BackgroundMachine learning-based clinical decision support systems (ML_CDSS) are increasingly employed in various sectors of health care aiming at supporting clinicians’ practice by matching the characteristics of individual patients with a computerised clinical knowledge base. Some studies even indicate that ML_CDSS may surpass physicians’ competencies regarding specific isolated tasks. From an ethical perspective, however, the usage of ML_CDSS in medical practice touches on a range of fundamental normative issues. This article aims to add to the ethical discussion by using professionalisation theory (...) as an analytical lens for investigating how medical action at the micro level and the physician–patient relationship might be affected by the employment of ML_CDSS.Main textProfessionalisation theory, as a distinct sociological framework, provides an elaborated account of what constitutes client-related professional action, such as medical action, at its core and why it is more than pure expertise-based action. Professionalisation theory is introduced by presenting five general structural features of professionalised medical practice: (i) the patient has a concern; (ii) the physician deals with the patient’s concern; (iii) s/he gives assistance without patronising; (iv) s/he regards the patient in a holistic manner without building up a private relationship; and (v) s/he applies her/his general expertise to the particularities of the individual case. Each of these five key aspects are then analysed regarding the usage of ML_CDSS, thereby integrating the perspectives of professionalisation theory and medical ethics.ConclusionsUsing ML_CDSS in medical practice requires the physician to pay special attention to those facts of the individual case that cannot be comprehensively considered by ML_CDSS, for example, the patient’s personality, life situation or cultural background. Moreover, the more routinized the use of ML_CDSS becomes in clinical practice, the more that physicians need to focus on the patient’s concern and strengthen patient autonomy, for instance, by adequately integrating digital decision support in shared decision-making. (shrink)

Important decisions that impact humans lives, livelihoods, and the natural environment are increasingly being automated. Delegating tasks to so-called automated decision-making systems can improve efficiency and enable new solutions. However, these benefits are coupled with ethical challenges. For example, ADMS may produce discriminatory outcomes, violate individual privacy, and undermine human self-determination. New governance mechanisms are thus needed that help organisations design and deploy ADMS in ways that are ethical, while enabling society to reap the full economic and social benefits of (...) automation. In this article, we consider the feasibility and efficacy of ethics-based auditing as a governance mechanism that allows organisations to validate claims made about their ADMS. Building on previous work, we define EBA as a structured process whereby an entity’s present or past behaviour is assessed for consistency with relevant principles or norms. We then offer three contributions to the existing literature. First, we provide a theoretical explanation of how EBA can contribute to good governance by promoting procedural regularity and transparency. Second, we propose seven criteria for how to design and implement EBA procedures successfully. Third, we identify and discuss the conceptual, technical, social, economic, organisational, and institutional constraints associated with EBA. We conclude that EBA should be considered an integral component of multifaced approaches to managing the ethical risks posed by ADMS. (shrink)

Artificial Intelligence (AI) is already having a major impact on society. As a result, many organizations have launched a wide range of initiatives to establish ethical principles for the adoption of socially beneficial AI. Unfortunately, the sheer volume of proposed principles threatens to overwhelm and confuse. How might this problem of ‘principle proliferation’ be solved? In this paper, we report the results of a fine-grained analysis of several of the highest-profile sets of ethical principles for AI. We assess whether these (...) principles converge upon a set of agreed-upon principles, or diverge, with significant disagreement over what constitutes ‘ethical AI.’ Our analysis finds a high degree of overlap among the sets of principles we analyze. We then identify an overarching framework consisting of five core principles for ethical AI. Four of them are core principles commonly used in bioethics: beneficence, non-maleficence, autonomy, and justice. On the basis of our comparative analysis, we argue that a new principle is needed in addition: explicability, understood as incorporating both the epistemological sense of intelligibility (as an answer to the question ‘how does it work?’) and in the ethical sense of accountability (as an answer to the question: ‘who is responsible for the way it works?’). In the ensuing discussion, we note the limitations and assess the implications of this ethical framework for future efforts to create laws, rules, technical standards, and best practices for ethical AI in a wide range of contexts. (shrink)

As the range of potential uses for Artificial Intelligence (AI), in particular machine learning (ML), has increased, so has awareness of the associated ethical issues. This increased awareness has led to the realisation that existing legislation and regulation provides insufficient protection to individuals, groups, society, and the environment from AI harms. In response to this realisation, there has been a proliferation of principle-based ethics codes, guidelines and frameworks. However, it has become increasingly clear that a significant gap exists between the (...) theory of AI ethics principles and the practical design of AI systems. In previous work , we analysed whether it is possible to close this gap between the ‘what’ and the ‘how’ of AI ethics through the use of tools and methods designed to help AI developers, engineers, and designers translate principles into practice. We concluded that this method of closure is currently ineffective as almost all existing translational tools and methods are either too flexible (and thus vulnerable to ethics washing) or too strict (unresponsive to context). This raised the question: if, even with technical guidance, AI ethics is challenging to embed in the process of algorithmic design, is the entire pro-ethical design endeavour rendered futile? And, if no, then how can AI ethics be made useful for AI practitioners? This is the question we seek to address here by exploring why principles and technical translational tools are still needed even if they are limited, and how these limitations can be potentially overcome by providing theoretical grounding of a concept that has been termed ‘Ethics as a Service’. (shrink)

Artificial intelligence is shaping strategy, activities, interactions, and relationships in business and specifically in marketing. The drawback of the substantial opportunities AI systems and applications provide in marketing are ethical controversies. Building on the literature on AI ethics, the authors systematically scrutinize the ethical challenges of deploying AI in marketing from a multi-stakeholder perspective. By revealing interdependencies and tensions between ethical principles, the authors shed light on the applicability of a purely principled, deontological approach to AI ethics in marketing. To (...) reconcile some of these tensions and account for the AI-for-social-good perspective, the authors make suggestions of how AI in marketing can be leveraged to promote societal and environmental well-being. (shrink)

