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)

As the range of potential uses for Artificial Intelligence, in particular machine learning, has increased, so has awareness of the associated ethical issues. This increased awareness has led to the realisation that existing legislation and regulation provides insufficient protection to individuals, groups, society, and the environment from AI harms. In response to this realisation, there has been a proliferation of principle-based ethics codes, guidelines and frameworks. However, it has become increasingly clear that a significant gap exists between the theory of (...) AI ethics principles and the practical design of AI systems. In previous work, we analysed whether it is possible to close this gap between the ‘what’ and the ‘how’ of AI ethics through the use of tools and methods designed to help AI developers, engineers, and designers translate principles into practice. We concluded that this method of closure is currently ineffective as almost all existing translational tools and methods are either too flexible or too strict. This raised the question: if, even with technical guidance, AI ethics is challenging to embed in the process of algorithmic design, is the entire pro-ethical design endeavour rendered futile? And, if no, then how can AI ethics be made useful for AI practitioners? This is the question we seek to address here by exploring why principles and technical translational tools are still needed even if they are limited, and how these limitations can be potentially overcome by providing theoretical grounding of a concept that has been termed ‘Ethics as a Service.’. (shrink)

As the range of potential uses for Artificial Intelligence (AI), in particular machine learning (ML), has increased, so has awareness of the associated ethical issues. This increased awareness has led to the realisation that existing legislation and regulation provides insufficient protection to individuals, groups, society, and the environment from AI harms. In response to this realisation, there has been a proliferation of principle-based ethics codes, guidelines and frameworks. However, it has become increasingly clear that a significant gap exists between the (...) theory of AI ethics principles and the practical design of AI systems. In previous work , we analysed whether it is possible to close this gap between the ‘what’ and the ‘how’ of AI ethics through the use of tools and methods designed to help AI developers, engineers, and designers translate principles into practice. We concluded that this method of closure is currently ineffective as almost all existing translational tools and methods are either too flexible (and thus vulnerable to ethics washing) or too strict (unresponsive to context). This raised the question: if, even with technical guidance, AI ethics is challenging to embed in the process of algorithmic design, is the entire pro-ethical design endeavour rendered futile? And, if no, then how can AI ethics be made useful for AI practitioners? This is the question we seek to address here by exploring why principles and technical translational tools are still needed even if they are limited, and how these limitations can be potentially overcome by providing theoretical grounding of a concept that has been termed ‘Ethics as a Service’. (shrink)

In this open peer commentary, we categorize the possible “neuroscience in national security” definitions of misuse of science and identify which, if any, are uniquely presented by advances in neuroscience. To define misuse, we first define what we would consider appropriate use: the application of reasonably safe and effective technology, based on valid and reliable scientific research, to serve a legitimate end. This definition presents distinct opportunities for assessing misuse: misuse is the application of invalid or unreliable science, or is (...) the use of reliable scientific methods to serve illegitimate ends. Ultimately, we conclude that while national security is often a politicized issue, assessing the state of scientific progress should not be. (shrink)

In the intersection between eugenics past and present, disability has never been far beneath the surface. Perceived and ascribed disabilities of body and mind were one of the core sets of eugenics traits that provided the basis for institutionalized and sterilization on eugenic grounds for the first 75 years of the 20th-century. Since that time, the eugenic preoccupation with the character of future generations has seeped into what have become everyday practices in the realm of reproductive choice. As Marsha (...) class='Hi'>Saxton (2000) and Adrienne Asch (2000, 2003) have forcefully argued, the use of prenatal screening technologies to facilitate the selective abortion of fetuses with features that signify disabling traits—the paradigm here being trisomy 21 in a fetus indicating Down Syndrome in the child—express a negative view of such disabilities sufficient to warrant terminating an otherwise wanted pregnancy. The eliminative structure of what Rosemary Garland Thompson (2012) has called eugenic logic persists in contemporary practices governing reproductive choice, social inclusion, and democratic participation and their relationship to disability. The tie between eugenics and contemporary disability studies suggests that eugenics and reflection on its history can also play a more positive role in disability politics. After focusing on eugenics in the first half of the paper, we will shift in the second half of the paper to eugenic resonances in contemporary thought and practice, concluding with some thoughts about ongoing practices of silencing and the very idea of eradicating disability. (shrink)