Luciano Floridi

Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016, 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative concerns, and to offer actionable guidance for the governance of the design, development and deployment of algorithms.

The modern abundance and prominence of data have 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.

Uno dei principali problemi nello studio della mente e della sua "collocazione" consiste nell'identificazione del corretto Livello di Astrazione a cui le varie descrizioni fenomeniche e le corrispondenti ipotesi esplicative possono essere sviluppate e confrontate. L'articolo contribuisce all'attuale dibattito metodologico in filosofia della mente analizzando due nozioni fondative, quella di modello e quella di simulazione, sulla base del concetto di Livello di Astrazione. L'ambito è quello del Metodo delle Astrazioni, proposto in Floridi e Sanders [2003]. Il Metodo delle Astrazioni si basa su una particolare branca dell'informatica teorica, quella dei Metodi Formali (Roever e Engelhardt [1998]; Woodcock e Davies [1996]), dove la matematica discreta è utilizzata per descrivere analiticamente le specifiche del comportamento di sistemi che elaborano informazioni. I sistemi sono descritti in termini di osservabili e regole di transizione, cioè come sistemi di transizione. Le descrizioni comportano l'utilizzo di variabili che rappresentano osservabili nella realtà, astraendo da altri eventuali osservabili. L'idea di fondo del Metodo delle Astrazioni è che tale processo di filtraggio sia ineliminabile in qualsiasi accesso alla realtà. Il tentativo di sondare un sistema in maggiore profondità produce una maggiore astrazione delle teorie e degli oggetti teorici che descrivono tale sistema. Il Metodo delle Astrazioni si presenta quindi come uno strumento per organizzare e chiarificare le varie teorie che intendono fornire una spiegazione della realtà o della conoscenza. La principale tesi dell'articolo è che "modello" e "simulazione" non possano essere pensati come nozioni a sé stanti, ma che la loro natura possa essere chiarita nei termini di tipi di relazioni tra sistemi di transizione. Nello specifico, si argomenta che la relazione di simulazione è una relazione tra gli osservabili di sistemi di transizione, mentre il modello è una relazione tra le regole di transizione di questi sistemi. Dati questi due nuovi tipi di relazioni, è possibile arricchire con ulteriori strutture di analisi il Metodo delle Astrazioni, estendendo la sua applicabilità. Queste strutture sono discusse nell'articolo come "Reti di Astrazione".

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.

I argue that, according to Descartes, even mathematics is not immune from doubt and absolutely reliable, and hence fails to grant the ultimate justification of science. Descartes offers two arguments and a corollary to support this view. They are sufficient to show that the mathematical atheist cannot justifiably claim to have absolutely certain knowledge even of simple mathematical truths. Philosophical reflection itself turns out to be the only alternative means to provide knowledge with a stable foundation.

Information plays a major role in any moral action. ICT have revolutionized the life of information, from its production and management to its consumption, thus deeply affecting our moral lives. Amid the many issues they have raised, a very serious one, discussed in this paper, is labelled the tragedy of the Good Will. This is represented by the increasing pressure that ICT and their deluge of information are putting on any agent who would like to act morally, when informed about actual or potential evils, but who also lacks the resources to do much about them. In the paper, it is argued that the tragedy may be at least mitigated, if not solved, by seeking to re-establish some equilibrium, through ICT themselves, between what agents know about the world and what they can do to improve it.

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?

In this paper I argue that recent technological transformations in the life-cycle of information have brought about a fourth revolution, in the long process of reassessing humanity’s fundamental nature and role in the universe, namely the idea that we might be informational organisms among many agents, inforgs not so dramatically different from clever, engineered artefacts, but sharing with them a global environment that is ultimately made of information, the infosphere. In view of this important evolution in our self-understanding, and given the sort of IT-mediated interactions that humans will increasingly enjoy with their environment and a variety of other agents, whether natural or synthetic, we have the unique opportunity of developing a new ecological approach to the whole of reality.

Philosophy and Computing explores each of the following areas of technology: the digital revolution; the computer; the Internet and the Web; CD-ROMs and Mulitmedia; databases, textbases, and hypertexts; Artificial Intelligence; the future of computing. Luciano Floridi shows us how the relationship between philosophy and computing provokes a wide range of philosophical questions: is there a philosophy of information? What can be achieved by a classic computer? How can we define complexity? What are the limits of quantam computers? Is the Internet an intellectual space or a polluted environment? What is the paradox in the Strong Artificial Intelligence program? Philosophy and Computing is essential reading for anyone wishing to fully understand both the development and history of information and communication technology as well as the philosophical issues it ultimately raises.

