Algorithm talk to me

Venema, Liesbeth

doi:10.1038/s41562-018-0314-7

Cited by 3 publications

(2 citation statements)

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“…Thirdly, another important research area for ethical decision-making by AI is to enable AI to explain its decisions under the framework of human ethics. The challenge here is that as deployed AI programs learn to update the decisionmaking logic, the AI designers may not be able to anticipate all outcomes at design time and may not understand the decisions made by the AI entities later [Venema, 2018]. Argumentation-based explainable AI [Fan and Toni, 2015;Langley et al, 2017] can be a good starting point for this purpose as it is well suited to the consequentialist ethics which is a commonly adopted approach for implementing AI ethics.…”

Section: Future Research Directionsmentioning

confidence: 99%

“…For example, an ethical autonomous gun system (if there can be such a thing) could be disabled by a child (who is generally regarded as a non-combatant and in need of protection) with a spray paint (which is generally not considered a lethal weapon) painting over the sensor system of the gun. In this case, Adversarial Game Theory [Vorobeychik et al, 2012] may need to be incorporated into future AI ethical decision frameworks in order to enable AI to preserve the original design objectives in the presence of strategic human behaviours.…”

Section: Future Research Directionsmentioning

confidence: 99%

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Building Ethics into Artificial Intelligence

Shen

Miao

et al. 2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

130

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As artificial intelligence (AI) systems become increasingly ubiquitous, the topic of AI governance for ethical decision-making by AI has captured public imagination. Within the AI research community, this topic remains less familiar to many researchers. In this paper, we complement existing surveys, which largely focused on the psychological, social and legal discussions of the topic, with an analysis of recent advances in technical solutions for AI governance. By reviewing publications in leading AI conferences including AAAI, AA-MAS, ECAI and IJCAI, we propose a taxonomy which divides the field into four areas: 1) exploring ethical dilemmas; 2) individual ethical decision frameworks; 3) collective ethical decision frameworks; and 4) ethics in human-AI interactions. We highlight the intuitions and key techniques used in each approach, and discuss promising future research directions towards successful integration of ethical AI systems into human societies.

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Section: Future Research Directionsmentioning

confidence: 99%

Section: Future Research Directionsmentioning

confidence: 99%

Building Ethics into Artificial Intelligence

Shen

Miao

et al. 2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

130

View full text Add to dashboard Cite

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Human–Algorithm Collaboration Works Best if Humans Lead (Because it is Fair!)

Cremer

McGuire

2021

Soc Just Res

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Autonomous algorithms are increasingly being used by organizations to reach ever increasing heights of organizational efficiency. The emerging business model of today therefore appears to be one where autonomous algorithms are gradually expanding their occupation into becoming a leading decision-maker, and humans by default become increasingly more subordinate to such decisions. We address the question of whether this business perspective is consistent with the sort of collaboration employees want to have with algorithms at work. We explored this question by investigating in what way humans preferred to collaborate with algorithms when making decisions. Using two experimental studies (Study 1, n = 237; Study 2, n = 684), we show that humans consider the collaboration with autonomous algorithms as unfair when the algorithm leads decision-making and will even incur high financial costs in order to avoid this. Our results also show that humans do not want to exclude algorithms entirely but seem to prefer a 60-40% human-algorithm partnership. These findings contrast the position taken by today's emerging business model on the issue of automated organizational decision-making. Our findings also provide support for the existence of an implicit theory-held by both present and future employees-that humans should lead and algorithms follow.

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Machines are not moral role models

Cremer

2022

Nat Hum Behav

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Algorithm talk to me

Cited by 3 publications

References 0 publications

Building Ethics into Artificial Intelligence

Building Ethics into Artificial Intelligence

Human–Algorithm Collaboration Works Best if Humans Lead (Because it is Fair!)

Machines are not moral role models

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