Abstract
The debate about the ethical implications of Artificial Intelligence dates from the 1960s (Samuel in Science, 132(3429):741–742, 1960. https://doi.org/10.1126/science.132.3429.741; 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 (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. 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.
Highlights
As the availability of data on almost every aspect of life, and the sophistication of machine learning (ML) techniques, has increased (Lepri et al 2018) so have the opportunities for improving both public and private life (Floridi and Taddeo 2016)
With the aim of identifying the methods and tools available to help developers, engineers and designers of ML reflect on and apply ‘ethics’ in mind, the first task was to design a typology, for the very practically minded ML community (Holzinger 2018), that would ‘match’ the tools and methods identified to the ethical principles outlined
Many of the tools included are relatively immature. This makes it difficult to assess the scope of their use and hard to encourage their adoption by the practically-minded ML developers, especially when the competitive advantage of more ethically-aligned Artificial Intelligence (AI) is not yet clear
Summary
As the availability of data on almost every aspect of life, and the sophistication of machine learning (ML) techniques, has increased (Lepri et al 2018) so have the opportunities for improving both public and private life (Floridi and Taddeo 2016). Balancing the tension between supporting innovation, so that society’s right to benefit from science is protected (Knoppers and Thorogood 2017), and limiting the potential harms associated with poorly-designed AI (and ML in this context), (summarised in Table 1) is challenging. ML algorithms are powerful socio-technical constructs (Ananny and Crawford 2018), which raise concerns that are as much (if not more) about people as they are about code (see Table 1) (Crawford and Calo 2016). Enabling the so-called dual advantage of ‘ethical ML’—so that the opportunities are capitalised on, whilst the harms are foreseen and minimised or prevented (Floridi et al 2018)—requires asking difficult questions about design, development, deployment, practices, uses and users, as well as the data that fuel the whole life-cycle of algorithms (Cath et al 2018). Lessig was right all along: code is both our greatest threat and our greatest promise (Lessig and Lessig 2006)
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