This article argues in defence of human–robot friendship. I begin by outlining the standard Aristotelian view of friendship, according to which there are certain necessary conditions which x must meet in order to ‘be a friend’. I explain how the current literature typically uses this Aristotelian view to object to human–robot friendships on theoretical and ethical grounds. Theoretically, a robot cannot

Philosophers interested in the theoretical consequences of predictive processing often assume that predictive processing is an inferentialist and representationalist theory of cognition. More specifically, they assume that predictive processing revolves around approximated Bayesian inferences drawn by inverting a generative model. Generative models, in turn, are said to be structural representations:

Given that empathy allows people to form and maintain satisfying social relationships with other subjects, it is no surprise that this is one of the most studied phenomena in the area of human–robot interaction (HRI). But the fact that the term ‘empathy’ has strong social connotations raises a question: can it be applied to robots? Can we actually use social terms and explanations in relation to these

As AI Systems become increasingly autonomous, they are expected to engage in decision-making processes that have moral implications. In this research we integrate theoretical and empirical lines of thought to address the matters of moral reasoning and moral uncertainty in AI Systems. We reconceptualize the metanormative framework for decision-making under moral uncertainty and we operationalize it

A series of recent developments points towards auditing as a promising mechanism to bridge the gap between principles and practice in AI ethics. Building on ongoing discussions concerning ethics-based auditing, we offer three contributions. First, we argue that ethics-based auditing can improve the quality of decision making, increase user satisfaction, unlock growth potential, enable law-making, and

The use of autonomous and intelligent personal social robots raises questions concerning their moral standing. Moving away from the discussion about direct moral standing and exploring the normative implications of a relational approach to moral standing, this paper offers four arguments that justify giving indirect moral standing to robots under specific conditions based on some of the ways humans—as

The traditional Turing test appeals to an interrogator's judgement to determine whether or not their interlocutor is an intelligent agent. This paper argues that this kind of asymmetric experimental set-up is inappropriate for tracking a property such as intelligence because intelligence is grounded in part by symmetric relations of recognition between agents. In place, it proposes a reciprocal test

An amendment to this paper has been published and can be accessed via the original article.

The Turing Test (TT) is best regarded as a model to test for intelligence, where an entity’s intelligence is inferred from its ability to be attributed with ‘human-likeness’ during a text-based conversation. The problem with this model, however, is that it does not care if or how well an entity produces a meaningful conversation, as long as its interactions are humanlike enough. As a consequence, the

An online game of chess against a human opponent appears to be indistinguishable from a game against a machine: both happen on the screen. Yet, people prefer to play chess against other people despite the fact that machines surpass people in skill. When the philosophers of 1970’s and 1980’s argued that computers will never surpass us in chess, perhaps their intuitions were rather saying “Computers

In computational complexity theory, decision problems are divided into complexity classes based on the amount of computational resources it takes for algorithms to solve them. In theoretical computer science, it is commonly accepted that only functions for solving problems in the complexity class P, solvable by a deterministic Turing machine in polynomial time, are considered to be tractable. In cognitive

In the last 20 years the Turing test has been left further behind by new developments in artificial intelligence. At the same time, however, these developments have revived some key elements of the Turing test: imitation and adversarialness. On the one hand, many generative models, such as generative adversarial networks (GAN), build imitators under an adversarial setting that strongly resembles the

Each of our theories of mental representation provides some insight into how the mind works. However, these insights often seem incompatible, as the debates between symbolic, dynamical, emergentist, sub-symbolic, and grounded approaches to cognition attest. Mental representations—whatever they are—must share many features with each of our theories of representation, and yet there are few hypotheses

In this commentary, we discuss the nature of reversible and irreversible questions, that is, questions that may enable one to identify the nature of the source of their answers. We then introduce GPT-3, a third-generation, autoregressive language model that uses deep learning to produce human-like texts, and use the previous distinction to analyse it. We expand the analysis to present three tests based

In current philosophy of information, different authors have been supporting the veridicality thesis (VT). According to this thesis, an epistemically-oriented concept of information must have truth as one of its necessary conditions. Two challenges can be raised against VT. First, some philosophers object that veridicalists erroneously ignore the informativeness of false messages. Secondly, it is not

