with data from many sources, there is an increased risk that some of the data contains extremist or xenophobic literature. In such circumstances, artificial intelligence could spread extremely dangerous theories and ideas. Thus, government intervention is required to preserve control over the different data categories that developers have access to. As an example, we would like to bring up the fact that during testing, one of the most well-known AI interfaces, GPT-4, provided "advice" on how to murder a huge amount of people for a single dollar and what messages to promote in order to attract people to join Al-Qaeda.