Computers are used to make decisions in an increasing number of domains. There is widespread agreement that some of these uses are ethically problematic. Far less clear is where ethical problems arise, and what might be done about them. This paper expands and defends the Ethical Gravity Thesis: ethical problems that arise at higher levels of analysis of an automated decision-making system are inherited by lower levels of analysis. Particular instantiations of systems can add new problems, but not ameliorate more (...) general ones. We defend this thesis by adapting Marr’s famous 1982 framework for understanding information-processing systems. We show how this framework allows one to situate ethical problems at the appropriate level of abstraction, which in turn can be used to target appropriate interventions. (shrink)

On virtue ethical grounds, Barbro Fröding and Martin Peterson recently recommended that near-future AIs should be developed as ‘Friendly AI’. AI in social interaction with humans should be programmed such that they mimic aspects of human friendship. While it is a reasonable goal to implement AI systems interacting with humans as Friendly AI, I identify four issues that need to be addressed concerning Friendly AI with Fröding’s and Peterson’s understanding of Friendly AI as a starting point. In a first step, (...) I briefly recapitulate Fröding’s and Peterson’s arguments for Friendly AI. I then highlight some issues with Fröding’s and Peterson’s approach and line of reasoning and identify four problems related to the notion of Friendly AI, which all pertain to the role and need for humans’ moral development. These are that one should consider the moral tendencies and preferences of the humans interacting with a friendly AI, it needs to be considered whether the humans interacting with a Friendly AI are still developing their virtues and character traits, the indirect effects of replacing humans with Friendly AI should be considered with respect to the possibilities for humans to develop their moral virtues and that the question whether the AI is perceived as some form of Artificial General Intelligence cannot be neglected. In conclusion, I argue that all of these four problems are related to humans moral development and that this observation strongly emphasizes the role and need for humans moral development in correlation to the accelerating development of AI-systems. (shrink)

The creation of artificial moral systems requires us to make difficult choices about which of varying human value sets should be instantiated. The industry-standard approach is to seek and encode moral consensus. Here we argue, based on evidence from empirical psychology, that encoding current moral consensus risks reinforcing current norms, and thus inhibiting moral progress. However, so do efforts to encode progressive norms. Machine ethics is thus caught between a rock and a hard place. The problem is particularly acute when (...) progress beyond prevailing moral norms is particularly urgent, as is currently the case due to the inadequacy of prevailing moral norms in the face of the climate and ecological crisis. (shrink)

This article develops a conceptual framework for regulating Artificial Intelligence (AI) that encompasses all stages of modern public policy-making, from the basics to a sustainable governance. Based on a vast systematic review of the literature on Artificial Intelligence Regulation (AIR) published between 2010 and 2020, a dispersed body of knowledge loosely centred around the “framework” concept was organised, described, and pictured for better understanding. The resulting integrative framework encapsulates 21 prior depictions of the policy-making process, aiming to achieve gold-standard societal (...) values, such as fairness, freedom and long-term sustainability. This challenge of integrating the AIR literature was matched by the identification of a structural common ground among different approaches. The AIR framework results from an effort to identify and later analytically deduce synthetic, and generic tool for a country-specific, stakeholder-aware analysis of AIR matters. Theories and principles as diverse as Agile and Ethics were combined in the “AIR framework”, which provides a conceptual lens for societies to think collectively and make informed policy decisions related to what, when, and how the uses and applications of AI should be regulated. Moreover, the AIR framework serves as a theoretically sound starting point for endeavours related to AI regulation, from legislation to research and development. As we know, the (potential) impacts of AI on society are immense, and therefore the discourses, social negotiations, and applications of this technology should be guided by common grounds based on contemporary governance techniques, and social values legitimated via dialogue and scientific research. (shrink)

Software systems play an ever more important role in our lives and software engineers and their companies find themselves in a position where they are held responsible for ethical issues that may arise. In this paper, we try to disentangle ethical considerations that can be performed at the level of the software engineer from those that belong in the wider domain of business ethics. The handling of ethical problems that fall into the responsibility of the engineer has traditionally been addressed (...) by the publication of Codes of Ethics and Conduct. We argue that these Codes are barely able to provide normative orientation in software development. The main contribution of this paper is, thus, to analyze the normative features of Codes of Ethics in software engineering and to explicate how their value-based approach might prevent their usefulness from a normative perspective. Codes of Conduct cannot replace ethical deliberation because they do not and cannot offer guidance because of their underdetermined nature. This lack of orientation, we argue, triggers reactive behavior such as “cherry-picking,” “risk of indifference,” “ex-post orientation,” and the “desire to rely on gut feeling.” In the light of this, we propose to implement ethical deliberation within software development teams as a way out. (shrink)

The ethics of artificial intelligence is a widely discussed topic. There are numerous initiatives that aim to develop the principles and guidance to ensure that the development, deployment and use of AI are ethically acceptable. What is generally unclear is how organisations that make use of AI understand and address these ethical issues in practice. While there is an abundance of conceptual work on AI ethics, empirical insights are rare and often anecdotal. This paper fills the gap in our current (...) understanding of how organisations deal with AI ethics by presenting empirical findings collected using a set of ten case studies and providing an account of the cross-case analysis. The paper reviews the discussion of ethical issues of AI as well as mitigation strategies that have been proposed in the literature. Using this background, the cross-case analysis categorises the organisational responses that were observed in practice. The discussion shows that organisations are highly aware of the AI ethics debate and keen to engage with ethical issues proactively. However, they make use of only a relatively small subsection of the mitigation strategies proposed in the literature. These insights are of importance to organisations deploying or using AI, to the academic AI ethics debate, but maybe most valuable to policymakers involved in the current debate about suitable policy developments to address the ethical issues raised by AI. (shrink)