Twenty years ago in "Turing's Two Tests for Intelligence" I distinguished two distinct tests to be found in Alan Turing's 1950 paper "Computing Machinery and Intelligence": one by then very well-known, the other neglected. I also explained the significance of the neglected test. This paper revisits some of the points in that paper and explains why they are even more relevant today. It also discusses

Showing remarkable insight into the relationship between language and thought, Alan Turing in 1950 proposed the Imitation Game as a proxy for the question “Can machines think?” and its meaning and practicality have been debated hotly ever since. The Imitation Game has come under criticism within the Computer Science and Artificial Intelligence communities with leading scientists proposing alternatives

How have online communities affected the ways their users construct, view, and define their identity? In this paper, we will approach this issue by considering two philosophical sets of problems related to personal identity: the “Characterization Question” and the “Self-Other Relations Question.” Since these queries have traditionally brought out different problems around the concept of identity, here

This paper looks at philosophical questions that arise in the context of AI alignment. It defends three propositions. First, normative and technical aspects of the AI alignment problem are interrelated, creating space for productive engagement between people working in both domains. Second, it is important to be clear about the goal of alignment. There are significant differences between AI that aligns

The rapid development of natural language processing in the last three decades has drastically changed the way professional translators do their work. Nowadays most of them use computer-assisted translation (CAT) or translation memory (TM) tools whose evolution has been overshadowed by the much more sensational development of machine translation (MT) systems, with which TM tools are sometimes confused

The paper deals with the governance of Unmanned Aircraft Systems (UAS) in European law. Three different kinds of balance have been struck between multiple regulatory systems, in accordance with the sector of the governance of UAS which is taken into account. The first model regards the field of civil aviation law and its European Union (EU)’s regulation: the model looks like a traditional mix of top-down

The goal of the paper is to develop and propose a general model of the state space of AI. Given the breathtaking progress in AI research and technologies in recent years, such conceptual work is of substantial theoretical interest. The present AI hype is mainly driven by the triumph of deep learning neural networks. As the distinguishing feature of such networks is the ability to self-learn, self-learning

Organizations such as the EU High-Level Expert Group on AI and the IEEE have recently formulated ethical principles and (moral) values that should be adhered to in the design and deployment of artificial intelligence (AI). These include respect for autonomy, non-maleficence, fairness, transparency, explainability, and accountability. But how can we ensure and verify that an AI system actually respects

In the 70 years since Alan Turing’s ‘Computing Machinery and Intelligence’ appeared in Mind, there have been two widely-accepted interpretations of the Turing test: the canonical behaviourist interpretation and the rival inductive or epistemic interpretation. These readings are based on Turing’s Mind paper; few seem aware that Turing described two other versions of the imitation game. I have argued

In ‘Computing Machinery and Intelligence’, Turing, sceptical of the question ‘Can machines think?’, quickly replaces it with an experimentally verifiable test: the imitation game. I suggest that for such a move to be successful the test needs to be relevant, expansive, solvable by exemplars, unpredictable, and lead to actionable research. The Imitation Game is only partially successful in this regard

The Turing Test is routinely understood as a behaviourist test for machine intelligence. Diane Proudfoot (Rethinking Turing’s Test, Journal of Philosophy, 2013) has argued for an alternative interpretation. According to Proudfoot, Turing’s claim that intelligence is what he calls ‘an emotional concept’ indicates that he conceived of intelligence in response-dependence terms. As she puts it: ‘Turing’s

Accountability and responsibility are key concepts in the academic and societal debate on Autonomous Weapon Systems, but these notions are often used as high-level overarching constructs and are not operationalised to be useful in practice. “Meaningful Human Control” is often mentioned as a requirement for the deployment of Autonomous Weapon Systems, but a common definition of what this notion means

In the original publication of this article, the Table 1 has been published incorrectly. Now the same has been provided in this correction.

