Luciano Floridi

The modern abundance and prominence of data has led to the development of “data science” as a new field of enquiry, along with a body of epistemological reflections upon its foundations, methods, and consequences. This article provides a systematic analysis and critical review of significant open problems and debates in the epistemology of data science. We propose a partition of the epistemology of data science into the following five domains: (i) the constitution of data science; (ii) the kind of enquiry that it identifies; (iii) the kinds of knowledge that data science generates; (iv) the nature and epistemological significance of “black box” problems; and (v) the relationship between data science and the philosophy of science more generally.

By mid-2019 there were more than 80 AI ethics guides available in the public domain. Despite this, 2020 saw numerous news stories break related to ethically questionable uses of AI. In part, this is because AI ethics theory remains highly abstract, and of limited practical applicability to those actually responsible for designing algorithms and AI systems. Our previous research sought to start closing this gap between the ‘what’ and the ‘how’ of AI ethics through the creation of a searchable typology of tools and methods designed to translate between the five most common AI ethics principles and implementable design practices. Whilst a useful starting point, that research rested on the assumption that _all_ AI practitioners are aware of the ethical implications of AI, understand their importance, and are actively seeking to respond to them. In reality, it is unclear whether this is the case. It is this limitation that we seek to overcome here by conducting a mixed-methods qualitative analysis to answer the following four questions: what do AI practitioners understand about the need to translate ethical principles into practice? What motivates AI practitioners to embed ethical principles into design practices? What barriers do AI practitioners face when attempting to translate ethical principles into practice? And finally, what assistance do AI practitioners want and need when translating ethical principles into practice?

Over the past few years, there has been a proliferation of artificial intelligence strategies, released by governments around the world, that seek to maximise the benefits of AI and minimise potential harms. This article provides a comparative analysis of the European Union and the United States’ AI strategies and considers the visions of a ‘Good AI Society’ that are forwarded in key policy documents and their opportunity costs, the extent to which the implementation of each vision is living up to stated aims and the consequences that these differing visions of a ‘Good AI Society’ have for transatlantic cooperation. The article concludes by comparing the ethical desirability of each vision and identifies areas where the EU, and especially the US, need to improve in order to achieve ethical outcomes and deepen cooperation.

The proposed European Artificial Intelligence Act (AIA) is the first attempt to elaborate a general legal framework for AI carried out by any major global economy. As such, the AIA is likely to become a point of reference in the larger discourse on how AI systems can (and should) be regulated. In this article, we describe and discuss the two primary enforcement mechanisms proposed in the AIA: the _conformity assessments_ that providers of high-risk AI systems are expected to conduct, and the _post-market monitoring plans_ that providers must establish to document the performance of high-risk AI systems throughout their lifetimes. We argue that the AIA can be interpreted as a proposal to establish a Europe-wide ecosystem for conducting AI auditing, albeit in other words. Our analysis offers two main contributions. First, by describing the enforcement mechanisms included in the AIA in terminology borrowed from existing literature on AI auditing, we help providers of AI systems understand how they can prove adherence to the requirements set out in the AIA in practice. Second, by examining the AIA from an auditing perspective, we seek to provide transferable lessons from previous research about how to refine further the regulatory approach outlined in the AIA. We conclude by highlighting seven aspects of the AIA where amendments (or simply clarifications) would be helpful. These include, above all, the need to translate vague concepts into verifiable criteria and to strengthen the institutional safeguards concerning conformity assessments based on internal checks.

In this article, we analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change. We identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change, and it can contribute to combatting the climate crisis effectively. However, the development of AI also raises two sets of problems when considering climate change: the possible exacerbation of social and ethical challenges already associated with AI, and the contribution to climate change of the greenhouse gases emitted by training data and computation-intensive AI systems. We assess the carbon footprint of AI research, and the factors that influence AI’s greenhouse gas (GHG) emissions in this domain. We find that the carbon footprint of AI research may be significant and highlight the need for more evidence concerning the trade-off between the GHG emissions generated by AI research and the energy and resource efficiency gains that AI can offer. In light of our analysis, we argue that leveraging the opportunities offered by AI for global climate change whilst limiting its risks is a gambit which requires responsive, evidence-based, and effective governance to become a winning strategy. We conclude by identifying the European Union as being especially well-placed to play a leading role in this policy response and provide 13 recommendations that are designed to identify and harness the opportunities of AI for combatting climate change, while reducing its impact on the environment.

We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealisedexplanation gamein which players collaborate to find the best explanation(s) for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal patterns of variable granularity and scope. We characterise the conditions under which such a game is almost surely guaranteed to converge on a (conditionally) optimal explanation surface in polynomial time, and highlight obstacles that will tend to prevent the players from advancing beyond certain explanatory thresholds. The game serves a descriptive and a normative function, establishing a conceptual space in which to analyse and compare existing proposals, as well as design new and improved solutions.

