Under the Superstition Mountains in central Arizona toil those who would rob humankind of its humanity. These gray, soulless monsters methodically tear away at our meaning, our subjectivity, our essence as transcendent beings. With each advance, they steal our freedom and dignity. Who are these denizens of darkness, these usurpers of all that is good and holy? None other than humanity’s arch-foe: The Cognitive Scientists -- AI researchers, fallen philosophers, psychologists, and other benighted lovers of computers. Unless they are stopped, (...) humanity -- you and I -- will soon be nothing but numbers and algorithms locked away on magnetic tape. (shrink)

This paper explores aspects of GPT-3 that have been discussed as harbingers of artificial general intelligence and, in particular, linguistic intelligence. After introducing key features of GPT-3 and assessing its performance in the light of the conversational standards set by Alan Turing in his seminal paper from 1950, the paper elucidates the difference between clever automation and genuine linguistic intelligence. A central theme of this discussion on genuine conversational intelligence is that members of a linguistic community (...) never merely respond “algorithmically” to queries through a selective kind of pattern recognition, because they must also jointly attend and act with other speakers in order to count as genuinely intelligent and trustworthy. This presents a challenge for systems like GPT-3, because representing the world in a way that makes conversational common ground salient is an essentially collective task that we can only achieve jointly with other speakers. Thus, the main difficulty for any artificially intelligent model of conversation is to account for the communicational intentions and motivations of a speaker through joint attention. These joint motivations and intentions seem to be completely absent from the standard way in which systems like GPT-3 and other artificial intelligent systems work. This is not merely a theoretical issue. Since GPT-3 and future iterations of similar systems will likely be available for commercial use through application programming interfaces, caution is needed regarding the risks created by these systems, which pass for “intelligent” but have no genuine communicational intentions, and can thereby produce fake and unreliable linguistic exchanges. (shrink)

In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of artificial intelligence. In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a ‘good AI society’. To do so, we examine how each report addresses the following three topics: the development of a (...) ‘good AI society’; the role and responsibility of the government, the private sector, and the research community in pursuing such a development; and where the recommendations to support such a development may be in need of improvement. Our analysis concludes that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a ‘good AI society’. In order to contribute to fill this gap, in the conclusion we suggest a two-pronged approach. (shrink)

As costs decline and technology inevitably improves, current trends suggest that artificial intelligence (AI) and a variety of "carebots" will increasingly be adopted in medical care. Medical ethicists have long expressed concerns that such technologies remove the human element from medicine, resulting in dehumanization and depersonalized care. However, we argue that where shame presents a barrier to medical care, it is sometimes ethically permissible and even desirable to deploy AI/carebots because (i) dehumanization in medicine is not always morally wrong, (...) and (ii) dehumanization can sometimes better promote and protect important medical values. Shame is often a consequence of the human-to-human element of medical care and can prevent patients from seeking treatment and from disclosing important information to their healthcare provider. Conditions and treatments that are shame-inducing offer opportunities for introducing AI/carebots in a manner that removes the human element of medicine but does so ethically. We outline numerous examples of shame-inducing interactions and how they are overcome by implementing existing and expected developments of AI/carebot technology that remove the human element from care. (shrink)

When reflecting on the nature of skilled action, it is easy to fall into familiar dichotomies such that one construes the flexibility and intelligence of skill at the level of intentional states while characterizing the automatic motor processes that constitute motor skill execution as learned but fixed, invariant, bottom-up, brute-causal responses. In this essay, I will argue that this picture of skilled, automatic, motor processes is overly simplistic. Specifically, I will argue that an adequate account of the learned motor (...) routines that constitute embodied skills cannot be given in a purely bottom-up, brute-causal fashion. Rather, motor control is intelligent all the way down. To establish this, I will first review two recent accounts of skill, Stanley and Krakauer and Papineau, which characterize the automatic motor control responsible for the fine-grained movements constitutive of motor skill as brute, low-level phenomena. I will then isolate five key features that should apply to skilled motor control, if these accounts are correct. Together, the accounts posit that motor control is: ballistic, invariant, independent of general action trajectories, Insensitive to semantic content, and independent of personal-level intentions. In the final section of this paper, I will appeal to optimal control theory for empirical evidence to challenge the commitment to skilled action as qualified by the above features. (shrink)

