What does it mean to embed ethics in data science? An integrative approach based on microethics and virtues

Bezuidenhout, Louise; Ratti, Emanuele

doi:10.1007/s00146-020-01112-w

Cited by 26 publications

(24 citation statements)

References 30 publications

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“…Inputs from patients, service end-users, physicians, and caregivers should be factored in for developing solutions [15] as a first step to consider the consequences of machine learning and AI, including the need to reduce bias and increase efficacy. Different approaches are emerging, including a virtue ethics framework (ie, moral attention and appropriate extension of moral concerns) and micro-ethics (to understand and grasp the grittiness and distinctiveness of each situation) [69]. Impactful actions for using ethics and data security checklists are required with digital capabilities increasing at a rapid rate; for example, IoT-based approaches.…”

Section: Renderxmentioning

confidence: 99%

Digital Mental Health Challenges and the Horizon Ahead for Solutions

Balcombe¹,

Leo²

2021

JMIR Ment Health

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The demand outstripping supply of mental health resources during the COVID-19 pandemic presents opportunities for digital technology tools to fill this new gap and, in the process, demonstrate capabilities to increase their effectiveness and efficiency. However, technology-enabled services have faced challenges in being sustainably implemented despite showing promising outcomes in efficacy trials since the early 2000s. The ongoing failure of these implementations has been addressed in reconceptualized models and frameworks, along with various efforts to branch out among disparate developers and clinical researchers to provide them with a key for furthering evaluative research. However, the limitations of traditional research methods in dealing with the complexities of mental health care warrant a diversified approach. The crux of the challenges of digital mental health implementation is the efficacy and evaluation of existing studies. Web-based interventions are increasingly used during the pandemic, allowing for affordable access to psychological therapies. However, a lagging infrastructure and skill base has limited the application of digital solutions in mental health care. Methodologies need to be converged owing to the rapid development of digital technologies that have outpaced the evaluation of rigorous digital mental health interventions and strategies to prevent mental illness. The functions and implications of human-computer interaction require a better understanding to overcome engagement barriers, especially with predictive technologies. Explainable artificial intelligence is being incorporated into digital mental health implementation to obtain positive and responsible outcomes. Investment in digital platforms and associated apps for real-time screening, tracking, and treatment offer the promise of cost-effectiveness in vulnerable populations. Although machine learning has been limited by study conduct and reporting methods, the increasing use of unstructured data has strengthened its potential. Early evidence suggests that the advantages outweigh the disadvantages of incrementing such technology. The limitations of an evidence-based approach require better integration of decision support tools to guide policymakers with digital mental health implementation. There is a complex range of issues with effectiveness, equity, access, and ethics (eg, privacy, confidentiality, fairness, transparency, reproducibility, and accountability), which warrant resolution. Evidence-informed policies, development of eminent digital products and services, and skills to use and maintain these solutions are required. Studies need to focus on developing digital platforms with explainable artificial intelligence–based apps to enhance resilience and guide the treatment decisions of mental health practitioners. Investments in digital mental health should ensure their safety and workability. End users should encourage the use of innovative methods to encourage developers to effectively evaluate their products and services and to render them a worthwhile investment. Technology-enabled services in a hybrid model of care are most likely to be effective (eg, specialists using these services among vulnerable, at-risk populations but not severe cases of mental ill health).

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Section: Renderxmentioning

confidence: 99%

Digital Mental Health Challenges and the Horizon Ahead for Solutions

Balcombe¹,

Leo²

2021

JMIR Ment Health

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“…Previous research has discussed the potential advantages of following a virtue ethics perspective when it comes to discussing responsibility and AI, such as responsible autonomy, situation sensitivity, or responsibility diffusion (Bilal et al, 2020;Berberich & Diepold, 2018;Bezuidenhout & Ratti, 2020;Gamez et al 2020;Hagendorff, 2020;Vallor, 2016). However, these accounts usually refer to contemporary neo-Aristotelian virtue ethics, in its various perspectives, for instance with a specific focus on technology and virtues (Vallor, 2016).…”

Section: The Virtue Ethics Frameworkmentioning

confidence: 99%

Understanding responsibility in Responsible AI. Dianoetic virtues and the hard problem of context

et al. 2021

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During the last decade there has been burgeoning research concerning the ways in which we should think of and apply the concept of responsibility for Artificial Intelligence. Despite this conceptual richness, there is still a lack of consensus regarding what Responsible AI entails on both conceptual and practical levels. The aim of this paper is to connect the ethical dimension of responsibility in Responsible AI with Aristotelian virtue ethics, where notions of context and dianoetic virtues play a grounding role for the concept of moral responsibility. The paper starts by highlighting the important difficulties in assigning responsibility to either technologies themselves or to their developers. Top-down and bottom-up approaches to moral responsibility are then contrasted, as we explore how they could inform debates about Responsible AI. We highlight the limits of the former ethical approaches and build the case for classical Aristotelian virtue ethics. We show that two building blocks of Aristotle’s ethics, dianoetic virtues and the context of actions, although largely ignored in the literature, can shed light on how we could think of moral responsibility for both AI and humans. We end by exploring the practical implications of this particular understanding of moral responsibility along the triadic dimensions of ethics by design, ethics in design and ethics for designers.

