In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that do not (...) consider multiple stakeholders or the broader social context. In this article, we present an auditing framework to guide the ethical assessment of an algorithm. The audit instrument itself is comprised of three elements: a list of possible interests of stakeholders affected by the algorithm, an assessment of metrics that describe key ethically salient features of the algorithm, and a relevancy matrix that connects the assessed metrics to stakeholder interests. The proposed audit instrument yields an ethical evaluation of an algorithm that could be used by regulators and others interested in doing due diligence, while paying careful attention to the complex societal context within which the algorithm is deployed. (shrink)

In 2019, the IEEE launched the P7000 standards projects intended to address ethical issues in the design of autonomous and intelligent systems. This move came amidst a growing public concern over the unintended consequences of artificial intelligence (AI), compounded by the lack of an anticipatory process for attending to ethical impact within professional practice. However, the difficulty in moving from principles to practice presents a significant challenge to the implementation of ethical guidelines. Herein, we describe two complementary frameworks for integrating (...) ethical analysis into engineering practice to help address this challenge. We then provide the outcomes of an ethical analysis informed by these frameworks, conducted within the specific context of internetdelivered therapy in digital mental health. We hope both the frameworks and analysis can provide tools and insights, not only for the context of digital healthcare, but for data-enabled and intelligent technology development more broadly. (shrink)

Ethics has powerful teeth, but these are barely being used in the ethics of AI today – it is no wonder the ethics of AI is then blamed for having no teeth. This article argues that ‘ethics’ in the current AI ethics field is largely ineffective, trapped in an ‘ethical principles’ approach and as such particularly prone to manipulation, especially by industry actors. Using ethics as a substitute for law risks its abuse and misuse. This significantly limits what ethics can (...) achieve and is a great loss to the AI field and its impacts on individuals and society. This article discusses these risks and then highlights the teeth of ethics and the essential value they can – and should – bring to AI ethics now. (shrink)

The debate about the ethical implications of Artificial Intelligence dates from the 1960s :741–742, 1960; Wiener in Cybernetics: or control and communication in the animal and the machine, MIT Press, New York, 1961). However, in recent years symbolic AI has been complemented and sometimes replaced by Neural Networks and Machine Learning 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 —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. 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 Machine Learning development 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. (shrink)

Most philosophers agree that abduction (in the sense of Inference to the Best Explanation) is a type of inference that is frequently employed, in some form or other, both in everyday and in scientific reasoning. However, the exact form as well as the normative status of abduction are still matters of controversy. This entry contrasts abduction with other types of inference; points at prominent uses of it, both in and outside philosophy; considers various more or less precise statements of it; (...) discusses its normative status; and highlights possible connections between abduction and Bayesian confirmation theory. (shrink)

As information systems transform our world, computer scientists design affordances that influence the uses and impacts of these technological objects. This article describes how the practices of design affect the social values materialized in emerging technologies, and explores how design practices can encourage ethical reflection and action. The article presents an ethnography of a laboratory that engineered software for mobile phones to track users’ locations, habits, and behaviors. This technical work raised a number of ethical challenges, particularly around questions of (...) data use and surveillance. The ethnography suggests that particular activities within laboratories can help engineers agree on social values as important to design. It characterizes these activities as values levers: practices that open new conversations about social values and encourage consensus around those values as design criteria. Laboratory leaders and advocates can enable and strengthen these levers to encourage ethical reflection and action as an explicit part of design practice. (shrink)

Though rapid collection of big data is ubiquitous across domains, from industry settings to academic contexts, the ethics of big data collection and research are contested. A nexus of data ethics issues is the concept of creep, or repurposing of data for other applications or research beyond the conditions of original collection. Data creep has proven controversial and has prompted concerns about the scope of ethical oversight. Institutional review boards offer little guidance regarding big data, and problematic research can still (...) meet ethical standards. While ethics seem concrete through institutional deployment, I frame ethics as produced. Informed by my ethnographic research at a large public university in the U.S., I explore ethics through two models: ethics as institutional procedures and ethics as acts and intentions. The university where I conducted fieldwork is the development grounds for a predictive model that uses student data to anticipate academic success. While students consent to data collection, the circumstances of consent and the degree to which they are informed are not so apparent, as many data are a product of creep. Drawing from interviews and participant observation with administrators, data scientists, developers, and students, I examine data ethics, from a larger institutional model to everyday enactments related to data creep. After demonstrating the limits of such models, I propose a remodeling of ethics that draws on recent works on data, justice, and refusal to pose generative questions for rethinking ethics in institutional contexts. (shrink)

Data science, and the related field of big data, is an emerging discipline involving the analysis of data to solve problems and develop insights. This rapidly growing domain promises many benefits to both consumers and businesses. However, the use of big data analytics can also introduce many ethical concerns, stemming from, for example, the possible loss of privacy or the harming of a sub-category of the population via a classification algorithm. To help address these potential ethical challenges, this paper maps (...) and describes the main ethical themes that were identified via systematic literature review. It then identifies a possible structure to integrate these themes within a data science project, thus helping to provide some structure in the on-going debate with respect to the possible ethical situations that can arise when using data science analytics. (shrink)

This theoretical paper considers the morality of machine learning algorithms and systems in the light of the biases that ground their correctness. It begins by presenting biases not as a priori negative entities but as contingent external referents—often gathered in benchmarked repositories called ground-truth datasets—that define what needs to be learned and allow for performance measures. I then argue that ground-truth datasets and their concomitant practices—that fundamentally involve establishing biases to enable learning procedures—can be described by their respective morality, here (...) defined as the more or less accounted experience of hesitation when faced with what pragmatist philosopher William James called “genuine options”—that is, choices to be made in the heat of the moment that engage different possible futures. I then stress three constitutive dimensions of this pragmatist morality, as far as ground-truthing practices are concerned: the definition of the problem to be solved, the identification of the data to be collected and set up, and the qualification of the targets to be learned. I finally suggest that this three-dimensional conceptual space can be used to map machine learning algorithmic projects in terms of the morality of their respective and constitutive ground-truthing practices. Such techno-moral graphs may, in turn, serve as equipment for greater governance of machine learning algorithms and systems. (shrink)