The ethics of AI and ML ethical codes

This post was inspired by the reading of ‘Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning’ by Daniel Green, Anna Lauren Hoffmann and Luke Stark [1]. The paper analysed public statements issued by independent institutions — varying from ‘Open AI’, ‘The Partnership on AI’, ‘The Montreal Declaration for a Responsible Development of Artificial Intelligence’, ‘The Toronto Declaration: Protecting the rights to equality and non-discrimination in machine learning systems’, etc… — on ethical approaches to Artificial Intelligence (AI) and Machine learning (ML). Overall, the researchers’ aim was to uncover assumptions and common themes across the statements to spot which, among those, foster ethical discourse and what hinder it. This article by no means attempts to reproduce the content of the researchers’ paper. Conversely, it aims at building on some interesting considerations that emerged from the paper and that, in my opinion, deserve further scrutiny.

Across the sample of public statements examined, what emerged was an overall deterministic vision of AI and ML. Deterministic in what way? Generally, the adjective ‘deterministic’ refers to all events that are ultimately determined by causes regarded as external to the will [2]. Once applied to the AI/ML discourse, ‘deterministic’ refers to the nature, development and impact of AI/ML having causes that are external to the human will. In philosophy, the term ‘determinism’ is often in conflict with the idea of ‘ethics’. What is anyway ‘determined’ is beyond our questions of ethics because we simply have no control over it. Therefore, it might seem puzzling that this is one of the themes or assumptions emerging from statements of the AI and ML codes of ethics. I think this is what deserves further scrutiny.

Through their analysis, the researchers identified seven core themes that were recurring in these AI/ML codes of ethics. I focus specifically on two; ‘values-driven determinism’ and ‘expert oversight’. As per the former, it recalls the idea that AI and ML are inevitable, impactful forces that we should nevertheless shape and dress-up with our own human values. The statements present deterministic framings of AI/ML. They present them as world-historical forces of change — inevitable seismic shifts to which humans can only react [3]. Paradoxically, AI/ML are also at the same time described as ‘values-driven’, insofar as human beings create them. For example, in the Montreal Declaration, there is overriding hope that “AI will make our societies better” [4]. This hope co-exists with sections exploring individual values such as Justice that range between instrumental impact (e.g., “What types of discrimination could AI create or exacerbate?”) and active human agency (e.g., “Should the development of AI be neutral or should it seek to reduce social and economic inequalities?”) [5]. Similarly, the Open AI charter aims at tackling the medium-term impact of inevitable “highly autonomous systems that outperform humans at most economically valuable work”, by collaborating on “value-aligned, safety-conscious project[s]” [6] in the present and near-term. These scenarios are ones in which AI and ML are inevitable forces to which we must adapt but for which, at the same time, we are also responsible [7].

This seemingly paradoxical aspect is probably simply explainable by the gap in expertise between AI experts and the wider population. What is ‘deterministic’ might not be the deterministic aspect of the technology but the determining role of the experts versus the ‘people’ in shaping the future of AI and ML. As the researchers suggest, ‘human agency is integral to ethical design, but it is largely a property of experts responsible for the design, implementation and, sometimes, oversight of AI/ML’ [8].

This brings us to the other core point in our focus; ‘expert oversight’. These statements frame ethical codes as a project of expert oversight, where technical and legal experts come together to articulate concerns and implement primarily technical, and secondarily legal solutions [9]. Nevertheless, they assume a universal community with ethical concerns in their statements. Despite doing the latter, these vision statements are not documents that refer to any mass mobilisation or participation. On one hand, this is a difficult issue to solve. In fact, the gap in knowledge between experts and the rest is a real issue as these ethical frameworks much depend on technical aspects and on the design of AI and ML technologies. Furthermore, many institutions such as the Toronto Declaration and Axon’s Ethics Board have tried to be more inclusive towards a not-strictly-technical public [10]. On the other, nevertheless, this state of affairs prevents civil society to exercise its role as a critical, democratic unit. Inevitably, experts, in their drafted ‘codes of ethics’, will be more likely to detail their own responsibilities as the responsibilities of individual professionals. However, they most likely will not actively scrutinise the nature of the profession or of the business in question itself. This is something more similar to codes of business ethics [11] rather than the ideals of political philosophy and ethics — justice, fairness… — that the codes seem to aspire to. While business ethics is concerned with engaging in business practices in ethical ways, political philosophy fundamentally questions — human/social — practices themselves. Similarly, the present approach to ethical codes prevents the possibility for the questioning of the practice or the ‘nature’ of AI and ML themselves.

Overall, these considerations suggest that there might be some fundamental contradictions in the way we approach AI/ML codes of ethics. On one hand, the so-framed deterministic aspect of the technology is in contrast with our capacity to shape it. What is anyway ‘inevitable’ goes beyond our ethical scope. On the other, the determining role of the experts prevents a fundamental questioning of the AI and ML systems. Both these issues stand on the way to a honest and coherent path to AI and ML ethical codes.