The global landscape of AI ethics guidelines

Published on Sep 2, 2019in Nature Machine Intelligence15.508
· DOI :10.1038/S42256-019-0088-2
Anna Jobin3
Estimated H-index: 3
(ETH Zurich),
Marcello Ienca16
Estimated H-index: 16
(ETH Zurich),
Effy Vayena33
Estimated H-index: 33
(ETH Zurich)
In the last five years, private companies, research institutions as well as public sector organisations have issued principles and guidelines for ethical AI, yet there is debate about both what constitutes "ethical AI" and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analyzed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted; why they are deemed important; what issue, domain or actors they pertain to; and how they should be implemented. Our findings highlight the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies.
📖 Papers frequently viewed together
3 Authors (Daniel Greene, ..., Luke Stark)
Jun 5, 2019 in ACL (Meeting of the Association for Computational Linguistics)
#1Emma Strubell (UMass: University of Massachusetts Amherst)H-Index: 15
#2Ananya Ganesh (UMass: University of Massachusetts Amherst)H-Index: 3
Last. Andrew McCallum (UMass: University of Massachusetts Amherst)H-Index: 112
view all 3 authors...
Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in accuracy across many NLP tasks. However, these accuracy improvements depend on the availability of exceptionally large computational resources that necessitate similarly substantial energy consumption. As a result these models are costly to train and develop, both financially, due to the cost of hardware a...
Technology companies are running a campaign to bend research and regulation for their benefit; society must fight back, says Yochai Benkler. Technology companies are running a campaign to bend research and regulation for their benefit; society must fight back, says Yochai Benkler.
Jan 27, 2019 in AAAI (National Conference on Artificial Intelligence)
#1Jess Whittlestone (University of Cambridge)H-Index: 6
#2Rune Nyrup (University of Cambridge)H-Index: 5
Last. Stephen Cave (University of Cambridge)H-Index: 10
view all 4 authors...
The last few years have seen a proliferation of principles for AI ethics. There is substantial overlap between different sets of principles, with widespread agreement that AI should be used for the common good, should not be used to harm people or undermine their rights, and should respect widely held values such as fairness, privacy, and autonomy. While articulating and agreeing on principles is important, it is only a starting point. Drawing on comparisons with the field of bioethics, we highl...
#1Daniel Greene (Microsoft)H-Index: 6
#2Anna Lauren Hoffmann (UW: University of Washington)H-Index: 9
Last. Luke Stark (Microsoft)H-Index: 12
view all 3 authors...
#1Raja Chatila (University of Paris)H-Index: 36
#2John C. Havens (University of Paris)H-Index: 4
The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems (A/IS) is a program of the IEEE initiated to address ethical issues raised by the development and dissemination of these systems. It identified over one hundred and twenty key issues and provided candidate recommendations to address them. In addition, it has provided the inspiration for fourteen approved standardization projects that are currently under development with the IEEE Standards Association.
#1Luciano Floridi (University of Oxford)H-Index: 72
#2Josh Cowls (University of Oxford)H-Index: 14
Last. Effy Vayena (ETH Zurich)H-Index: 33
view all 13 authors...
This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in oth...
#1Alan F. T. Winfield (University of the West of England)H-Index: 30
#2Marina Jirotka (University of Oxford)H-Index: 23
This paper explores the question of ethical governance for robotics and AI systems. We outline a roadmap – which links a number of elements including ethics, standards, regulation, responsible research and innovation and public engagement – as a framework to guide ethical governance in robotics and AI. We argue that ethical governance is essential to building public trust in robotics and AI, and conclude by proposing five pillars of good ethical governance.
#1Effy Vayena (ETH Zurich)H-Index: 33
#2Alessandro Blasimme (ETH Zurich)H-Index: 18
Last. I. Glenn Cohen (Harvard University)H-Index: 27
view all 3 authors...
Effy Vayena and colleagues argue that machine learning in medicine must offer data protection, algorithmic transparency, and accountability to earn the trust of patients and clinicians.
#1Edmond Awad (MIT: Massachusetts Institute of Technology)H-Index: 11
#2Sohan Dsouza (MIT: Massachusetts Institute of Technology)H-Index: 11
Last. Iyad Rahwan (MIT: Massachusetts Institute of Technology)H-Index: 50
view all 8 authors...
