publication . Other literature type . Preprint . Report . 2019

Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector

Leslie, David;
Open Access English
  • Published: 11 Jun 2019
  • Publisher: Zenodo
Abstract
A remarkable time of human promise has been ushered in by the convergence of the ever-expanding availability of big data, the soaring speed and stretch of cloud computing platforms, and the advancement of increasingly sophisticated machine learning algorithms. Innovations in AI are already leaving a mark on government, by improving the provision of essential social goods and services from healthcare, education, and transportation to food supply, energy, and environmental management. These bounties are likely just the start. The prospect that progress in AI will help government to confront some of its most urgent challenges is exciting, but legitimate worries abo...
Subjects
ACM Computing Classification System: GeneralLiterature_MISCELLANEOUS
free text keywords: The Alan Turing Institute, Public policy, AI, Ethics, Safety, Guidance, Government, Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Applications
Download fromView all 4 versions
Zenodo
Other literature type . 2019
Provider: Datacite
Zenodo
Other literature type . 2019
Provider: Datacite
ZENODO
Report . 2019
Provider: ZENODO
127 references, page 1 of 9

Access Now. (2018). The Toronto declaration: Protecting the rights to equality and non-discrimination in machine learning systems. Retrieved from https://www.accessnow.org/cms/assets/uploads /2018/08/The-Toronto-Declaration_ENG_08-2018.pdf

Adamson, G., Havens, J. C., & Chatila, R. (2019). Designing a value-driven future for ethical autonomous and intelligent systems. Proceedings of the IEEE, 107(3), 518-525. https://doi.org/10.1109 /JPROC.2018.2884923 [OpenAIRE]

American Medical Association. (2001). AMA code of medical ethics. Retrieved from https://www.amaassn.org/sites/ama-assn.org/files/corp/media-browser/principles-of-medical-ethics.pdf

American Psychological Association. (2016). Ethical principles of psychologists and code of conduct. Retrieved from https://www.apa.org/ethics/code/ [OpenAIRE]

Article 19. (2019). Governance with teeth: How human rights can strengthen FAT and ethics initiatives on artificial intelligence. Retrieved from https://www.article19.org/resources/governance-with-teeth-howhuman-rights-can-strengthen-fat-and-ethics-initiatives-on-artificial-intelligence/

Beauchamp, T. L., & Childress, J. F. (2009). Principles of biomedical ethics. 6th edition. Oxford University Press, USA.

Cath, C. (2018). Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180080. https://doi.org/10.1098/rsta.2018.0080 [OpenAIRE]

Binns, R. (2017). Fairness in machine learning: Lessons from political philosophy. arXiv:1712.03586. Retrieved from https://arxiv.org/abs/1712.03586

Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., & Shadbolt, N. (2018). 'It's reducing a human being to a percentage': Perceptions of justice in algorithmic decisions. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (p. 377). ACM. Retrieved from https://dl.acm.org /citation.cfm?id=3173951 [OpenAIRE]

Holstein, K., Vaughan, J. W., Daumé III, H., Dudík, M., & Wallach, H. (2018). Improving fairness in machine learning systems: What do industry practitioners need?. ArXiv:1812.05239. https://doi.org/10.1145 /3290605.3300830

Lepri, B., Oliver, N., Letouzé, E., Pentland, A., & Vinck, P. (2018). Fair, transparent, and accountable algorithmic decision-making processes: The premise, the proposed solutions, and the open challenges. Philosophy & Technology, 31(4), 611-627. https://doi.org/10.1007/s13347-017-0279-x

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 205395171667967. https://doi.org/10.1177/2053951716679679 [OpenAIRE]

Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 59-68). ACM. Retrieved from https://dl.acm.org/citation.cfm?id=3287598 [OpenAIRE]

Suresh, H., & Guttag, J. V. (2019). A Framework for Understanding Unintended Consequences of Machine Learning. arXiv:1901.10002. Retrieved from https://arxiv.org/abs/1901.10002 [OpenAIRE]

Veale, M., Van Kleek, M., & Binns, R. (2018). Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (p. 440). ACM. Retrieved from https://dl.acm.org /citation.cfm?id=3174014 [OpenAIRE]