In the last years, artificial intelligence (AI) safety gained international recognition in the light of heterogeneous safety-critical and ethical issues that risk overshadowing the broad beneficial impacts of AI. In this context, the implementation of AI observatory endeavors represents one key research direction. This paper motivates the need for an inherently _transdisciplinary_ AI observatory approach integrating diverse retrospective and counterfactual views. We delineate aims and limitations while providing hands-on-advice utilizing _concrete practical examples_. Distinguishing between unintentionally and intentionally triggered AI risks (...) with diverse socio-psycho-technological impacts, we exemplify a _retrospective descriptive analysis_ followed by a _retrospective counterfactual risk analysis_. Building on these AI observatory tools, we present near-term transdisciplinary guidelines for AI safety. As further contribution, we discuss _differentiated_ and tailored long-term directions through the lens of two disparate modern AI safety paradigms. For simplicity, we refer to these two different paradigms with the terms _artificial stupidity_ (AS) and _eternal creativity_ (EC) respectively. While both AS and EC acknowledge the need for a hybrid cognitive-affective approach to AI safety and overlap with regard to many short-term considerations, they differ fundamentally in the nature of multiple envisaged long-term solution patterns. By compiling relevant underlying _contradistinctions_, we aim to provide _future-oriented_ incentives for constructive dialectics in practical and theoretical AI safety research. (shrink)

What does Cyberpunk and AI Ethics have to do with each other? Cyberpunk is a sub-genre of science fiction that explores the post-human relationships between human experience and technology. One similarity between AI Ethics and Cyberpunk literature is that both seek a dialogue in which the reader may inquire about the future and the ethical and social problems that our technological advance may bring upon society. In recent years, an increasing number of ethical matters involving AI have been pointed and (...) debated, and several ethical principles and guides have been suggested as governance policies for the tech industry. However, would this be the role of AI Ethics? To serve as a soft and ambiguous version of the law? I would like to promote in this article a more Cyberpunk way of doing AI Ethics, whit a more anarchic way of governance. In this study, I will seek to expose some of the deficits of the underlying power structures of our society, and suggest that AI governance be subject to public opinion, so that ‘good AI’ can become ‘good AI for all’. (shrink)

The debate about the ethical implications of Artificial Intelligence dates from the 1960s :741–742, 1960; Wiener in Cybernetics: or control and communication in the animal and the machine, MIT Press, New York, 1961). However, in recent years symbolic AI has been complemented and sometimes replaced by Neural Networks and Machine Learning techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles—the (...) ‘what’ of AI ethics —rather than on practices, the ‘how.’ Awareness of the potential issues is increasing at a fast rate, but the AI community’s ability to take action to mitigate the associated risks is still at its infancy. Our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers apply ethics at each stage of the Machine Learning development pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs. (shrink)

Language agents are AI systems capable of understanding and responding to natural language, potentially facilitating the process of encoding human goals into AI systems. However, this paper argues that if language agents can achieve easy alignment, they also increase the risk of malevolent agents building harmful AI systems aligned with destructive intentions. The paper contends that if training AI becomes sufficiently easy or is perceived as such, it enables malicious actors, including rogue states, terrorists, and criminal organizations, to create powerful (...) AI systems devoted to their nefarious aims. Given the strong incentives for such groups and the rapid progress in AI capabilities, this risk demands serious attention. In addition, the paper highlights considerations suggesting that the negative impacts of language agents may outweigh the positive ones, including the potential irreversibility of certain negative AI impacts. The overarching lesson is that various AI-related issues are intimately connected with each other, and we must recognize this interconnected nature when addressing those issues. (shrink)

The call to make artificial intelligence (AI) more democratic, or to “democratize AI,” is sometimes framed as a promising response for mitigating algorithmic injustice or making AI more aligned with social justice. However, the notion of “democratizing AI” is elusive, as the phrase has been associated with multiple meanings and practices, and the extent to which it may help mitigate algorithmic injustice is still underexplored. In this paper, based on a socio-technical understanding of algorithmic injustice, I examine three notable notions (...) of democratizing AI and their associated measures—democratizing AI use, democratizing AI development, and democratizing AI governance—regarding their respective prospects and limits in response to algorithmic injustice. My examinations reveal that while some versions of democratizing AI bear the prospect of mitigating the concern of algorithmic injustice, others are somewhat limited and might even function to perpetuate unjust power hierarchies. This analysis thus urges a more fine-grained discussion on how to democratize AI and suggests that closer scrutiny of the power dynamics embedded in the socio-technical structure can help guide such explorations. (shrink)

One of the predominant debates in AI Ethics is the worry and necessity to create fair, transparent and accountable algorithms that do not perpetuate current social inequities. I offer a critical analysis of Reuben Binns’s argument in which he suggests using public reason to address the potential bias of the outcomes of machine learning algorithms. In contrast to him, I argue that ultimately what is needed is not public reason per se, but an audit of the implicit moral assumptions of (...) societies within which algorithms are built and applied. Public justification is appealing since it offers us the possibility to align the decision-making outcomes of the algorithm with the core moral values of stakeholders within a constitutional democratic society. My concern is that the common moral principles that form the foundation of public reason are not necessarily neutral, as they still express specific moral ideals and normative standards even if there is moral agreement by society as a whole, or among different stakeholders within society. Appealing to such normative standards may thus still lead to algorithmic outcomes being biased as common moral values may very well still be discriminatory even though they are formed from a consensus, and even if public reason is applied as a kind of filter for potential algorithmic outcomes. Hence, I argue that these implicit moral norms within society that we take as a given in public reasoning, need to be audited from generation to generation in order to effectively mitigate potential algorithmic bias. (shrink)