In philosophical logic and metaphysics there is a long-standing debate around the most appropriate structures to represent indeterministic scenarios concerning the future. We reconstruct here such a debate in a computational setting, focusing on the fundamental difference between moment-based and history-based structures. Our presentation is centered around two versions of an indeterministic scenario

Artificial intelligence is becoming increasingly entwined with our daily lives: AIs work as assistants through our phones, control our vehicles, and navigate our vacuums. As these objects become more complex and work within our societies in ways that affect our well-being, there is a growing demand for machine ethics—we want a guarantee that the various automata in our lives will behave in a way that

During the COVID-19 crisis there have been many difficult decisions governments and other decision makers had to make. E.g. do we go for a total lock down or keep schools open? How many people and which people should be tested? Although there are many good models from e.g. epidemiologists on the spread of the virus under certain conditions, these models do not directly translate into the interventions

To address the rising concern that algorithmic decision-making may reinforce discriminatory biases, researchers have proposed many notions of fairness and corresponding mathematical formalizations. Each of these notions is often presented as a one-size-fits-all, absolute condition; however, in reality, the practical and ethical trade-offs are unavoidable and more complex. We introduce a new approach

This paper proposes a methodological redirection of the philosophical debate on artificial moral agency (AMA) in view of increasingly pressing practical needs due to technological development. This “normative approach” suggests abandoning theoretical discussions about what conditions may hold for moral agency and to what extent these may be met by artificial entities such as AI systems and robots.

I describe a suite of reinforcement learning environments in which artificial agents learn to value and respond to moral content and contexts. I illustrate the core principles of the framework by characterizing one such environment, or “gridworld,” in which an agent learns to trade-off between monetary profit and fair dealing, as applied in a standard behavioral economic paradigm. I then highlight

Many philosophical accounts of scientific models fail to distinguish between a simulation model and other forms of models. This failure is unfortunate because there are important differences pertaining to their methodology and epistemology that favor their philosophical understanding. The core claim presented here is that simulation models are rich and complex units of analysis in their own right,

This article addresses the question of how ‘Country of Origin Information’ (COI) reports—that is, research developed and used to support decision-making in the asylum process—can be published in an ethical manner. The article focuses on the risk that published COI reports could be misused and thereby harm the subjects of the reports and/or those involved in their development. It supports a situational

I examine the abstraction/representation theory of computation put forward by Horsman et al., connecting it to the broader notion of modeling, and in particular, model-based explanation, as considered by Rosen. I argue that the ‘representational entities’ it depends on cannot themselves be computational, and that, in particular, their representational capacities cannot be realized by computational

This article describes a heuristic argument for understanding certain physical systems in terms of properties that resemble the beliefs and goals of folk psychology. The argument rests on very simple assumptions. The core of the argument is that predictions about certain events can legitimately be based on assumptions about later events, resembling Aristotelian ‘final causation’; however, more nuanced

An increasing number of technology firms are implementing processes to identify and evaluate the ethical risks of their systems and products. A key part of these review processes is to foresee potential impacts of these technologies on different groups of users. In this article, we use the expression Ethical Foresight Analysis (EFA) to refer to a variety of analytical strategies for anticipating or

This paper attempts to describe and address a specific puzzle related to compositionality in artificial networks such as Deep Neural Networks and machine learning in general. The puzzle identified here touches on a larger debate in Artificial Intelligence related to epistemic opacity but specifically focuses on computational applications of human level linguistic abilities or properties and a special

Closed Loop medical devices such as Closed Loop Deep Brain Stimulation (CL-DBS) and Brain Computer Interface (BCI) are some of the emerging neurotechnologies. New generations of implantable brain–computer interfaces have recently gained success in human clinical trials. These implants detect specific neuronal patterns and provide the subject with information to respond to these patterns. Further, Closed

Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation

Scientific theories of consciousness identify its contents with the spatiotemporal structure of neural population activity. We follow up on this approach by stating and motivating Dynamical Emergence Theory (DET), which defines the amount and structure of experience in terms of the intrinsic topology and geometry of a physical system’s collective dynamics. Specifically, we posit that distinct perceptual