Organisations that design and deploy artificial intelligence (AI) systems increasingly commit themselves to high-level, ethical principles. However, there still exists a gap between principles and practices in AI ethics. One major obstacle organisations face when attempting to operationalise AI Ethics is the lack of a well-defined material scope. Put differently, the question to which systems and processes AI ethics principles ought to apply remains unanswered. Of course, there exists no universally accepted definition of AI, and different systems pose different ethical challenges. Nevertheless, pragmatic problem-solving demands that things should be sorted so that their grouping will promote successful actions for some specific end. In this article, we review and compare previous attempts to classify AI systems for the purpose of implementing AI governance in practice. We find that attempts to classify AI systems proposed in previous literature use one of three mental models: _the Switch_, i.e., a binary approach according to which systems either are or are not considered AI systems depending on their characteristics; _the Ladder_, i.e., a risk-based approach that classifies systems according to the ethical risks they pose; and _the Matrix_, i.e., a multi-dimensional classification of systems that take various aspects into account, such as context, input data, and decision-model. Each of these models for classifying AI systems comes with its own set of strengths and weaknesses. By conceptualising different ways of classifying AI systems into simple mental models, we hope to provide organisations that design, deploy, or regulate AI systems with the vocabulary needed to demarcate the material scope of their AI governance frameworks.

The subject is Sextus Empiricus, one the chief sources of information on ancient philosophy and one of the most influential authors in the history of skepticism. Sextus' works have had an extraordinary influence on western philosophy, and this book provides the first exhaustive and detailed study of their recovery, transmission, and intellectual influence through Late Antiquity, the Middle Ages, and the Renaissance. This study deals with Sextus' biography, as well as the history of the availability and reception of his works. It also contains an extensive bibliographical section, including editions, translations, and commentaries.

As China and the United States strive to be the primary global leader in AI, their visions are coming into conflict. This is frequently painted as a fundamental clash of civilisations, with evidence based primarily around each country’s current political system and present geopolitical tensions. However, such a narrow view claims to extrapolate into the future from an analysis of a momentary situation, ignoring a wealth of historical factors that influence each country’s prevailing philosophy of technology and thus their overarching AI strategies. In this article, we build a philosophy-of-technology-grounded framework to analyse what differences in Chinese and American AI policies exist and, on a fundamental level, why they exist. We support this with Natural Language Processing methods to provide an evidentiary basis for our analysis of policy differences. By looking at documents from three different American presidential administrations––Barack Obama, Donald Trump, and Joe Biden––as well as both national and local policy documents (many available only in Chinese) from China, we provide a thorough comparative analysis of policy differences. This article fills a gap in US–China AI policy comparison and constructs a framework for understanding the origin and trajectory of policy differences. By investigating what factors are informing each country’s philosophy of technology and thus their overall approach to AI policy, we argue that while significant obstacles to cooperation remain, there is room for dialogue and mutual growth.

Altruism is a well-studied phenomenon in the social sciences, but online altruism has received relatively little attention. In this article, we examine several cases of online altruism, and analyse the key characteristics of the phenomenon, in particular comparing and contrasting it against models of traditional donor behaviour. We suggest a novel definition of online altruism, and provide an in-depth, mixed-method study of a significant case, represented by the r/Assistance subreddit. We argue that online altruism can be characterized by its differing experiences compared to traditional giving, from a giver’s point of view, and unique mechanisms and actions made possible by the internet. These findings explain why people give to anonymous strangers online and provide a new perspective on altruism that is important in building a more altruistic internet and society.

The world’s current model for economic development is unsustainable. It encourages high levels of resource extraction, consumption, and waste that undermine positive environmental outcomes. Transitioning to a circular economy (CE) model of development has been proposed as a sustainable alternative. Artificial intelligence (AI) is a crucial enabler for CE. It can aid in designing robust and sustainable products, facilitate new circular business models, and support the broader infrastructures needed to scale circularity. However, to date, considerations of the ethical implications of using AI to achieve a transition to CE have been limited. This article addresses this gap. It outlines how AI is and can be used to transition towards CE, analyzes the ethical risks associated with using AI for this purpose, and supports some recommendations to policymakers and industry on how to minimise these risks.

Since the announcement and establishment of the Oversight Board (OB) by the technology company Meta as an independent institution reviewing Facebook and Instagram’s content moderation decisions, the OB has been subjected to scholarly scrutiny ranging from praise to criticism. However, there is currently no overarching framework for understanding the OB’s various strengths and weaknesses. Consequently, this article analyses, organises, and supplements academic literature, news articles, and Meta and OB documents to understand the OB’s strengths and weaknesses and how it can be improved. Significant strengths include its ability to enhance the transparency of content moderation decisions and processes, to effect reform indirectly through policy recommendations, and its assertiveness in interpreting its jurisdiction and overruling Meta. Significant weaknesses include its limited jurisdiction, limited impact, Meta’s control over the OB’s precedent, and its lack of diversity. The analysis of a recent OB case in Ethiopia shows these strengths and weaknesses in practice. The OB’s relationship with Meta and governments will lead to challenges and opportunities shaping its future development. Reforms to the OB should improve the OB’s control over its precedent, apply OB precedent to currently disputed cases, and clarify the standards for invoking OB precedent. Finally, these reforms provide the foundation for an additional improvement to address the OB’s institutional weaknesses, by involving users in determining whether the OB’s precedent should be applied to decide current content moderation disputes.