AI has become the poster child for a certain kind of thinking which holds that some technologies can become objective, independent and emergent entities which can evolve beyond the control of their creators. This thinking is not new however. It is a product of certain philosophical ideas such as materialism, a common-sense world of objective and independent objects, a correspondence theory of truth, and so forth, which are centered around the pre-eminence of science, epistemology, and logical reasoning, among others, as (...) the supremely valid modes of engaging experience. This paper aims to critically examine the synthesis of the development program for AI to be found in the writings of computer scientist John McCarthy (1927–2011). McCarthy has been called one of the founding fathers of AI. First, some of the main themes which recur in his writings from as early as the mid nineteen fifties in the Dartmouth proposal, up to his late writings, are considered. A discussion of the goals that such a program implies, follows, in terms of society’s relation with AI based technology and the nature and purpose of AI itself. These implications are arguably related to social control and are also moral. Ultimately, McCarthy’s idea that AI is and can be built upon a paradigm of reasoning and logic so as to simulate a perfectly rational abstraction of human thinking is shown to be motivated by his view that AI systems should be controlled, as servants. This understanding of human thinking is questionable, however, given current research on human irrationality, as well as on the argumentative role of reasoning. And yet, the process of development toward the unachievable goal of controllable, rational, logical AI systems, has handily come to serve as a blind for increasing techno-corporate control of society, with techno-corporate interests displacing blame for devaluations and harms caused by their push to develop the technology to AI systems which they can conveniently ‘lose control of.’. (shrink)

This article provides a methodology for the interpretation of AI ethics principles to specify ethical criteria for the development and deployment of AI systems in high-risk domains. The methodology consists of a three-step process deployed by an independent, multi-stakeholder ethics board to: (1) identify the appropriate level of abstraction for modelling the AI lifecycle; (2) interpret prescribed principles to extract specific requirements to be met at each step of the AI lifecycle; and (3) define the criteria to inform purpose- and (...) context-specific balancing of the principles. The methodology presented in this article is designed to be agile, adaptable, and replicable, and when used as part of a pro-ethical institutional culture, will help to foster the ethical design, development, and deployment of AI systems. The application of the methodology is illustrated through reference to the UK Ministry of Defence AI ethics principles. (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)

Responsible AI (RAI) guidelines aim to ensure that AI systems respect democratic values. While a step in the right direction, they currently fail to impact practice. Our work discusses reasons for this lack of impact and clusters them into five areas: (1) the abstract nature of RAI guidelines, (2) the problem of selecting and reconciling values, (3) the difficulty of operationalising RAI success metrics, (4) the fragmentation of the AI pipeline, and (5) the lack of internal advocacy and accountability. Afterwards, (...) we introduce a number of approaches to RAI from a range of disciplines, exploring their potential as solutions to the identified challenges. We anchor these solutions in practice through concrete examples, bridging the gap between the theoretical considerations of RAI and on-the-ground processes that currently shape how AI systems are built. Our work considers the socio-technical nature of RAI limitations and the resulting necessity of producing socio-technical solutions. (shrink)

Purpose Artificial intelligence (AI) has sparked interest in various areas, including marketing. However, this exhilaration is being tempered by growing concerns about the moral and legal implications of using AI in marketing. Although previous research has revealed various ethical and legal issues, such as algorithmic discrimination and data privacy, there are no definitive answers. This paper aims to fill this gap by investigating AI’s ethical and legal concerns in marketing and suggesting feasible solutions. Design/methodology/approach The paper synthesises information from academic (...) articles, industry reports, case studies and legal documents through a thematic literature review. A qualitative analysis approach categorises and interprets ethical and legal challenges and proposes potential solutions. Findings The findings of this paper raise concerns about ethical and legal challenges related to AI in the marketing area. Ethical concerns related to discrimination, bias, manipulation, job displacement, absence of social interaction, cybersecurity, unintended consequences, environmental impact, privacy and legal issues such as consumer security, responsibility, liability, brand protection, competition law, agreements, data protection, consumer protection and intellectual property rights are discussed in the paper, and their potential solutions are discussed. Research limitations/implications Notwithstanding the interesting insights gathered from this investigation of the ethical and legal consequences of AI in marketing, it is important to recognise the limits of this research. Initially, the focus of this study is confined to a review of the most important ethical and legal issues pertaining to AI in marketing. Additional possible repercussions, such as those associated with intellectual property, contracts and licencing, should be investigated more deeply in future studies. Despite the fact that this study gives various answers and best practices for tackling the stated ethical and legal concerns, the viability and efficacy of these solutions may differ depending on the context and industry. Thus, more research and case studies are required to evaluate the applicability and efficacy of these solutions in other circumstances. This research is mostly based on a literature review and may not represent the experiences or opinions of all stakeholders engaged in AI-powered marketing. Further study might involve interviews or surveys with marketing professionals, customers and other key stakeholders to offer a full knowledge of the practical difficulties and solutions. Because of the rapid speed of technical progress, AI’s ethical and regulatory ramifications in marketing are continually increasing. Consequently, this work should be a springboard for more research and continuing conversations on this subject. Practical implications This study’s findings have several practical implications for marketing professionals. Emphasising openness and explainability: Marketing professionals should prioritise transparency in their use of AI, ensuring that customers are fully informed about data collection and utilisation for targeted advertising. By promoting openness and explainability, marketers can foster customer trust and avoid the negative consequences of a lack of transparency. Establishing ethical guidelines: Marketing professionals need to develop ethical rules for the creation and implementation of AI-powered marketing strategies. Adhering to ethical principles ensures compliance with legal norms and aligns with the organisation’s values and ideals. Investing in bias detection tools and privacy-enhancing technology: To mitigate risks associated with AI in marketing, marketers should allocate resources to develop and implement bias detection tools and privacy-enhancing technology. These tools can identify and address biases in AI algorithms, safeguard consumer privacy and extract valuable insights from consumer data. Social implications This study’s social implications emphasise the need for a comprehensive approach to address the ethical and legal challenges of AI in marketing. This includes adopting a responsible innovation framework, promoting ethical leadership, using ethical decision-making frameworks and conducting multidisciplinary research. By incorporating these approaches, marketers can navigate the complexities of AI in marketing responsibly, foster an ethical organisational culture, make informed ethical decisions and develop effective solutions. Such practices promote public trust, ensure equitable distribution of benefits and risk, and mitigate potential negative social consequences associated with AI in marketing. Originality/value To the best of the authors’ knowledge, this paper is among the first to explore potential solutions comprehensively. This paper provides a nuanced understanding of the challenges by using a multidisciplinary framework and synthesising various sources. It contributes valuable insights for academia and industry. (shrink)

