26,483 research outputs found
AI for the Common Good?! Pitfalls, challenges, and Ethics Pen-Testing
Recently, many AI researchers and practitioners have embarked on research
visions that involve doing AI for "Good". This is part of a general drive
towards infusing AI research and practice with ethical thinking. One frequent
theme in current ethical guidelines is the requirement that AI be good for all,
or: contribute to the Common Good. But what is the Common Good, and is it
enough to want to be good? Via four lead questions, I will illustrate
challenges and pitfalls when determining, from an AI point of view, what the
Common Good is and how it can be enhanced by AI. The questions are: What is the
problem / What is a problem?, Who defines the problem?, What is the role of
knowledge?, and What are important side effects and dynamics? The illustration
will use an example from the domain of "AI for Social Good", more specifically
"Data Science for Social Good". Even if the importance of these questions may
be known at an abstract level, they do not get asked sufficiently in practice,
as shown by an exploratory study of 99 contributions to recent conferences in
the field. Turning these challenges and pitfalls into a positive
recommendation, as a conclusion I will draw on another characteristic of
computer-science thinking and practice to make these impediments visible and
attenuate them: "attacks" as a method for improving design. This results in the
proposal of ethics pen-testing as a method for helping AI designs to better
contribute to the Common Good.Comment: to appear in Paladyn. Journal of Behavioral Robotics; accepted on
27-10-201
Principles alone cannot guarantee ethical AI
AI Ethics is now a global topic of discussion in academic and policy circles.
At least 84 public-private initiatives have produced statements describing
high-level principles, values, and other tenets to guide the ethical
development, deployment, and governance of AI. According to recent
meta-analyses, AI Ethics has seemingly converged on a set of principles that
closely resemble the four classic principles of medical ethics. Despite the
initial credibility granted to a principled approach to AI Ethics by the
connection to principles in medical ethics, there are reasons to be concerned
about its future impact on AI development and governance. Significant
differences exist between medicine and AI development that suggest a principled
approach in the latter may not enjoy success comparable to the former. Compared
to medicine, AI development lacks (1) common aims and fiduciary duties, (2)
professional history and norms, (3) proven methods to translate principles into
practice, and (4) robust legal and professional accountability mechanisms.
These differences suggest we should not yet celebrate consensus around
high-level principles that hide deep political and normative disagreement.Comment: A previous, pre-print version of this paper was entitled 'AI Ethics -
Too Principled to Fail?
Artificial Intelligence and Patient-Centered Decision-Making
Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, calculations, and procedures cannot be meaningfully understood by human practitioners. When AI systems reach this level of complexity, we can also speak of black-box medicine. In this paper, we want to argue that black-box medicine conflicts with core ideals of patient-centered medicine. In particular, we claim, black-box medicine is not conducive for supporting informed decision-making based on shared information, shared deliberation, and shared mind between practitioner and patient
"Revolution? What Revolution?" Successes and limits of computing technologies in philosophy and religion
Computing technologies like other technological innovations in the modern West are inevitably introduced with the rhetoric of "revolution". Especially during the 1980s (the PC revolution) and 1990s (the Internet and Web revolutions), enthusiasts insistently celebrated radical changes— changes ostensibly inevitable and certainly as radical as those brought about by the invention of the printing press, if not the discovery of fire.\ud
These enthusiasms now seem very "1990s�—in part as the revolution stumbled with the dot.com failures and the devastating impacts of 9/11. Moreover, as I will sketch out below, the patterns of diffusion and impact in philosophy and religion show both tremendous success, as certain revolutionary promises are indeed kept—as well as (sometimes spectacular) failures. Perhaps we use revolutionary rhetoric less frequently because the revolution has indeed succeeded: computing technologies, and many of the powers and potentials they bring us as scholars and religionists have become so ubiquitous and normal that they no longer seem "revolutionary at all. At the same time, many of the early hopes and promises instantiated in such specific projects as Artificial Intelligence and anticipations of virtual religious communities only have been dashed against the apparently intractable limits of even these most remarkable technologies. While these failures are usually forgotten they leave in their wake a clearer sense of what these new technologies can, and cannot do
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
Time for AI (Ethics) maturity model is now
Publisher Copyright: Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).There appears to be a common agreement that ethical concerns are of high importance when it comes to systems equipped with some sort of Artificial Intelligence (AI). Demands for ethical AI are declared from all directions. As a response, in recent years, public bodies, governments, and universities have rushed in to provide a set of principles to be considered when AI based systems are designed and used. We have learned, however, that high-level principles do not turn easily into actionable advice for practitioners. Hence, also companies are publishing their own ethical guidelines to guide their AI development. This paper argues that AI software is still software and needs to be approached from the software development perspective. The software engineering paradigm has introduced maturity model thinking, which provides a roadmap for companies to improve their performance from the selected viewpoints known as the key capabilities. We want to voice out a call for action for the development of a maturity model for AI software. We wish to discuss whether the focus should be on AI ethics or, more broadly, the quality of an AI system, called a maturity model for the development of AI systems.Peer reviewe
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