There is, in some quarters, concern about high–level machine intelligence and superintelligent AI coming up in a few decades, bringing with it significant risks for humanity. In other quarters, these issues are ignored or considered science fiction. We wanted to clarify what the distribution of opinions actually is, what probability the best experts currently assign to high–level machine intelligence coming up within a particular time–frame, which risks they see with that development, and how fast they see these developing. (...) We thus designed a brief questionnaire and distributed it to four groups of experts in 2012/2013. The median estimate of respondents was for a one in two chance that high-level machine intelligence will be developed around 2040-2050, rising to a nine in ten chance by 2075. Experts expect that systems will move on to superintelligence in less than 30 years thereafter. They estimate the chance is about one in three that this development turns out to be ‘bad’ or ‘extremely bad’ for humanity. (shrink)

Accountability is a cornerstone of the governance of artificial intelligence (AI). However, it is often defined too imprecisely because its multifaceted nature and the sociotechnical structure of AI systems imply a variety of values, practices, and measures to which accountability in AI can refer. We address this lack of clarity by defining accountability in terms of answerability, identifying three conditions of possibility (authority recognition, interrogation, and limitation of power), and an architecture of seven features (context, range, agent, forum, standards, (...) process, and implications). We analyse this architecture through four accountability goals (compliance, report, oversight, and enforcement). We argue that these goals are often complementary and that policy-makers emphasise or prioritise some over others depending on the proactive or reactive use of accountability and the missions of AI governance. (shrink)

A major approach to the ethics of artificial intelligence is to use social choice, in which the AI is designed to act according to the aggregate views of society. This is found in the AI ethics of “coherent extrapolated volition” and “bottom–up ethics”. This paper shows that the normative basis of AI social choice ethics is weak due to the fact that there is no one single aggregate ethical view of society. Instead, the design of social choice AI faces (...) three sets of decisions: standing, concerning whose ethics views are included; measurement, concerning how their views are identified; and aggregation, concerning how individual views are combined to a single view that will guide AI behavior. These decisions must be made up front in the initial AI design—designers cannot “let the AI figure it out”. Each set of decisions poses difficult ethical dilemmas with major consequences for AI behavior, with some decision options yielding pathological or even catastrophic results. Furthermore, non-social choice ethics face similar issues, such as whether to count future generations or the AI itself. These issues can be more important than the question of whether or not to use social choice ethics. Attention should focus on these issues, not on social choice. (shrink)

Clark Glymour, Richard Scheines, Peter Spirtes and Kevin Kelly. Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling.

In this 60th anniversary of Business & Society essay, we seek to make three main contributions at the intersection of governance and artificial intelligence. First, we aim to illuminate some of the deeper social, legal, organizational, and democratic challenges of rising AI adoption and resulting algorithmic power by reviewing AI research through a governance lens. Second, we propose an AI governance framework that aims to better assess AI challenges as well as how different governance modalities can support AI. At (...) the heart of our framework lies the governance forces that apply to institutions, organizations, and individuals, who ultimately provide, regulate, and use AI decision-making. We discuss how businesses may harness AI’s economic power through governance solutions without creating or amplifying societal biases and inequalities. Third, as part of our section on future research, we identify a set of governance trade-offs in AI adoption, suggest future research avenues to conceptually strengthen research on the governance of AI, and lay out key policy recommendations. (shrink)

Legg and Hutter, as well as subsequent authors, considered intelligent agents through the lens of interaction with reward-giving environments, attempting to assign numeric intelligence measures to such agents, with the guiding principle that a more intelligent agent should gain higher rewards from environments in some aggregate sense. In this paper, we consider a related question: rather than measure numeric intelligence of one Legg- Hutter agent, how can we compare the relative intelligence of two Legg-Hutter agents? We propose (...) an elegant answer based on the following insight: we can view Legg-Hutter agents as candidates in an election, whose voters are environments, letting each environment vote (via its rewards) which agent (if either) is more intelligent. This leads to an abstract family of comparators simple enough that we can prove some structural theorems about them. It is an open question whether these structural theorems apply to more practical intelligence measures. (shrink)