Impact sourcing is the practice of employing socio-economically disadvantaged individuals at business process outsourcing centres to reduce poverty and create secure jobs. One of the pioneers of impact sourcing is Sama, a training-data company that focuses on annotating data for artificial intelligence (AI) systems and claims to support an ethical AI supply chain through its business operations. Drawing on fieldwork undertaken at three of Sama’s East African delivery centres in Kenya and Uganda and follow-up online interviews, this article interrogates Sama’s (...) claims regarding the benefits of its impact sourcing model. Our analysis reveals alarming accounts of low wages, insecure work, a tightly disciplined labour management process, gender-based exploitation and harassment and a system designed to extract value from low-paid workers to produce profits for investors. We argue that competitive market-based dynamics generate a powerful force that pushes such companies towards limiting the actual social impact of their business model in favour of ensuring higher profit margins. This force can be resisted, but only through countervailing measures such as pressure from organised workers, civil society, or regulation. These findings have broad implications related to working conditions for low-wage data annotators across the sector and cast doubt on the ethical nature of AI products that rely on this form of AI data work. (shrink)

Recent attempts to develop and apply digital ethics principles to address the challenges of the digital transformation leave organisations with an operationalisation gap. To successfully implement such guidance, they must find ways to translate high-level ethics frameworks into practical methods and tools that match their specific workflows and needs. Here, we describe the development of a standardised risk assessment tool, the Principle-at-Risk Analysis (PaRA), as a means to close this operationalisation gap for a key level of the ethics infrastructure at (...) many organisations – the work of an interdisciplinary ethics panel. The PaRA tool serves to guide and harmonise the work of the Digital Ethics Advisory Panel at the multinational science and technology company Merck KGaA in alignment with the principles outlined in the company’s Code of Digital Ethics. We examine how such a tool can be used as part of a multifaceted approach to operationalise high-level principles at an organisational level and provide general requirements for its implementation. We showcase its application in an example case dealing with the comprehensibility of consent forms in a data-sharing context at Syntropy, a collaborative technology platform for clinical research. (shrink)

Virtue ethics has many times been suggested as a promising recipe for the construction of artificial moral agents due to its emphasis on moral character and learning. However, given the complex nature of the theory, hardly any work has de facto attempted to implement the core tenets of virtue ethics in moral machines. The main goal of this paper is to demonstrate how virtue ethics can be taken all the way from theory to machine implementation. To achieve this goal, we (...) critically explore the possibilities and challenges for virtue ethics from a computational perspective. Drawing on previous conceptual and technical work, we outline a version of artificial virtue based on moral functionalism, connectionist bottom–up learning, and eudaimonic reward. We then describe how core features of the outlined theory can be interpreted in terms of functionality, which in turn informs the design of components necessary for virtuous cognition. Finally, we present a comprehensive framework for the technical development of artificial virtuous agents and discuss how they can be implemented in moral environments. (shrink)

This paper argues that the plethora of experiments with decentralized social technologies (DSTs)—clusters of which are sometimes called “the Web 3.0 ecosystem” or “the Fediverse”—have brought us to a constitutional moment. These technologies enable radical innovations in social, economic, and political institutions and practices, with the potential to support transformative approaches to political economy. They demand governance innovation. The paper develops a framework of prudent vigilance for making ethical choices in this space that help to both grasp positive opportunities for (...) transformation and avoid the potentially problematic consequences. Most of our specific examples and concerns come from the blockchain/Web3 universe, as this has received the greatest investment, attention, and adoption to date. However, we aim to offer a framework for governance decision-making in conditions of uncertainty that applies more broadly to other DSTs. Specifically, under the framework of prudent vigilance, we propose a pragmatic, democratic, and pluralist approach to navigating bold experimentation with social practices and political economy enabled by these technologies. Our overarching goal is to provide a framework open to transformative improvement and constrained by guardrails and guiding values supportive of democracy, freedom, and pluralism. We take a relatively strong position, rather than simply laying out ethical issues and potential approaches. We seek to be provocative in order to spur further work and hope this paper will serve as a first bridge between academic philosophy and the DST community, which have hardly interacted to date. (shrink)