With the rapid development of artificial intelligence have come concerns about how machines will make moral decisions, and the major challenge of quantifying societal expectations about the ethical principles that should guide machine behaviour. To address this challenge, we deployed the Moral Machine, an online experimental platform designed to explore the moral dilemmas faced by autonomous vehicles. This platform gathered 40 million decisions in ten languages from millions of people in 233 cou...
Cited By454
#1Mona Ashok (University of Reading)H-Index: 5
#2Rohit Madan (University of Reading)H-Index: 1
Last. Uthayasankar Sivarajah (University of Bradford)H-Index: 15
view all 4 authors...
Abstract null null The use of Artificial Intelligence (AI) in Digital technologies (DT) is proliferating a profound socio-technical transformation. Governments and AI scholarship have endorsed key AI principles but lack direction at the implementation level. Through a systematic literature review of 59 papers, this paper contributes to the critical debate on the ethical use of AI in DTs beyond high-level AI principles. To our knowledge, this is the first paper that identifies 14 digital ethics i...
#1Jose Irizar (Heidelberg University)H-Index: 1
#1Ville Vakkuri (University of JyvÀskylÀ)H-Index: 6
#2Kai-Kristian Kemell (University of JyvÀskylÀ)H-Index: 6
Last. Pekka Abrahamsson (University of JyvÀskylÀ)H-Index: 55
view all 5 authors...
Abstract null null Artificial Intelligence (AI) systems are becoming increasingly widespread and exert a growing influence on society at large. The growing impact of these systems has also highlighted potential issues that may arise from their utilization, such as data privacy issues, resulting in calls for ethical AI systems. Yet, how to develop ethical AI systems remains an important question in the area. How should the principles and values be converted into requirements for these systems, an...
#1Andrea CossuH-Index: 3
#2Marta ZiosiH-Index: 1
Last. Vincenzo LomonacoH-Index: 13
view all 3 authors...
The increasing attention on Artificial Intelligence (AI) regulation has led to the definition of a set of ethical principles grouped into the Sustainable AI framework. In this article, we identify Continual Learning, an active area of AI research, as a promising approach towards the design of systems compliant with the Sustainable AI principles. While Sustainable AI outlines general desiderata for ethical applications, Continual Learning provides means to put such desiderata into practice.
#1Qinghua LuH-Index: 21
#2Liming ZhuH-Index: 31
Last. Conrad SandersonH-Index: 43
view all 6 authors...
Although artificial intelligence (AI) is solving real-world challenges and transforming industries, there are serious concerns about its ability to behave and make decisions in a responsible way. Many AI ethics principles and guidelines for responsible AI have been recently issued by governments, organisations, and enterprises. However, these AI ethics principles and guidelines are typically high-level and do not provide concrete guidance on how to design and develop responsible AI systems. To a...
#1Inga StrĂŒmke (NTNU: Norwegian University of Science and Technology)H-Index: 5
#2Marija SlavkovikH-Index: 11
Last. Vince I. MadaiH-Index: 15
view all 3 authors...
While the demand for ethical artificial intelligence (AI) systems increases, the number of unethical uses of AI accelerates, even though there is no shortage of ethical guidelines. We argue that a possible underlying cause for this is that AI developers face a social dilemma in AI development ethics, preventing the widespread adaptation of ethical best practices. We define the social dilemma for AI development and describe why the current crisis in AI development ethics cannot be solved without ...
#1David De CremerH-Index: 75
Last. Jack McGuireH-Index: 2
view all 5 authors...
#1Praveen K. Kopalle (Dartmouth College)H-Index: 31
#2Manish Gangwar (Indian School of Business)H-Index: 7
Last. Aric Rindfleisch (UIUC: University of Illinois at Urbana–Champaign)H-Index: 32
view all 6 authors...
Abstract null null Artificial intelligence (AI) has captured substantial interest from a wide array of marketing scholars in recent years. Our research contributes to this emerging domain by examining AI technologies in marketing via a global lens. Specifically, our lens focuses on three levels of analysis: country, company, and consumer. Our country-level analysis emphasizes the heterogeneity in economic inequality across countries due to the considerable economic resources necessary for AI ado...
This website uses cookies.
We use cookies to improve your online experience. By continuing to use our website we assume you agree to the placement of these cookies.
To learn more, you can find in our Privacy Policy.