Healthcare robots enable practices that seemed far-fetched in the past. Robots might be the solution to bridge the loneliness that the elderly often experience; they may help wheelchair users walk again, or may help navigate the blind. European Institutions, however, acknowledge that human contact is an essential aspect of personal care and that the insertion of robots could dehumanize caring practices

This paper analyzes the rapid and unexpected rise of deep learning within Artificial Intelligence and its applications. It tackles the possible reasons for this remarkable success, providing candidate paths towards a satisfactory explanation of why it works so well, at least in some domains. A historical account is given for the ups and downs, which have characterized neural networks research and its

The danger of human operators devolving responsibility to machines and failing to detect cases where they fail has been recognised for many years by industrial psychologists and engineers studying the human operators of complex machines. We call it “the control problem”, understood as the tendency of the human within a human–machine control loop to become complacent, over-reliant or unduly diffident

Risk management is a well-known method to face technological challenges through a win–win combination of protective and proactive approaches, fostering the collaboration of operators, researchers, regulators, and industries for the exploitation of new markets. In the field of autonomous and unmanned aerial systems, or UAS, a considerable amount of work has been devoted to risk analysis, the generation

In this paper, we review three influential theories of imagination in order to understand how the dynamics of imagination acts could be modeled using formal languages. While reviewing them, we notice that they are not detailed enough to account for all the mechanisms involved in creating and developing imaginary worlds. We claim those theories could be further refined into what we call the Common Frame

There is widespread agreement that there should be a principle requiring that artificial intelligence (AI) be ‘explicable’. Microsoft, Google, the World Economic Forum, the draft AI ethics guidelines for the EU commission, etc. all include a principle for AI that falls under the umbrella of ‘explicability’. Roughly, the principle states that “for AI to promote and not constrain human autonomy, our

Sovereignty and strategic autonomy are felt to be at risk today, being threatened by the forces of rising international tensions, disruptive digital transformations and explosive growth of cybersecurity incidents. The combination of AI and cybersecurity is at the sharp edge of this development and raises many ethical questions and dilemmas. In this commentary, I analyse how we can understand the ethics

Artificial intelligence (AI) has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic

Two long-standing arguments in cognitive science invoke the assumption that holistic inference is computationally infeasible. The first is Fodor’s skeptical argument toward computational modeling of ordinary inductive reasoning. The second advocates modular computational mechanisms of the kind posited by Cosmides, Tooby and Sperber. Based on advances in machine learning related to Bayes nets, as well

The Epistemology Of Computer Simulation (EOCS) has developed as an epistemological and methodological analysis of simulative sciences using quantitative computational models to represent and predict empirical phenomena of interest. In this paper, Executable Cell Biology (ECB) and Agent-Based Modelling (ABM) are examined to show how one may take advantage of qualitative computational models to evaluate

In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say

Opponents of the computational theory of mind (CTM) have held that the theory is devoid of explanatory content, since whatever computational procedures are said to account for our cognitive attributes will also be realized by a host of other ‘deviant’ physical systems, such as buckets of water and possibly even stones. Such ‘triviality’ claims rely on a simple mapping account (SMA) of physical implementation

It has been argued that the Duhem problem is renewed with computational models since model assumptions having a representational aim and computational assumptions cannot be tested in isolation. In particular, while the Verification and Validation methodology is supposed to prevent such holism, Winsberg (Philos Compass 4:835–845, 2009; Science in the age of computer simulation, University of Chicago

Scientificity is essentially methodology. The use of information technology as methodological instruments in science has been increasing for decades, this raises the question: Does this transform science? This question is the subject of the Special Issue in Minds and Machines “The epistemological significance of methods in computer simulation and machine learning”. We show that there is a technological

The automation of online social life is an urgent issue for researchers and the public alike. However, one of the most significant uses of such technologies seems to have gone largely unnoticed by the research community: religion. Focusing on Islamic Prayer Apps, which automatically post prayers from its users’ accounts, we show that even one such service is already responsible for millions of tweets

We explore the question of whether machines can infer information about our psychological traits or mental states by observing samples of our behaviour gathered from our online activities. Ongoing technical advances across a range of research communities indicate that machines are now able to access this information, but the extent to which this is possible and the consequent implications have not