Why teaching ethics to AI practitioners is important

Judy Goldsmith; Emanuelle Burton

Why teaching ethics to AI practitioners is important

Judy Goldsmith
, Emanuelle Burton

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

We argue that it is crucial to the future of AI that our students be trained in multiple complementary modes of ethical reasoning, so that they may make ethical design and implementation choices, ethical career decisions, and that their software will be programmed to take into account the complexities of acting ethically in the world.

Original language	English
Title of host publication	WS-17-01
Subtitle of host publication	Artificial Intelligence and Operations Research for Social Good; WS-17-02: Artificial Intelligence, Ethics, and Society; WS-17-03: Artificial Intelligence for Connected and Automated Vehicles; WS-17-04: Artificial Intelligence for Cyber Security; WS-17-05: Artificial Intelligence for Smart Grids and Buildings; WS-17-06: Computer Poker and Imperfect Information Games; WS-17-07: Crowdsourcing, Deep Learning and Artificial Intelligence Agents; WS-17-08: Distributed Machine Learning; WS-17-09: Joint Workshop on Health Intelligence; WS-17-10: Human-Aware Artificial Intelligence; WS-17-11: Human-Machine Collaborative Learning; WS-17-12: Knowledge-Based Techniques for Problem Solving and Reasoning; WS-17-13: Plan, Activity, and Intent Recognition; WS-17-14: Symbolic Inference and Optimization; WS-17-15: What's Next for AI in Games?
Pages	110-114
Number of pages	5
ISBN (Electronic)	9781577357865
State	Published - 2017
Event	31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States Duration: Feb 4 2017 → Feb 10 2017

Publication series

Name	AAAI Workshop - Technical Report
Volume	WS-17-01 - WS-17-15

Conference

Conference	31st AAAI Conference on Artificial Intelligence, AAAI 2017
Country/Territory	United States
City	San Francisco
Period	2/4/17 → 2/10/17

Bibliographical note

Publisher Copyright:
© Copyright 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Funding

This material is based upon work supported by the National Science Foundation under Grant No. 1646887.

Funders	Funder number
National Science Foundation (NSF)	1646887

ASJC Scopus subject areas

General Engineering

Cite this

Goldsmith, J., & Burton, E. (2017). Why teaching ethics to AI practitioners is important. In WS-17-01: Artificial Intelligence and Operations Research for Social Good; WS-17-02: Artificial Intelligence, Ethics, and Society; WS-17-03: Artificial Intelligence for Connected and Automated Vehicles; WS-17-04: Artificial Intelligence for Cyber Security; WS-17-05: Artificial Intelligence for Smart Grids and Buildings; WS-17-06: Computer Poker and Imperfect Information Games; WS-17-07: Crowdsourcing, Deep Learning and Artificial Intelligence Agents; WS-17-08: Distributed Machine Learning; WS-17-09: Joint Workshop on Health Intelligence; WS-17-10: Human-Aware Artificial Intelligence; WS-17-11: Human-Machine Collaborative Learning; WS-17-12: Knowledge-Based Techniques for Problem Solving and Reasoning; WS-17-13: Plan, Activity, and Intent Recognition; WS-17-14: Symbolic Inference and Optimization; WS-17-15: What's Next for AI in Games? (pp. 110-114). (AAAI Workshop - Technical Report; Vol. WS-17-01 - WS-17-15).

Goldsmith, Judy ; Burton, Emanuelle. / Why teaching ethics to AI practitioners is important. WS-17-01: Artificial Intelligence and Operations Research for Social Good; WS-17-02: Artificial Intelligence, Ethics, and Society; WS-17-03: Artificial Intelligence for Connected and Automated Vehicles; WS-17-04: Artificial Intelligence for Cyber Security; WS-17-05: Artificial Intelligence for Smart Grids and Buildings; WS-17-06: Computer Poker and Imperfect Information Games; WS-17-07: Crowdsourcing, Deep Learning and Artificial Intelligence Agents; WS-17-08: Distributed Machine Learning; WS-17-09: Joint Workshop on Health Intelligence; WS-17-10: Human-Aware Artificial Intelligence; WS-17-11: Human-Machine Collaborative Learning; WS-17-12: Knowledge-Based Techniques for Problem Solving and Reasoning; WS-17-13: Plan, Activity, and Intent Recognition; WS-17-14: Symbolic Inference and Optimization; WS-17-15: What's Next for AI in Games?. 2017. pp. 110-114 (AAAI Workshop - Technical Report).