La PAI es la “Asociación sobre inteligencia artificial en beneficio de las personas y la sociedad” (Partnership on AI to Benefit People and Society) y es una organización sin fines de lucro con sede en San Francisco, California, que reúne a organizaciones académicas, de la sociedad civil, a empresas tecnológicas y de los medios de comu- nicación para abordar cuestiones sustanciales, básicamente sobre el futuro de la IA, pero también otros importantes retos mundiales como el cambio climático, la alimentación, la (...) desigualdad, la salud y la educación. Tienen cinco programas que contribuyen al desarrollo de recursos, recomendaciones y mejores prácticas para la IA: inteligencia artificial e integridad mediática; IA, trabajo y economía; justicia, transparencia y responsabilidad en aprendizaje automático (ML); investigación y diseño inclusivos; y seguridad para la IA crítica. En este capítulo se destaca que, si bien PAI ha sido criticada por estar dominada en gran medida por grandes empresas tecnológicas, lo que podría limitar su autonomía y autocrítica, es una organización valiosa e influyente en el campo de la ética y la gobernanza de la IA, y desempeña un papel importante en la promoción del desarrollo y el despliegue responsables, no solo de la IA, sino de la tecnología y los medios de comunicación en general. (shrink)

La repetición y la superposición innecesaria de principios éticos similares para el desarrollo de una inteligencia artificial responsable no solo entran en conflicto, sino que esta confusión y ambigüedad pueden llegar, incluso, a resultar peligrosas si los postulados son un mero “lavado de cara” y las verdaderas intenciones se esconden detrás de intereses mezquinos. Esto aplica tanto a particulares, a empresas, como a gobiernos. El proceso de establecer leyes, normas, estándares y mejores prácticas para asegurar que la IA sea benéfica (...) para toda la sociedad es un llamado urgente para un “mejor futuro de la humanidad”. De todo lo anterior, surge este intento por traducir en solo seis principios fundamentales el cúmulo de literatura que hasta ahora se tiene y, posteriormente, defender tres máximas que podrían sintetizar no solo los principios esbozados en este libro, sino los marcos normativos vigentes con aspiraciones universalistas: responsabilidad, transparencia, seguridad, no discriminación, privacidad y sostenibilidad; y como máximas clásicas de la ética: honradez, intencionalidad y conciencia moral. (shrink)

En este capítulo se analizan las propuestas éticas para el desarrollo digital y empresarial de cuatro grandes corporativos internacionales: Microsoft, Google (Alphabet), Facebook (Meta) y Apple. Se ponderan cada uno de sus compromisos publicados en sus plataformas respectivas o las políticas compartidas por sus direcciones ejecutivas. Si bien cada una de las megaempresas, al menos en el papel, presume una serie de valores incuestionables por su integridad, también es cierto que la mayoría ha tenido que enfrentar crisis por la carencia (...) precisamente de algunos de los principios por ellos mismos proclamados. También es cierto que no puede hacerse generalizaciones precipitadas, por eso aquí se comparte primero, objetivamente, lo que proponen y, siendo autorregulatorias, se discute hasta qué punto han cumplido sus propias aspiraciones éticas. (shrink)

The recent proliferation of AI scandals led private and public organisations to implement new ethics guidelines, introduce AI ethics boards, and list ethical principles. Nevertheless, some of these efforts remained a façade not backed by any substantive action. Such behaviour made the public question the legitimacy of the AI industry and prompted scholars to accuse the sector of ethicswashing, machinewashing, and ethics trivialisation—criticisms that spilt over to institutional AI ethics boards. To counter this widespread issue, contributions in the literature have (...) proposed fixes that do not consider its systemic character and are based on a top-down, expert-centric governance. To fill this gap, we propose to make use of qualified informed lotteries: a two-step model that transposes the documented benefits of the ancient practice of sortition into the selection of AI ethics boards’ members and combines them with the advantages of a stakeholder-driven, participative, and deliberative bottom-up process typical of Citizens’ Assemblies. The model permits increasing the public’s legitimacy and participation in the decision-making process and its deliverables, curbing the industry’s over-influence and lobbying, and diminishing the instrumentalisation of ethics boards. We suggest that this sortition-based approach may provide a sound base for both public and private organisations in smart societies for constructing a decentralised, bottom-up, participative digital democracy. (shrink)

Purpose The purpose of this paper is to demonstrate the advancements in artificial intelligence (AI) and conversational agents, emphasizing their potential benefits while also highlighting the need for vigilant monitoring to prevent unethical applications. Design/methodology/approach As AI becomes more prevalent in academia and research, it is crucial to explore ways to ensure ethical usage of the technology and to identify potentially unethical usage. This manuscript uses a popular AI chatbot to write the introduction and parts of the body of a (...) manuscript discussing conversational agents, the ethical usage of chatbots and ethical concerns for academic researchers. Findings The authors reveal which sections were written entirely by the AI using a conversational agent. This serves as a cautionary tale highlighting the importance of ethical considerations for researchers and students when using AI and how educators must be prepared for the increasing prevalence of AI in the academy and industry. Measures to mitigate potential unethical use of this evolving technology are also discussed in the manuscript. Originality/value As conversational agents and chatbots increase in society, it is crucial to understand how they will impact the community and how we can live with technology instead of fighting against it. (shrink)

AI researchers have developed sophisticated language models capable of generating paragraphs of 'synthetic text' on topics specified by the user. While AI text generation has legitimate benefits, it could also be misused, potentially to grave effect. For example, AI text generators could be used to automate the production of convincing fake news, or to inundate social media platforms with machine-generated disinformation. This paper argues that AI text generators should be conceptualised as a dual-use technology, outlines some relevant lessons from earlier (...) debates on dual-use life sciences research, and calls for closer collaboration between ethicists and the machine learning community to address AI language models’ dual-use implications. (shrink)

