Building Ethics into Artificial Intelligence

Yu, Han; Shen, Zhiqi; Miao, Yuan; Leung, Cyril; Lesser, Victor; Yang, Qiang

doi:10.24963/ijcai.2018/779

Cited by 131 publications

(74 citation statements)

References 12 publications

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“…Zhang and Conitzer [30] showed that many classical results from Probably Approximately Correct (PAC) learning can be applied to the preference aggregation framework. For a comprehensive study of more related work 2 on AI ethics, interested readers can refer to a recent survey by Yu et al [29].…”

Section: Related Workmentioning

confidence: 99%

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Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas

Wang

Zhao

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

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With the rapid development of artificial intelligence (AI), ethical issues surrounding AI have attracted increasing attention. In particular, autonomous vehicles may face moral dilemmas in accident scenarios, such as staying the course resulting in hurting pedestrians or swerving leading to hurting passengers. To investigate such ethical dilemmas, recent studies have adopted preference aggregation, in which each voter expresses her/his preferences over decisions for the possible ethical dilemma scenarios, and a centralized system aggregates these preferences to obtain the winning decision. Although a useful methodology for building ethical AI systems, such an approach can potentially violate the privacy of voters since moral preferences are sensitive information and their disclosure can be exploited by malicious parties resulting in negative consequences. In this paper, we report a firstof-its-kind privacy-preserving crowd-guided AI decision-making approach in ethical dilemmas. We adopt the formal and popular notion of differential privacy to quantify privacy, and consider four granularities of privacy protection by taking voter-/recordlevel privacy protection and centralized/distributed perturbation into account, resulting in four approaches VLCP, RLCP, VLDP, and RLDP, respectively. Moreover, we propose different algorithms to achieve these privacy protection granularities, while retaining the accuracy of the learned moral preference model. Specifically, VLCP and RLCP are implemented with the data aggregator setting a universal privacy parameter and perturbing the averaged moral preference to protect the privacy of voters' data. VLDP and RLDP are implemented in such a way that each voter perturbs her/his

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Section: Related Workmentioning

confidence: 99%

“…One of the fundamental questions in AI ethics is how to allow humans to guide AI to make moral decisions when faced with ethical dilemmas. An ethical dilemma is a situation in which any decision violates certain aspects of ethics [29]. The following is a concrete example of an ethical dilemma that autonomous vehicles encounter [16].…”

Section: Introductionmentioning

confidence: 99%

Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas

Wang

Zhao

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

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“…incorporates the ethical considerations (Yu et al 2018) by prioritizing worker wellbeing considerations through recommending opportunistic rest breaks, and allowing stakeholders to influence the AI recommendations by expressing their preferences through φ and m i (t). The recommendations from CPL are given to a worker in the form of the number of tasks he should complete over a given time slot (a 0 value indicates that the worker should rest) so as to make it actionable enough for the worker to follow.…”

Section: Wrimentioning

confidence: 99%

“…From an ethically aligned design perspective (The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems 2018), it is desirable to incorporate breaks into scheduling approaches in order to prioritize workers' wellbeing. Nevertheless, as highlighted in (Yu et al 2018), ethically aligned Copyright c 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.…”

Section: Introductionmentioning

confidence: 99%

Ethically Aligned Opportunistic Scheduling for Productive Laziness

Miao

Zheng

et al. 2019

Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society

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In artificial intelligence (AI) mediated workforce management systems (e.g., crowdsourcing), long-term success depends on workers accomplishing tasks productively and resting well. This dual objective can be summarized by the concept of productive laziness. Existing scheduling approaches mostly focus on efficiency but overlook worker wellbeing through proper rest. In order to enable workforce management systems to follow the IEEE Ethically Aligned Design guidelines to prioritize worker wellbeing, we propose a distributed Computational Productive Laziness (CPL) approach in this paper. It intelligently recommends personalized workrest schedules based on local data concerning a worker's capabilities and situational factors to incorporate opportunistic resting and achieve superlinear collective productivity without the need for explicit coordination messages. Extensive experiments based on a real-world dataset of over 5,000 workers demonstrate that CPL enables workers to spend 70% of the effort to complete 90% of the tasks on average, providing more ethically aligned scheduling than existing approaches.

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“…As for the ethical guidelines, the idea of teaching machines right from wrong has become an important research topic in both AI (Yu et al 2018) and in other disciplines (Wallach and Allen 2008). Much of the research at the intersection of AI and ethics falls under the heading of machine ethics, i.e., adding ethics and/or constraints to a particular system's decision making process (Anderson and Anderson 2011).…”

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confidence: 99%

Building Ethically Bounded AI

Rossi¹,

Mattei

2019

AAAI

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The more AI agents are deployed in scenarios with possibly unexpected situations, the more they need to be flexible, adaptive, and creative in achieving the goal we have given them. Thus, a certain level of freedom to choose the best path to the goal is inherent in making AI robust and flexible enough. At the same time, however, the pervasive deployment of AI in our life, whether AI is autonomous or collaborating with humans, raises several ethical challenges. AI agents should be aware and follow appropriate ethical principles and should thus exhibit properties such as fairness or other virtues. These ethical principles should define the boundaries of AI’s freedom and creativity. However, it is still a challenge to understand how to specify and reason with ethical boundaries in AI agents and how to combine them appropriately with subjective preferences and goal specifications. Some initial attempts employ either a data-driven examplebased approach for both, or a symbolic rule-based approach for both. We envision a modular approach where any AI technique can be used for any of these essential ingredients in decision making or decision support systems, paired with a contextual approach to define their combination and relative weight. In a world where neither humans nor AI systems work in isolation, but are tightly interconnected, e.g., the Internet of Things, we also envision a compositional approach to building ethically bounded AI, where the ethical properties of each component can be fruitfully exploited to derive those of the overall system. In this paper we define and motivate the notion of ethically-bounded AI, we describe two concrete examples, and we outline some outstanding challenges.

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Building Ethics into Artificial Intelligence

Cited by 131 publications

References 12 publications

Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas

Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas

Ethically Aligned Opportunistic Scheduling for Productive Laziness

Building Ethically Bounded AI

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