Transparent, explainable, and accountable AI for robotics

Wachter, Sandra; Mittelstadt, Brent; Floridi, Luciano

doi:10.1126/scirobotics.aan6080

Cited by 223 publications

(118 citation statements)

References 3 publications

(1 reference statement)

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“…First, our research contributes to research on moral outrage. By demonstrating the role of attribution of prejudiced motivation we contribute the literature suggesting that moral outrage is not only a response to harm (Hechler & Kessler, 2018 Third, our research complements current work in computer science, legal studies, and other disciplines on how to create fair algorithms (Abdul, Vermeulen, Wang, Lim, & Kankanhalli, 2018;Ananny, 2016;Kusner & Loftus, 2020;Sandvig, Hamilton, Karahalios, & Langbort, 2016;Selbst & Barocas, 2018;Wachter, Mittelstadt, & Floridi, 2017;Zou & Schiebinger, 2018). While this work on "algorithm ethics" discusses how to create fair algorithms, our work starts exploring the psychological response of biased algorithms, and how they differ from the psychological response to biased humans.…”

Section: Discussionmentioning

confidence: 57%

Algorithmic Discrimination Causes Less Moral Outrage than Human Discrimination

Bigman¹,

Waytz²,

Arnestad³

et al. 2020

Preprint

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The use of algorithms hold promise for overcoming human biases in decision making.Companies and governments are using algorithms to improve decision-making for hiring, medical treatments, and parole. Unfortunately, as with humans, some of these algorithms make persistently biased decisions, functionally discriminating people based on their race and gender.Media coverage suggests that people are morally outraged by algorithmic discrimination, but here we examine whether people are less outraged by algorithmic discrimination than by human discrimination. Six studies test this algorithmic outrage asymmetry hypothesis across diverse discrimination in hiring practices (sexism, ageism, racism) and across diverse participant groups (online samples, a quasi-representative sample, and a sample of tech workers). As predicted, people are less morally outraged by algorithmic discrimination. The studies further reveal that this algorithmic outrage asymmetry is driven by the reduced attribution of prejudicial motivation to machines. We also reveal a downstream consequence of algorithmic outrage asymmetrypeople are more likely to endorse racial stereotypes after algorithmic discrimination versus human discrimination. We discuss the theoretical and practical implications of these results, including the potential weakening of collective action to address systemic discrimination.

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Section: Discussionmentioning

confidence: 57%

Algorithmic Discrimination Causes Less Moral Outrage than Human Discrimination

Bigman¹,

Waytz²,

Arnestad³

et al. 2020

Preprint

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“…Complexity, variability, subjectivity, and lack of standardisation, including variable interpretation of the 'components' of each of the ethical principles, make this challenging (Alshammari & Simpson, 2017). However, it is achievable if the right questions are asked (Green, 2018) (Wachter, Mittelstadt, & Floridi, 2017a) and closer attention is payed to how the design process can influence (Kroll, 2018) whether an algorithm is more or less 'ethically-aligned.' Thus, this is the aim of this research project: to identify the methods and tools available to help developers, engineers and designers of ML specifically (but we hope the results of the this research may be easily applicable to other branches of AI) reflect on and apply 'ethics' (Adamson, Havens, & Chatila, 2019) so that they may know not only what to do or not to do, but also how to do it, or avoid doing it (Alshammari & Simpson, 2017).…”

Section: Moving From Principles To Practicementioning

confidence: 99%

From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices

et al. 2019

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The debate about the ethical implications of Artificial Intelligence dates from the 1960s (Wiener, 1960) (Samuel, 1960). However, in recent years symbolic AI has been complemented and sometimes replaced by (Deep) Neural Networks and Machine Learning (ML) techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles-the 'what' of AI ethics (beneficence, non-maleficence, autonomy, justice and explicability)-rather than on practices, the 'how.' Awareness of the potential issues is increasing at a fast rate, but the AI community's ability to take action to mitigate the associated risks is still at its infancy. Therefore, our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers 'apply ethics' at each stage of the pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs.

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“…Today, American and European policies are divergent regarding accountability of AI . The US is moving towards ethical design, education, and self‐regulation, while the European Union is focusing on individual rights by adopting a regulatory approach in the Regulation of 27 April 2016 on the personal data . It is for this reason that Wachter et al (2017) state that “Regulatory standards need to be developed to set system‐ and context‐dependent accountability requirements based on potential bias and discriminatory decision‐making and risks to safety, fairness, and privacy” .…”

Section: Responsibilitymentioning

confidence: 99%

Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery

O’Sullivan

Nevejans

Allen

et al. 2019

Robotics Computer Surgery

235

150

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Background This paper aims to move the debate forward regarding the potential for artificial intelligence (AI) and autonomous robotic surgery with a particular focus on ethics, regulation and legal aspects (such as civil law, international law, tort law, liability, medical malpractice, privacy and product/device legislation, among other aspects). Methods We conducted an intensive literature search on current or emerging AI and autonomous technologies (eg, vehicles), military and medical technologies (eg, surgical robots), relevant frameworks and standards, cyber security/safety‐ and legal‐systems worldwide. We provide a discussion on unique challenges for robotic surgery faced by proposals made for AI more generally (eg, Explainable AI) and machine learning more specifically (eg, black box), as well as recommendations for developing and improving relevant frameworks or standards. Conclusion We classify responsibility into the following: (1) Accountability; (2) Liability; and (3) Culpability. All three aspects were addressed when discussing responsibility for AI and autonomous surgical robots, be these civil or military patients (however, these aspects may require revision in cases where robots become citizens). The component which produces the least clarity is Culpability, since it is unthinkable in the current state of technology. We envision that in the near future a surgical robot can learn and perform routine operative tasks that can then be supervised by a human surgeon. This represents a surgical parallel to autonomously driven vehicles. Here a human remains in the ‘driving seat’ as a ‘doctor‐in‐the‐loop’ thereby safeguarding patients undergoing operations that are supported by surgical machines with autonomous capabilities.

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Transparent, explainable, and accountable AI for robotics

Abstract: Fair, accountable AI and robotics need precise regulation and better methods to certify, explain, and audit inscrutable systems.

Cited by 223 publications

References 3 publications

Algorithmic Discrimination Causes Less Moral Outrage than Human Discrimination

Algorithmic Discrimination Causes Less Moral Outrage than Human Discrimination

From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices

Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery

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