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title = "Why teaching ethics to AI practitioners is important",

abstract = "We argue that it is crucial to the future of AI that our students be trained in multiple complementary modes of ethical reasoning, so that they may make ethical design and implementation choices, ethical career decisions, and that their software will be programmed to take into account the complexities of acting ethically in the world.",

author = "Judy Goldsmith and Emanuelle Burton",

note = "Publisher Copyright: {\textcopyright} Copyright 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; 31st AAAI Conference on Artificial Intelligence, AAAI 2017 ; Conference date: 04-02-2017 Through 10-02-2017",

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Goldsmith, J & Burton, E 2017, Why teaching ethics to AI practitioners is important. in WS-17-01: Artificial Intelligence and Operations Research for Social Good; WS-17-02: Artificial Intelligence, Ethics, and Society; WS-17-03: Artificial Intelligence for Connected and Automated Vehicles; WS-17-04: Artificial Intelligence for Cyber Security; WS-17-05: Artificial Intelligence for Smart Grids and Buildings; WS-17-06: Computer Poker and Imperfect Information Games; WS-17-07: Crowdsourcing, Deep Learning and Artificial Intelligence Agents; WS-17-08: Distributed Machine Learning; WS-17-09: Joint Workshop on Health Intelligence; WS-17-10: Human-Aware Artificial Intelligence; WS-17-11: Human-Machine Collaborative Learning; WS-17-12: Knowledge-Based Techniques for Problem Solving and Reasoning; WS-17-13: Plan, Activity, and Intent Recognition; WS-17-14: Symbolic Inference and Optimization; WS-17-15: What's Next for AI in Games?. AAAI Workshop - Technical Report, vol. WS-17-01 - WS-17-15, pp. 110-114, 31st AAAI Conference on Artificial Intelligence, AAAI 2017, San Francisco, United States, 2/4/17.

Why teaching ethics to AI practitioners is important. / Goldsmith, Judy; Burton, Emanuelle.
WS-17-01: Artificial Intelligence and Operations Research for Social Good; WS-17-02: Artificial Intelligence, Ethics, and Society; WS-17-03: Artificial Intelligence for Connected and Automated Vehicles; WS-17-04: Artificial Intelligence for Cyber Security; WS-17-05: Artificial Intelligence for Smart Grids and Buildings; WS-17-06: Computer Poker and Imperfect Information Games; WS-17-07: Crowdsourcing, Deep Learning and Artificial Intelligence Agents; WS-17-08: Distributed Machine Learning; WS-17-09: Joint Workshop on Health Intelligence; WS-17-10: Human-Aware Artificial Intelligence; WS-17-11: Human-Machine Collaborative Learning; WS-17-12: Knowledge-Based Techniques for Problem Solving and Reasoning; WS-17-13: Plan, Activity, and Intent Recognition; WS-17-14: Symbolic Inference and Optimization; WS-17-15: What's Next for AI in Games?. 2017. p. 110-114 (AAAI Workshop - Technical Report; Vol. WS-17-01 - WS-17-15).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N1 - Publisher Copyright: © Copyright 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.

PY - 2017

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M3 - Conference contribution

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Goldsmith J, Burton E. Why teaching ethics to AI practitioners is important. In WS-17-01: Artificial Intelligence and Operations Research for Social Good; WS-17-02: Artificial Intelligence, Ethics, and Society; WS-17-03: Artificial Intelligence for Connected and Automated Vehicles; WS-17-04: Artificial Intelligence for Cyber Security; WS-17-05: Artificial Intelligence for Smart Grids and Buildings; WS-17-06: Computer Poker and Imperfect Information Games; WS-17-07: Crowdsourcing, Deep Learning and Artificial Intelligence Agents; WS-17-08: Distributed Machine Learning; WS-17-09: Joint Workshop on Health Intelligence; WS-17-10: Human-Aware Artificial Intelligence; WS-17-11: Human-Machine Collaborative Learning; WS-17-12: Knowledge-Based Techniques for Problem Solving and Reasoning; WS-17-13: Plan, Activity, and Intent Recognition; WS-17-14: Symbolic Inference and Optimization; WS-17-15: What's Next for AI in Games?. 2017. p. 110-114. (AAAI Workshop - Technical Report).

Why teaching ethics to AI practitioners is important

Abstract

Publication series

Conference

Bibliographical note

Funding

ASJC Scopus subject areas

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Cite this