Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

Piano, Samuele Lo

doi:10.1057/s41599-020-0501-9

Cited by 121 publications

(63 citation statements)

References 48 publications

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“…Consequently, recent works in both social sciences and machine learning have highlighted the challenges in socio-cultural structures that are reflected and amplified by learning algorithms. As a result, many guidelines from the technical (Cath, 2018;Silberg and Manyika, 2019;Hagendorff, 2020a;Piano, 2020) and social perspectives (Verbeek, 2008;Liu and Zawieska, 2017;Birhane and Cummins, 2019) have been presented. These guidelines (Vayena et al, 2018;Hagendorff, 2020b;Piano, 2020) some works in robotics (Anderson and Anderson, 2010;Lin et al, 2012;BSI-2016BSI- , 2016Boden et al, 2017) have also investigated the importance of addressing ethical issues for safe and responsible development.…”

Section: Ethical Aspects and Fairness Implicationsmentioning

confidence: 99%

“…As a result, many guidelines from the technical (Cath, 2018;Silberg and Manyika, 2019;Hagendorff, 2020a;Piano, 2020) and social perspectives (Verbeek, 2008;Liu and Zawieska, 2017;Birhane and Cummins, 2019) have been presented. These guidelines (Vayena et al, 2018;Hagendorff, 2020b;Piano, 2020) some works in robotics (Anderson and Anderson, 2010;Lin et al, 2012;BSI-2016BSI- , 2016Boden et al, 2017) have also investigated the importance of addressing ethical issues for safe and responsible development. These ethical guidelines (Reed et al, 2016;Goodman and Flaxman, 2017;Johnson et al, 2019;Arrieta et al, 2020) share the value of robots effectively and safely assisting people, and under no circumstance cause harm or endanger their physical integrity (De Santis et al, 2008;Riek and Howard, 2014;Vandemeulebroucke et al, 2020).…”

Section: Ethical Aspects and Fairness Implicationsmentioning

confidence: 99%

“…Consequently, recent works in both social sciences and machine learning have highlighted the challenges in socio-cultural structures that are reflected and amplified by learning algorithms. As a result, many guidelines from the technical (Cath, 2018 ; Silberg and Manyika, 2019 ; Hagendorff, 2020a ; Piano, 2020 ) and social perspectives (Verbeek, 2008 ; Liu and Zawieska, 2017 ; Birhane and Cummins, 2019 ) have been presented. These guidelines (Vayena et al, 2018 ; Hagendorff, 2020b ; Piano, 2020 ) are aimed toward mitigating the adverse effects and advocating for ethical principles, such as fairness, trust, privacy, liability, data management, transparency, equality, justice, truth, and welfare.…”

Section: Ethical Aspects and Fairness Implicationsmentioning

confidence: 99%

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From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation

2021

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The exponentially increasing advances in robotics and machine learning are facilitating the transition of robots from being confined to controlled industrial spaces to performing novel everyday tasks in domestic and urban environments. In order to make the presence of robots safe as well as comfortable for humans, and to facilitate their acceptance in public environments, they are often equipped with social abilities for navigation and interaction. Socially compliant robot navigation is increasingly being learned from human observations or demonstrations. We argue that these techniques that typically aim to mimic human behavior do not guarantee fair behavior. As a consequence, social navigation models can replicate, promote, and amplify societal unfairness, such as discrimination and segregation. In this work, we investigate a framework for diminishing bias in social robot navigation models so that robots are equipped with the capability to plan as well as adapt their paths based on both physical and social demands. Our proposed framework consists of two components: learning which incorporates social context into the learning process to account for safety and comfort, and relearning to detect and correct potentially harmful outcomes before the onset. We provide both technological and societal analysis using three diverse case studies in different social scenarios of interaction. Moreover, we present ethical implications of deploying robots in social environments and propose potential solutions. Through this study, we highlight the importance and advocate for fairness in human-robot interactions in order to promote more equitable social relationships, roles, and dynamics and consequently positively influence our society.

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Section: Ethical Aspects and Fairness Implicationsmentioning

confidence: 99%

Section: Ethical Aspects and Fairness Implicationsmentioning

confidence: 99%

“…Consequently, recent works in both social sciences and machine learning have highlighted the challenges in socio-cultural structures that are reflected and amplified by learning algorithms. As a result, many guidelines from the technical (Cath, 2018 ; Silberg and Manyika, 2019 ; Hagendorff, 2020a ; Piano, 2020 ) and social perspectives (Verbeek, 2008 ; Liu and Zawieska, 2017 ; Birhane and Cummins, 2019 ) have been presented. These guidelines (Vayena et al, 2018 ; Hagendorff, 2020b ; Piano, 2020 ) are aimed toward mitigating the adverse effects and advocating for ethical principles, such as fairness, trust, privacy, liability, data management, transparency, equality, justice, truth, and welfare.…”

Section: Ethical Aspects and Fairness Implicationsmentioning

confidence: 99%

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From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation

2021

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“…In our reading, the field of AI ethics has undergone three broad phases. The first was an AI ethics set of principles [15]. The second phase was an ethical-bydesign approach, which was an engineering focused problem-solving exercise [16].…”

Section: Value Canvassmentioning

confidence: 99%

The interrelation between data and AI ethics in the context of impact assessments

Kazim

Koshiyama

2020

AI Ethics

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In the growing literature on artificial intelligence (AI) impact assessments, the literature on data protection impact assessments is heavily referenced. Given the relative maturity of the data protection debate and that it has translated into legal codification, it is indeed a natural place to start for AI. In this article, we anticipate directions in what we believe will become a dominant and impactful forthcoming debate, namely, how to conceptualise the relationship between data protection and AI impact. We begin by discussing the value canvas i.e. the ethical principles that underpin data and AI ethics, and discuss how these are instantiated in the context of value trade-offs when the ethics are applied. Following this, we map three kinds of relationships that can be envisioned between data and AI ethics, and then close with a discussion of asymmetry in value trade-offs when privacy and fairness are concerned.

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“…Since the gist of this work is in the commonality of the issues met in the different families of quantification, the authors do not repeat in this section all the policy initiatives which are already ongoing in existing fields where the discussion is more advanced, as for example in the ethics of artificial intelligence and algorithms, see a recent reviews in (Cath et al, 2018), (Lo Piano, 2020) or in the domain of official statistics, where codes of good practices have existed for a long time.…”

Section: Some Emerging Implications For Policymentioning

confidence: 99%

From sociology of quantification to ethics of quantification

Saltelli

Fiore

2020

Humanit Soc Sci Commun

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Quantifications are produced by several disciplinary houses in a myriad of different styles. The concerns about unethical use of algorithms, unintended consequences of metrics, as well as the warning about statistical and mathematical malpractices are all part of a general malaise, symptoms of our tight addiction to quantification. What problems are shared by all these instances of quantification? After reviewing existing concerns about different domains, the present perspective article illustrates the need and the urgency for an encompassing ethics of quantification. The difficulties to discipline the existing regime of numerification are addressed; obstacles and lock-ins are identified. Finally, indications for policies for different actors are suggested.

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Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

Cited by 121 publications

References 48 publications

From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation

From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation

The interrelation between data and AI ethics in the context of impact assessments

From sociology of quantification to ethics of quantification

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