127 references, page 1 of 9
Abstract
A remarkable time of human promise has been ushered in by the convergence of the ever-expanding availability of big data, the soaring speed and stretch of cloud computing platforms, and the advancement of increasingly sophisticated machine learning algorithms. Innovations in AI are already leaving a mark on government, by improving the provision of essential social goods and services from healthcare, education, and transportation to food supply, energy, and environmental management. These bounties are likely just the start. The prospect that progress in AI will help government to confront some of its most urgent challenges is exciting, but legitimate worries abo...
Subjects
ACM Computing Classification System: GeneralLiterature_MISCELLANEOUS
free text keywords: The Alan Turing Institute, Public policy, AI, Ethics, Safety, Guidance, Government, Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Applications
Download fromView all 4 versions
Zenodo
Other literature type . 2019
Provider: Datacite
Zenodo
Other literature type . 2019
Provider: Datacite
ZENODO
Report . 2019
Provider: ZENODO
127 references, page 1 of 9

Access Now. (2018). The Toronto declaration: Protecting the rights to equality and non-discrimination in machine learning systems. Retrieved from https://www.accessnow.org/cms/assets/uploads /2018/08/The-Toronto-Declaration_ENG_08-2018.pdf

Adamson, G., Havens, J. C., & Chatila, R. (2019). Designing a value-driven future for ethical autonomous and intelligent systems. Proceedings of the IEEE, 107(3), 518-525. https://doi.org/10.1109 /JPROC.2018.2884923 [OpenAIRE]

American Medical Association. (2001). AMA code of medical ethics. Retrieved from https://www.amaassn.org/sites/ama-assn.org/files/corp/media-browser/principles-of-medical-ethics.pdf

American Psychological Association. (2016). Ethical principles of psychologists and code of conduct. Retrieved from https://www.apa.org/ethics/code/ [OpenAIRE]

Article 19. (2019). Governance with teeth: How human rights can strengthen FAT and ethics initiatives on artificial intelligence. Retrieved from https://www.article19.org/resources/governance-with-teeth-howhuman-rights-can-strengthen-fat-and-ethics-initiatives-on-artificial-intelligence/

Beauchamp, T. L., & Childress, J. F. (2009). Principles of biomedical ethics. 6th edition. Oxford University Press, USA.

Cath, C. (2018). Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180080. https://doi.org/10.1098/rsta.2018.0080 [OpenAIRE]

Binns, R. (2017). Fairness in machine learning: Lessons from political philosophy. arXiv:1712.03586. Retrieved from https://arxiv.org/abs/1712.03586

Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., & Shadbolt, N. (2018). 'It's reducing a human being to a percentage': Perceptions of justice in algorithmic decisions. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (p. 377). ACM. Retrieved from https://dl.acm.org /citation.cfm?id=3173951 [OpenAIRE]

Holstein, K., Vaughan, J. W., Daumé III, H., Dudík, M., & Wallach, H. (2018). Improving fairness in machine learning systems: What do industry practitioners need?. ArXiv:1812.05239. https://doi.org/10.1145 /3290605.3300830

Lepri, B., Oliver, N., Letouzé, E., Pentland, A., & Vinck, P. (2018). Fair, transparent, and accountable algorithmic decision-making processes: The premise, the proposed solutions, and the open challenges. Philosophy & Technology, 31(4), 611-627. https://doi.org/10.1007/s13347-017-0279-x

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 205395171667967. https://doi.org/10.1177/2053951716679679 [OpenAIRE]

Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 59-68). ACM. Retrieved from https://dl.acm.org/citation.cfm?id=3287598 [OpenAIRE]

Suresh, H., & Guttag, J. V. (2019). A Framework for Understanding Unintended Consequences of Machine Learning. arXiv:1901.10002. Retrieved from https://arxiv.org/abs/1901.10002 [OpenAIRE]

Veale, M., Van Kleek, M., & Binns, R. (2018). Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (p. 440). ACM. Retrieved from https://dl.acm.org /citation.cfm?id=3174014 [OpenAIRE]

127 references, page 1 of 9
Powered by OpenAIRE Open Research Graph
Any information missing or wrong?Report an Issue
publication . Other literature type . Preprint . Report . 2019

Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector

Leslie, David;