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“…Moreover, in the context of data science, it suggests that ethics is taught and cultivated in the same way in which technical skills are taught, in particular by practicing in the context where they are needed, and by having exemplars showing you how and why we should make certain choices rather than others. Ethics is embedded in data science, not something external to it (Bezuidenhout & Ratti, 2020), and data scientists should be provided with exercises, heuristics, and novel problems, in order to increase their skills as well as their virtues. Therefore, "learning ethics" is the process of cultivating virtues or moral abilities for the data science context 7 ; it "requires time, experience, and habituation to develop it" (Annas, 2011, p 14), and the result is "the kind of actively and intelligently engaged practical mastery that we find in practical experts such as pianists and athletes 8 " (Annas, 2011, p 14).…”

Section: Good Data Scientists: Skills and Virtuesmentioning

confidence: 99%

“…There have been efforts to formulate higher-level principles (such as beneficence or fairness), which should guide research and development of AI tools in a direction consistent with societal values. This way of understanding the discipline has been named hard ethics (Floridi, 2018) or macroethics (Bezuidenhout & Ratti, 2020), and it plays a fundamental role in informing conversations about governance and regulation of AI more in general as a policy goal (Floridi, 2018). Under this rubric, there has been a proliferation of private and public initiatives for formulating the correct principles (Floridi & Cowls, 2019;Jobin et al, 2019;Saltz & Dewar, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, while we agree with the "from what to how" literature that we should embed ethical decision-making in the actual data science process, we understand this idea of "embedding" as the cultivation of moral abilities in the daily activities of data scientists. We consider our approach a "microethics" (Bezuidenhout & Ratti, 2020;Hagendorff, 2020a;Komesaroff, 1995), and we conceive it as a form of ethical training and exercise in which the goal for a data scientist is to learn how to identify the ethical relevance of his/her day-to-day activities. Rather than "applying ethics" (which can be done also mindlessly), data scientists should cultivate ethics as a form of reflection on the subtleties of their technical choices: our goal is to provide a framework to do this.…”

Section: Introductionmentioning

confidence: 99%

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Cultivating Moral Attention: a Virtue-Oriented Approach to Responsible Data Science in Healthcare

Ratti

Graves

2021

Philos. Technol.

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In the past few years, the ethical ramifications of AI technologies (in particular data science) have been at the center of intense debates. Considerable attention has been devoted to understanding how a morally responsible practice of data science can be promoted and which values have to shape it. In this context, ethics and moral responsibility have been mainly conceptualized as compliance to widely shared principles. However, several scholars have highlighted the limitations of such a principled approach. Drawing from microethics and the virtue theory tradition, in this paper, we formulate a different approach to ethics in data science which is based on a different conception of “being ethical” and, ultimately, of what it means to promote a morally responsible data science. First, we develop the idea that, rather than only compliance, ethical decision-making consists in using certain moral abilities (e.g., virtues), which are cultivated by practicing and exercising them in the data science process. An aspect of virtue development that we discuss here is moral attention, which is the ability of data scientists to identify the ethical relevance of their own technical decisions in data science activities. Next, by elaborating on the capability approach, we define a technical act as ethically relevant when it impacts one or more of the basic human capabilities of data subjects. Therefore, rather than “applying ethics” (which can be mindless), data scientists should cultivate ethics as a form of reflection on how technical choices and ethical impacts shape one another. Finally, we show how this microethical framework concretely works, by dissecting the ethical dimension of the technical procedures involved in data understanding and preparation of electronic health records.

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What does it mean to embed ethics in data science? An integrative approach based on microethics and virtues

Cited by 26 publications

References 30 publications

Digital Mental Health Challenges and the Horizon Ahead for Solutions

Digital Mental Health Challenges and the Horizon Ahead for Solutions

Understanding responsibility in Responsible AI. Dianoetic virtues and the hard problem of context

Cultivating Moral Attention: a Virtue-Oriented Approach to Responsible Data Science in Healthcare

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