Artificial intelligence research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re-orientation of AI technologies to facilitate criminal acts, term in this article AI-Crime. AIC is theoretically feasible thanks to published experiments in automating fraud targeted at social media users, as well as demonstrations of AI-driven manipulation of simulated markets. However, because AIC is still a relatively young and (...) inherently interdisciplinary area—spanning socio-legal studies to formal science—there is little certainty of what an AIC future might look like. This article offers the first systematic, interdisciplinary literature analysis of the foreseeable threats of AIC, providing ethicists, policy-makers, and law enforcement organisations with a synthesis of the current problems, and a possible solution space. (shrink)

Artificial Intelligence, both as a hope of making substantial progress, and a fear of the unknown and unimaginable, has its roots in human dreams. These dreams are materialized by means of rational intellectual efforts. We see the beginnings of such a process in Lullus’s fancies. Many scholars and enthusiasts participated in the development of Lullus’s art, ars combinatoria. Amongst them, Athanasius Kircher distinguished himself. Gottfried Leibniz ended the period in which the idea of artificial intelligence was shaped, and (...) started the new period, in which artificial intelligence could be considered part of science, by today’s standards. (shrink)

BackgroundHealthcare providers have to make ethically complex clinical decisions which may be a source of stress. Researchers have recently introduced Artificial Intelligence (AI)-based applications to assist in clinical ethical decision-making. However, the use of such tools is controversial. This review aims to provide a comprehensive overview of the reasons given in the academic literature for and against their use.MethodsPubMed, Web of Science, Philpapers.org and Google Scholar were searched for all relevant publications. The resulting set of publications was title and (...) abstract screened according to defined inclusion and exclusion criteria, resulting in 44 papers whose full texts were analysed using the Kuckartz method of qualitative text analysis.ResultsArtificial Intelligence might increase patient autonomy by improving the accuracy of predictions and allowing patients to receive their preferred treatment. It is thought to increase beneficence by providing reliable information, thereby, supporting surrogate decision-making. Some authors fear that reducing ethical decision-making to statistical correlations may limit autonomy. Others argue that AI may not be able to replicate the process of ethical deliberation because it lacks human characteristics. Concerns have been raised about issues of justice, as AI may replicate existing biases in the decision-making process.ConclusionsThe prospective benefits of using AI in clinical ethical decision-making are manifold, but its development and use should be undertaken carefully to avoid ethical pitfalls. Several issues that are central to the discussion of Clinical Decision Support Systems, such as justice, explicability or human–machine interaction, have been neglected in the debate on AI for clinical ethics so far.Trial registrationThis review is registered at Open Science Framework (https://osf.io/wvcs9). (shrink)

An arms race for an artificial general intelligence would be detrimental for and even pose an existential threat to humanity if it results in an unfriendly AGI. In this paper, an all-pay contest model is developed to derive implications for public policy to avoid such an outcome. It is established that, in a winner-takes-all race, where players must invest in R&D, only the most competitive teams will participate. Thus, given the difficulty of AGI, the number of competing teams is (...) unlikely ever to be very large. It is also established that the intention of teams competing in an AGI race, as well as the possibility of an intermediate outcome, is important. The possibility of an intermediate prize will raise the probability of finding the dominant AGI application and, hence, will make public control more urgent. It is recommended that the danger of an unfriendly AGI can be reduced by taxing AI and using public procurement. This would reduce the pay-off of contestants, raise the amount of R&D needed to compete, and coordinate and incentivize co-operation. This will help to alleviate the control and political problems in AI. Future research is needed to elaborate the design of systems of public procurement of AI innovation and for appropriately adjusting the legal frameworks underpinning high-tech innovation, in particular dealing with patenting by AI. (shrink)

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. (shrink)

The rapid diffusion of artificial intelligence (AI) technologies in the defence domain raises challenges for the ethical governance of these systems. A recent shift from the what to the how of AI ethics sees a nascent body of literature published by defence organisations focussed on guidance to implement AI ethics principles. These efforts have neglected a crucial intermediate step between principles and guidance concerning the elicitation of ethical requirements for specifying the guidance. In this article, we outline the key (...) normative choices and corresponding tradeoffs that are involved in specifying guidance for the implementation of AI ethics principles in the defence domain. These correspond to: the AI lifecycle model used; the scope of stakeholder involvement; the accountability goals chosen; the choice of auditing requirements; and the choice of mechanisms for transparency and traceability. We provide initial recommendations for navigating these tradeoffs and highlight the importance of a pro-ethical institutional culture. (shrink)

Linguistics. Artificial intelligence. Related fields. Computation.