Scholars, policymakers and organizations in the EU, especially at the level of the European Commission, have turned their attention to the ethics of (trustworthy and human-centric) Artificial Intelligence (AI). However, there has been little reflexivity on (1) the history of the ethics of AI as an institutionalized phenomenon and (2) the comparison to similar episodes of “ethification” in other fields, to highlight common (unresolved) challenges.Contrary to some mainstream narratives, which stress how the increasing attention to ethical aspects of AI is (...) due to the fast pace and increasing risks of technological developments, Science and Technology Studies(STS)-informed perspectives highlight that the rise of institutionalized assessment methods indicates a need for governments to gain more control of scientific research and to bring EU institutions closer to the public on controversies related to emerging technologies.This article analyzes how different approaches of the recent past (i.e. bioethics, technology assessment (TA) and ethical, legal and social (ELS) research, Responsible Research and Innovation (RRI)) followed one another, often “in the name of ethics”, to address previous criticisms and/or to legitimate certain scientific and technological research programs. The focus is on how a brief history of the institutionalization of these approaches can provide insights into present challenges to the ethics of AI related to methodological issues, mobilization of expertise and public participation. (shrink)

AI ethics is proposed, by the Big Tech companies which lead AI research and development, as the cure for diverse social problems posed by the commercialization of data-intensive technologies. It aims to reconcile capitalist AI production with ethics. However, AI ethics is itself now the subject of wide criticism; most notably, it is accused of being no more than “ethics washing” a cynical means of dissimulation for Big Tech, while it continues its business operations unchanged. This paper aims to critically (...) assess, and go beyond the ethics washing thesis. I argue that AI ethics is indeed ethics washing, but not only that. It has a more significant economic function for Big Tech. To make this argument I draw on the theory of intellectual monopoly capital. I argue that ethics washing is better understood as a subordinated innovation network: a dispersed network of contributors beyond Big Tech’s formal employment whose research is indirectly planned by Big Tech, which also appropriates its results. These results are not intended to render AI more ethical, but rather to advance the business processes of data-intensive capital. Because the parameters of AI ethics are indirectly set in advance by Big tech, the ostensible goal that AI ethics sets for itself—to resolve the contradiction between business and ethics—is in fact insoluble. I demonstrate this via an analysis of the latest trend in AI ethics: the operationalization of ethical principles. (shrink)

That technologies may raise ethical issues is now widely recognised. The ‘responsible innovation’ literature – as well as, to a lesser extent, the applied ethics and bioethics literature – has responded to the need for ethical reflection on technologies by developing a number of tools and approaches to facilitate such reflection. Some of these instruments consist of lists of questions that people are encouraged to ask about technologies – a methodology known as the ‘Socratic approach’. However, to date, these instruments (...) have often not adequately acknowledged various political impacts of technologies, which are, I suggest, essential to a proper account of the ethical issues they raise. New technologies can make some people richer and some people poorer, empower some and disempower others, have dramatic implications for relationships between different social groups and impact on social understandings and experiences that are central to the lives, and narratives, of denizens of technological societies. The distinctive contribution of this paper, then, is to offer a revised and updated version of the Socratic approach that highlights the political, as well as the more traditionally ethical, issues raised by the development of new technologies. (shrink)

It is frequently demanded that AI-based Decision Support Tools (AI-DSTs) ought to be both explainable to, and trusted by, those who use them. The joint pursuit of these two principles is ordinarily believed to be uncontroversial. In fact, a common view is that AI systems should be made explainable so that they can be trusted, and in turn, accepted by decision-makers. However, the moral scope of these two principles extends far beyond this particular instrumental connection. This paper argues that if (...) we were to account for the rich and diverse moral reasons that ground the call for explainable AI, and fully consider what it means to “trust” AI in a descriptively rich sense of the term, we would uncover a deep and persistent tension between the two principles. For explainable AI to usefully serve the pursuit of normatively desirable goals, decision-makers must carefully monitor and critically reflect on the content of an AI-DST’s explanation. This entails a deliberative attitude. Conversely, calls for trust in AI-DSTs imply the disposition to put questions about their reliability out of mind. This entails an unquestioning attitude. As such, the joint pursuit of explainable and trusted AI calls on decision-makers to simultaneously adopt incompatible attitudes towards their AI-DST, which leads to an intractable implementation gap. We analyze this gap and explore its broader implications: suggesting that we may need alternate theoretical conceptualizations of what explainability and trust entail, and/or alternate decision-making arrangements that separate the requirements for trust and deliberation to different parties. (shrink)

Abstract:This paper considers novel ethical issues pertaining to near-future artificial intelligence (AI) systems that seek to support, maintain, or enhance the capabilities of older adults as they age and experience cognitive decline. In particular, we focus on smart assistants (SAs) that would seek to provide proactive assistance and mediate social interactions between users and other members of their social or support networks. Such systems would potentially have significant utility for users and their caregivers if they could reduce the cognitive load (...) for tasks that help older adults maintain their autonomy and independence. However, proactively supporting even simple tasks, such as providing the user with a summary of a meeting or a conversation, would require a future SA to engage with ethical aspects of human interactions which computational systems currently have difficulty identifying, tracking, and navigating. If SAs fail to perceive ethically relevant aspects of social interactions, the resulting deficit in moral discernment would threaten important aspects of user autonomy and well-being. After describing the dynamic that generates these ethical challenges, we note how simple strategies for prompting user oversight of such systems might also undermine their utility. We conclude by considering how near-future SAs could exacerbate current worries about privacy, commodification of users, trust calibration and injustice. (shrink)