Today, in any mature information society, we live neither online nor offline but on life, that is, we increasingly live in that special space that is both analogue and digital, both online and offline. Imagine someone asking whether the water is fresh or salty in the estuary where the river meets the sea. That someone has not understood the special nature of the place. Our information society is that place. And our technologies are perfectly evolved to take advantage of it, like mangroves growing in brackish water. In the mangrove society, machine-readable data, new forms of agency and interactions are constantly evolving. The consequences of such a radical transformation are many, but one is particularly significant and rich in consequences: how should we design the mangrove society, when it comes to the relationship between the natural and the digital? In this text, I argue that the digital blue is a great ally of the environmental green. The two work best in tandem. And if the world will enjoy a sustainable development for the environment that is also socially preferable for humanity, it will be because the green and the blue will have created a virtuous circle of nature and technology. It can be done. There is just no time to waste.

There is growing concern that decision-making informed by machine learning (ML) algorithms may unfairly discriminate based on personal demographic attributes, such as race and gender. Scholars have responded by introducing numerous mathematical definitions of fairness to test the algorithm, many of which are in conflict with one another. However, these reductionist representations of fairness often bear little resemblance to real-life fairness considerations, which in practice are highly contextual. Moreover, fairness metrics tend to be implemented in narrow and targeted toolkits that are difficult to integrate into an algorithm’s broader ethical assessment. In this paper, we derive lessons from ethical philosophy and welfare economics as they relate to the contextual factors relevant for fairness. In particular we highlight the debate around acceptability of particular inequalities and the inextricable links between fairness, welfare and autonomy. We propose Key Ethics Indicators (KEIs) as a way towards providing a more holistic understanding of whether or not an algorithm is aligned to the decision-maker’s ethical values.

The Internet is like a new country, with a growing population of millions of well educated citizens. If it wants to keep track of its own cultural achievements in real time, it will have to provide itself with an infostructure like a virtual National Library system. This paper proposes that institutions all over the world should take full advantage of the new technologies available, and promote and coordinate such a global service. This is essential in order to make possible a really efficient management of human knowledge on a global scale.

The article concerns the meta-epistemological problem of the justification of a theory of knowledge and provides a reconstruction of the history of its formulations. In the first section, I analyse the connections between Sextus Empiricus' diallelus, Montaigne's rouet and Chisholm's problem of criterion; in the second section I focus on the link between thediallelus and the Cartesian circle; in the third section I reconstruct the origin of Fries' trilemma; finally, in the last section I draw some general conclusions about the issue qua a general problem for a theory of knowledge.

In this paper, I first outline a model of Computer-Meditated Interaction (CMI) to distinguish between (1) CMI HCI-transparent (HCI = human-computer interaction) and (2) CMI HCI-dependent. Some main features of (1) are then analysed (data-radiation, data-tracking, internalisation of problem solving), while (2) is further divided into (2.1) CMI-HCI stand-alone and (2.2) CMI-HCI on-line. Some advantages and limits of (2.1) are suggested, together with an overview of the present and future strategies leading the production of new applications in the field. The analysis of (2.2) provides a last distinction between communication-oriented and elaboration-oriented CMI-HCI on-line (i.e. CMC and NIA). A description of these two forms of CMI is then sketched, together with an interpretation of the new strategic view which should lead the projection of applications in the field, a possible agenda and a forecast of the chief effects of some present trends.

The previous chapters have provided a detailed overview of the variety of ethical challenges posed by the development of ICTs. By way of conclusion, in this epilogue I would like to invite the reader to look into the possible future of Information and Computer Ethics. More specifically, I shall try to forecast how the convergence of two fundamental trends of our times, globalization and the development of the information society, may interact with the ethical problems analysed in this book. The exercise will not be based on some untenable technological determinism. Humanity is, and will remain, firmly in charge of its destiny and hence be responsible for it. Rather, it will mean adopting the farmer's view that, with enough intelligence, toil and a bit of luck, one might be able to tell today what one will probably reap tomorrow. Before trying to ‘look into the seeds of time, and say which grain will grow and which will not’ (Shakespeare, Macbeth, Act I, Scene III, 59–62), two clarifications might be in order. First, the future of globalization is a phenomenon too complex even to sketch in this brief epilogue. For a synthetic, well-balanced and informed overview, the reader may wish to consult Held and McGrew (2001) and consider that this chapter is written from what Held et al. (1999)) have defined as a ‘transformationalist perspective’, according to which ‘globalization does not simply denote a shift in the extensity or scale of social relations and activity.