As the capabilities of artificial intelligence systems improve, it becomes important to constrain their actions to ensure their behaviour remains beneficial to humanity. A variety of ethical, legal and safety-based frameworks have been proposed as a basis for designing these constraints. Despite their variations, these frameworks share the common characteristic that decision-making must consider multiple potentially conflicting factors. We demonstrate that these alignment frameworks can be represented as utility functions, but that the widely used Maximum Expected Utility paradigm provides (...) insufficient support for such multiobjective decision-making. We show that a Multiobjective Maximum Expected Utility paradigm based on the combination of vector utilities and non-linear action–selection can overcome many of the issues which limit MEU’s effectiveness in implementing aligned AI. We examine existing approaches to multiobjective AI, and identify how these can contribute to the development of human-aligned intelligent agents. (shrink)

An exploration of the important philosophical issues and concerns related to artificial intelligence. The book focuses on the philosphical, rather than the technical or technological aspects of artificial intelligence.

This paper presents a critical reflection on insights into the ongoing endeavours for community engagement by Ayara and MAL; two urban grassroot organisations in Bogota, Colombia, where a long history of internal conflicts has resulted in diverse human right violations. The paper presents examples of the grassroots organisations’ unique methods of engagement that promotes building collective intelligence from the bottom–up through creative collaboration and design processes, leading to rebuilding social fabrics that support the common good for the people of (...) Bogota. (shrink)

On the whole, the US Algorithmic Accountability Act of 2022 (US AAA) is a pragmatic approach to balancing the benefits and risks of automated decision systems. Yet there is still room for improvement. This commentary highlights how the US AAA can both inform and learn from the European Artificial Intelligence Act (EU AIA).

Machine learning algorithms are increasingly used to support decision-making in the exercise of public authority. Here, we argue that an important consideration has been overlooked in previous discussions: whether the use of ML undermines the democratic legitimacy of public institutions. From the perspective of democratic legitimacy, it is not enough that ML contributes to efficiency and accuracy in the exercise of public authority, which has so far been the focus in the scholarly literature engaging with these developments. According to one (...) influential theory, exercises of administrative and judicial authority are democratically legitimate if and only if administrative and judicial decisions serve the ends of the democratic law maker, are based on reasons that align with these ends and are accessible to the public. These requirements are not satisfied by decisions determined through ML since such decisions are determined by statistical operations that are opaque in several respects. However, not all ML-based decision support systems pose the same risk, and we argue that a considered judgment on the democratic legitimacy of ML in exercises of public authority need take the complexity of the issue into account. This paper outlines considerations that help guide the assessment of whether a ML undermines democratic legitimacy when used to support public decisions. We argue that two main considerations are pertinent to such normative assessment. The first is the extent to which ML is practiced as intended and the extent to which it replaces decisions that were previously accessible and based on reasons. The second is that uses of ML in exercises of public authority should be embedded in an institutional infrastructure that secures reason giving and accessibility. (shrink)

Michael Hand's interesting analysis of the concept of intelligence crucially depends upon three assumptions: firstly, that there is an ordinary use of the term which, when applied to an individual is perfectly general and not context dependent. Secondly, that this use is best cashed in terms of aptitude. Thirdly, that the aptitude in question is to be explained in terms of theorizing. I shall argue in what follows that the first assumption may be true, but, if it is true, (...) this presents a problem for Hand's analysis and not a path to a solution. That the second assumption is false but, even if it were true, it would sit badly with assumption one. And, finally, that the third assumption runs into too many difficulties to be acceptable. (shrink)

We discuss, first, TUring's role in the development of the computer; second, the early history of Artificial Intelligence (to 1956); and third, TUring's fa- mous imitation game, now universally known as the TUring test, which he proposed in cameo form in 1948 and then more fully in 1950 and 1952. Various objections have been raised to Turing's test: we describe some of the most prominent and explain why, in our view, they fail.

