The ethics of crashes with self‐driving cars: A roadmap, II

Nyholm, Sven

doi:10.1111/phc3.12506

Cited by 49 publications

(22 citation statements)

References 31 publications

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“…The obscuration of accountability just described has its counterparts in many other domains where artificial intelligence is being deployed—most notably self-driving vehicles. By looking at the ethical literature in relevant debates, one take-home message might be that we might be required to implement less individualistic notions of responsibility, such as distributed or collective responsibility, to close potential ‘responsibility gaps’ 28–30. Having said this, it remains unclear how these less individualistic notions of responsibility might translate into the legal system.…”

Section: Pitfalls Of Algorithmic Decision-making At the Structural Lementioning

confidence: 99%

“…Thus, if we have good enough reasons to believe that involving algorithms in medicine promotes more reliable decision-making, does that justify their deployment on consequentialist grounds? In this vein, how should we balance the values of transparency and evidence on the one hand, and reliability and efficacy on the other30 (for a more sceptical view cf. Stegenga35)?…”

Section: Pitfalls Of Algorithmic Decision-making At the Structural Lementioning

confidence: 99%

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On the ethics of algorithmic decision-making in healthcare

2019

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In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level. Whereas involving machine learning might improve the accuracy of medical diagnosis, it comes at the expense of opacity when trying to assess the reliability of given diagnosis. Drawing on literature in social epistemology and moral responsibility, we argue that the uncertainty in question potentially undermines the epistemic authority of clinicians. Furthermore, we elucidate potential pitfalls of involving machine learning in healthcare with respect to paternalism, moral responsibility and fairness. At last, we discuss how the deployment of machine learning algorithms might shift the evidentiary norms of medical diagnosis. In this regard, we hope to lay the grounds for further ethical reflection of the opportunities and pitfalls of machine learning for enhancing decision-making in healthcare.

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Section: Pitfalls Of Algorithmic Decision-making At the Structural Lementioning

confidence: 99%

Section: Pitfalls Of Algorithmic Decision-making At the Structural Lementioning

confidence: 99%

On the ethics of algorithmic decision-making in healthcare

2019

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“…The actions taken in such situations have potentially harmful consequences for car occupants, other traffic participants, and pedestrians. Therefore, it is important to carefully consider the ethics of how self-driving cars will be designed to make decisions, an issue that is the topic of current debate (Nyholm, 2018a,b; Dietrich and Weisswange, 2019; Keeling et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Moral Judgements on the Actions of Self-Driving Cars and Human Drivers in Dilemma Situations From Different Perspectives

et al. 2019

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Self-driving cars have the potential to greatly improve public safety. However, their introduction onto public roads must overcome both ethical and technical challenges. To further understand the ethical issues of introducing self-driving cars, we conducted two moral judgement studies investigating potential differences in the moral norms applied to human drivers and self-driving cars. In the experiments, participants made judgements on a series of dilemma situations involving human drivers or self-driving cars. We manipulated which perspective situations were presented from in order to ascertain the effect of perspective on moral judgements. Two main findings were apparent from the results of the experiments. First, human drivers and self-driving cars were largely judged similarly. However, there was a stronger tendency to prefer self-driving cars to act in ways to minimize harm, compared to human drivers. Second, there was an indication that perspective influences judgements in some situations. Specifically, when considering situations from the perspective of a pedestrian, people preferred actions that would endanger car occupants instead of themselves. However, they did not show such a self-preservation tendency when the alternative was to endanger other pedestrians to save themselves. This effect was more prevalent for judgements on human drivers than self-driving cars. Overall, the results extend and agree with previous research, again contradicting existing ethical guidelines for self-driving car decision making and highlighting the difficulties with adapting public opinion to decision making algorithms.

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“…Most SDVs are supervised autonomous systems, which refers to systems that require human intervention to operate properly. Even with human supervision, this particular application in AI is controversial due to safety concerns since human lives is at stake [17][18]. However, despite the safety concerns, the steady development and research in safety measures in SDVs are slowly gaining the trust of drivers and passengers.…”

Section: Self-driving Vehiclesmentioning

confidence: 99%

Artificial Intelligence and Military Applications: Innovations, Cybersecurity Challenges & Open Research Areas

Jayakumar¹,

Brohi²,

Jhanjhi³

2021

Preprint

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Over the years, Artificial Intelligence (AI) has seen a steady progress in development and evolution that can aid many sectors. Today, AI plays an essential role in the Fourth Industrial Revolution, and it is making its way into the military sector as well. Many countries are actively looking into AI military technology. Some of these innovations include image recognition, text analysis, Self-driving vehicles (SDV), gaming, robotic process automation (RPA) & Robotic and Autonomous Systems (RAS), and Autonomous Weapons Systems (AWS). In this research, we discuss the advantages of each innovation mentioned and analyze the many different cybersecurity threats that awaken due to military artificial intelligence systems. We then discuss the open research areas to better improve the current state of military artificial intelligence applications in terms of ethics, system security, proper strategy plan, accepted responsibility matrix, and introduction of relevant laws.

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The ethics of crashes with self‐driving cars: A roadmap, II

Cited by 49 publications

References 31 publications

On the ethics of algorithmic decision-making in healthcare

On the ethics of algorithmic decision-making in healthcare

Moral Judgements on the Actions of Self-Driving Cars and Human Drivers in Dilemma Situations From Different Perspectives

Artificial Intelligence and Military Applications: Innovations, Cybersecurity Challenges & Open Research Areas

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