A high-level overview of AI ethics

Artificial intelligence (AI) ethics is a field that has emerged as a response to the growing concern regarding the impact of AI. It can be read as a nascent field and as a subset of the wider field of digital ethics, which addresses concerns raised by the development and deployment of new digital technologies, such as AI, big data analytics, and blockchain technologies. The principle aim of this article is to provide a high-level conceptual discussion of the field by way of introducing basic concepts and sketching approaches and central themes in AI ethics. The first part introduces concepts by noting what is being referred to by “AI” and “ethics”, etc.; the second part explores some predecessors to AI ethics, namely engineering ethics, philosophy of technology, and science and technology studies; the third part discusses three current approaches to AI ethics namely, principles, processes, and ethical consciousness; and finally, the fourth part discusses central themes in translating ethics in to engineering practice. We conclude by summarizing and noting the inherent interdisciplinary future directions and debates in AI ethics

Philosophical foundations for digital ethics and AI Ethics: a dignitarian approach

AI Ethics is a burgeoning and relatively new field that has emerged in response to growing concerns about the impact of artificial intelligence (AI) on human individuals and their social institutions. In turn, AI ethics is a part of the broader field of digital ethics, which addresses similar concerns generated by the development and deployment of new digital technologies. Here, we tackle the important worry that digital ethics in general, and AI ethics in particular, lack adequate philosophical foundations. In direct response to that worry, we formulate and rationally justify some basic concepts and principles for digital ethics/AI ethics, all drawn from a broadly Kantian theory of human dignity. Our argument, which is designed to be relatively compact and easily accessible, is presented in ten distinct steps: (1) what "digital ethics" and "AI ethics" mean, (2) refuting the dignity-skeptic, (3) the metaphysics of human dignity, (4) human happiness or flourishing, true human needs, and human dignity, (5) our moral obligations with respect to all human real persons, (6) what a natural automaton or natural machine is, (7) why human real persons are not natural automata/natural machines: because consciousness is a form of life, (8) our moral obligations with respect to the design and use of artificial automata or artificial machines, aka computers, and digital technology more generally, (9) what privacy is, why invasions of digital privacy are morally impermissible, whereas consensual entrances into digital privacy are either morally permissible or even obligatory, and finally (10) dignitarian morality versus legality, and digital ethics/AI ethics. We conclude by asserting our strongly-held belief that a well-founded and generally-accepted dignitarian digital ethics/AI ethics is of global existential importance for humanity

Towards a Feminist Metaethics of AI

The proliferation of Artificial Intelligence (AI) has sparked an overwhelming number of AI ethics guidelines, boards and codes of conduct. These outputs primarily analyse competing theories, principles and values for AI development and deployment. However, as a series of recent problematic incidents about AI ethics/ethicists demonstrate, this orientation is insufficient. Before proceeding to evaluate other professions, AI ethicists should critically evaluate their own; yet, such an evaluation should be more explicitly and systematically undertaken in the literature. I argue that these insufficiencies could be mitigated by developing a research agenda for a feminist metaethics of AI. Contrary to traditional metaethics, which reflects on the nature of morality and moral judgements in a non-normative way, feminist metaethics expands its scope to ask not only what ethics is but also what our engagement with it should be like. Applying this perspective to the context of AI, I suggest that a feminist metaethics of AI would examine: (i) the continuity between theory and action in AI ethics; (ii) the real-life effects of AI ethics; (iii) the role and profile of those involved in AI ethics; and (iv) the effects of AI on power relations through methods that pay attention to context, emotions and narrative.Comment: In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES' 22), August 1-3, 2022, Oxford, United Kingdom. ACM, New York, NY, USA, 10 page

Tekoälyn etiikka soveltavana etiikkana

The need to design and develop artificial intelligence (AI) in a sustainable manner has motivated researchers, institutions, and organizations to formulate suggestions for AI ethics. Although these suggestions cover various topics and address diverse audiences, they share the presupposition that AI ethics provides a generalizable basis for designers that is applicable to their work. We propose that one of the reasons the influence of current ethical codes has remained modest, may be the conception of the applied ethics that they represent. We discuss bioethics as a point of reference for weighing the metaethical and methodological approaches adopted in AI ethics, and propose that AI ethics could be made more methodologically solid and substantively more influential if the resources were enriched by adopting tools from fields of study created to improve the quality of human action and safeguard its desired outcomes. The approaches we consider to be useful for this purpose are the systems theory, safety research, impact assessment approach, and theory of change.Peer reviewe

Teaching AI Ethics: Observations and Challenges

This report summarises the experience in teaching Artificial Intelligence (AI) Ethics as an elective masters level course at the University of Bergen. The goal of the summary is twofold: 1) to draw lessons for teaching this in-high demand very new discipline; 2) to serve as a basis in developing a bachelor level AI Ethics course for students of artificial intelligence. AI Ethics as a topic is particularly challenging to teach as the discipline itself is very new and no textbooks have been established. The added challenge is introducing methodologies and skills from humanity- and social sciences to students of computational and information sciences.publishedVersio

Teaching AI Ethics: Observations and Challenges

This report summarises the experience in teaching Artificial Intelligence (AI) Ethics as an elective masters level course at the University of Bergen. The goal of the summary is twofold: 1) to draw lessons for teaching this in-high demand very new discipline; 2) to serve as a basis in developing a bachelor level AI Ethics course for students of artificial intelligence. AI Ethics as a topic is particularly challenging to teach as the discipline itself is very new and no textbooks have been established. The added challenge is introducing methodologies and skills from humanity- and social sciences to students of computational and information sciences

Ethical Perspectives in AI: A Two-folded Exploratory Study From Literature and Active Development Projects

Background: Interest in Artificial Intelligence (AI) based systems has been gaining traction at a fast pace, both for software development teams and for society as a whole. This increased interest has lead to the employment of AI techniques such as Machine Learning and Deep Learning for diverse purposes, like medicine and surveillance systems, and such uses have raised the awareness about the ethical implications of the usage of AI systems. Aims: With this work we aim to obtain an overview of the current state of the literature and software projects on tools, methods and techniques used in practical AI ethics. Method: We have conducted an exploratory study in both a scientific database and a software projects repository in order to understand their current state on techniques, methods and tools used for implementing AI ethics. Results: A total of 182 abstracts were retrieved and five classes were devised from the analysis in Scopus, 1) AI in Agile and Business for Requirement Engineering (RE) (22.8%), 2) RE in Theoretical Context (14.8%), 3) Quality Requirements (22.6%), 4) Proceedings and Conferences (22%), 5) AI in Requirements Engineering (17.8%). Furthermore, out of 589 projects from GitHub, we found 21 tools for implementing AI ethics. Highlighted publicly available tools found to assist the implementation of AI ethics are InterpretML, Deon and TransparentAI. Conclusions: The combined energy of both explored sources fosters an enhanced debate and stimulates progress towards AI ethics in practice