Gilbert Harman and other situationists have argued, on thefollowing grounds, that many ordinary moral judgments are false.First, many moral judgments posit robust personal character traits inthe course of describing or explaining individual human behavior.Second, the empirical evidence strongly suggests these traits do notexist. I sketch some of the reasoning behind situationism and arguethat Harman’s view cannot be entirely right. He is himselfcommitted to there being at least one robust individual charactertrait, namely a form of personal intelligence. Moreover, the notion (...) ofa situation upon which he relies is inadequate to his purpose. (shrink)

Reference to security and intelligence in education today will undoubtedly elicit concerns over terrorism, radicalisation and, in the UK, counter-terrorism measures such as Channel and Prevent (UK...

This study explores the effect of a short ethics intervention—a chapter of business ethics in a business course—on perceptions of business courses and personal values toward making money and making ethical decisions and Monetary Intelligence. Since attitudes predict intentions and behaviors, Monetary Intelligence, a form of social intelligence, is defined as the extent to which individuals monitor their own monetary motive, behavior, and cognition; apply the information to evaluate critical concerns and options; select strategies to achieve financial (...) goals; and reach ultimate success and subjective well-being. I theorize that the affective aspect of MI is unrelated to perceptions of “course work,” yet it is positively related to their “personal values” toward making money, but negatively related to making ethical decisions. Individuals with high MI have low interests in making money, but high levels of intrinsic religiosity and recall of the Ten Commandments and high interests in making ethical decisions and making the grade. Based on data from multiple panels and multiple sources, this study provides the following discoveries. Contrary to expectations, there are no differences in students’ perceptions of course work and their personal values toward making money and making ethical decisions between two measures—before and after the ethics intervention. Results of this study not only provide empirical supports for the bright side of theory of Monetary Intelligence but also reveal a new paradox—recall of the Ten Commandments is positively related to making ethical decisions, but negatively related to making money. Results illustrate important theoretical, empirical, and practical implications to the literature of money attitudes, religiosity, intrinsic motivation, and business ethics. (shrink)

The human-centered AI approach posits a future in which the work done by humans and machines will become ever more interactive and integrated. This article takes human-centered AI one step further. It argues that the integration of human and machine intelligence is achievable only if human organizations—not just individual human workers—are kept “in the loop.” We support this argument with evidence of two case studies in the area of predictive maintenance, by which we show how organizational practices are needed (...) and shape the use of AI/ML. Specifically, organizational processes and outputs such as decision-making workflows, etc. directly influence how AI/ML affects the workplace, and they are crucial for answering our first and second research questions, which address the pre-conditions for keeping humans in the loop and for supporting continuous and reliable functioning of AI-based socio-technical processes. From the empirical cases, we extrapolate a concept of “keeping the organization in the loop” that integrates four different kinds of loops: AI use, AI customization, AI-supported original tasks, and taking contextual changes into account. The analysis culminates in a systematic framework of keeping the organization in the loop look based on interacting organizational practices. (shrink)

This article draws on the sociology of expectations to examine the construction of expectations of ‘ethical AI’ and considers the implications of these expectations for communication governance. We first analyse a range of public documents to identify the key actors, mechanisms and issues which structure societal expectations around artificial intelligence and an emerging discourse on ethics. We then explore expectations of AI and ethics through a survey of members of the public. Finally, we discuss the implications of our findings (...) for the role of AI in communication governance. We find that, despite societal expectations that we can design ethical AI, and public expectations that developers and governments should share responsibility for the outcomes of AI use, there is a significant divergence between these expectations and the ways in which AI technologies are currently used and governed in large scale communication systems. We conclude that discourses of ‘ethical AI’ are generically performative, but to become more effective we need to acknowledge the limitations of contemporary AI and the requirement for extensive human labour to meet the challenges of communication governance. An effective ethics of AI requires domain appropriate AI tools, updated professional practices, dignified places of work and robust regulatory and accountability frameworks. (shrink)

Les ordinateurs, peuvent-ils être un instrument pour faire de la philosophie? La réponse proposée par cet article développe dix moments dont les thèmes sont.