The increasing workplace use of artificially intelligent (AI) technologies has implications for the experience of meaningful human work. Meaningful work refers to the perception that one’s work has worth, significance, or a higher purpose. The development and organisational deployment of AI is accelerating, but the ways in which this will support or diminish opportunities for meaningful work and the ethical implications of these changes remain under-explored. This conceptual paper is positioned at the intersection of the meaningful work and ethical AI (...) literatures and offers a detailed assessment of the ways in which the deployment of AI can enhance or diminish employees’ experiences of meaningful work. We first outline the nature of meaningful work and draw on philosophical and business ethics accounts to establish its ethical importance. We then explore the impacts of three paths of AI deployment (replacing some tasks, ‘tending the machine’, and amplifying human skills) across five dimensions constituting a holistic account of meaningful work, and finally assess the ethical implications. In doing so we help to contextualise the meaningful work literature for the era of AI, extend the ethical AI literature into the workplace, and conclude with a range of practical implications and future research directions. (shrink)

The use of AI in weapons systems raises numerous ethical issues. To date, work on weaponized AI has tended to be theoretical and normative in nature, consisting in critical policy analyses and ethical considerations, carried out by philosophers, legal scholars, and political scientists. However, adequately addressing the cultural and social dimensions of technology requires insights and methods from empirical moral and cultural psychology. To do so, this position piece describes the motivations for and sketches the nature of a normative, cultural (...) psychology of weaponized AI. The motivations for this project include the increasingly global, cross-cultural and international, nature of technologies, and counter-intuitive nature of normative thoughts and behaviors. The nature of this project consists in developing standardized measures of AI ethical reasoning and intuitions, coupled with questions exploring the development of norms, administered and validated across different cultural groups and disciplinary contexts. The goal of this piece is not to provide a comprehensive framework for understanding the cultural facets and psychological dimensions of weaponized AI but, rather, to outline in broad terms the contours of an emerging research agenda. (shrink)

The need for fair and just AI is often related to the possibility of understanding AI itself, in other words, of turning an opaque box into a glass box, as inspectable as possible. Transparency and explainability, however, pertain to the technical domain and to philosophy of science, thus leaving the ethics and epistemology of AI largely disconnected. To remedy this, we propose an integrated approach premised on the idea that a glass-box epistemology should explicitly consider how to incorporate values and (...) other normative considerations, such as intersectoral vulnerabilities, at critical stages of the whole process from design and implementation to use and assessment. To connect ethics and epistemology of AI, we perform a double shift of focus. First, we move from trusting the output of an AI system to trusting the process that leads to the outcome. Second, we move from expert assessment to more inclusive assessment strategies, aiming to facilitate expert and non-expert assessment. Together, these two moves yield a framework usable for experts and non-experts when they inquire into relevant epistemological and ethical aspects of AI systems. We dub our framework ‘epistemology-cum-ethics’ to signal the equal importance of both aspects. We develop it from the vantage point of the designers: how to create the conditions to internalize values into the whole process of design, implementation, use, and assessment of an AI system, in which values (epistemic and non-epistemic) are explicitly considered at each stage and inspectable by every salient actor involved at any moment. (shrink)

Artificially intelligent (AI) technologies are increasingly being used in many workplaces. It is recognised that there are ethical dimensions to the ways in which organisations implement AI alongside, or substituting for, their human workforces. How will these technologically driven disruptions impact the employee–employer exchange? We provide one way to explore this question by drawing on scholarship linking Integrative Social Contracts Theory (ISCT) to the psychological contract (PC). Using ISCT, we show that the macrosocial contract’s ethical AI norms of beneficence, non-maleficence, (...) autonomy, justice, and explicability can feed into national- and organisational-level microsocial contracts. We also outline the role of employees’ technology frames in this process. We then use an illustrative example to demonstrate how this multilevel normative background helps to inform the content of individuals’ PCs in the context of working with AI technologies. (shrink)

Artificial Social Agents (ASAs), which are AI software driven entities programmed with rules and preferences to act autonomously and socially with humans, are increasingly playing roles in society. As their sophistication grows, humans will share greater amounts of personal information, thoughts, and feelings with ASAs, which has significant ethical implications. We conducted a study to investigate what ethical principles are of relative importance when people engage with ASAs and whether there is a relationship between people’s values and the ethical principles (...) they prioritise. The study uses scenarios, embedded with the five AI4People ethical principles (Beneficence, Non-maleficence, Autonomy, Justice, and Explicability), involving ASAs taking on roles traditionally played by humans to understand whether these roles and behaviours (including dialogues) are seen as acceptable or unacceptable. Results from 268 participants reveal the greatest sensitivity to ASA behaviours that relate to Autonomy, Justice, Explicability, and the privacy of their personal data. Models were created using Schwartz’s Refined Values as a possible indicator of how stakeholders discern and prioritise the different AI4People ethical principles when interacting with ASAs. Our findings raise issues around the ethical acceptability of ASAs for nudging and changing behaviour due to participants’ desire for autonomy and their concerns over deceptive ASA behaviours such as pretending to have memories and emotions. (shrink)

Artificial intelligence (AI) assurance is an umbrella term describing many approaches—such as impact assessment, audit, and certification procedures—used to provide evidence that an AI system is legal, ethical, and technically robust. AI assurance approaches largely focus on two overlapping categories of harms: deployment harms that emerge at, or after, the point of use, and individual harms that directly impact a person as an individual. Current approaches generally overlook upstream collective and societal harms associated with the development of systems, such as (...) resource extraction and processing, exploitative labour practices and energy intensive model training. Thus, the scope of current AI assurance practice is insufficient for ensuring that AI is ethical in a holistic sense, i.e. in ways that are legally permissible, socially acceptable, economically viable and environmentally sustainable. This article addresses this shortcoming by arguing for a broader approach to AI assurance that is sensitive to the full scope of AI development and deployment harms. To do so, the article maps harms related to AI and highlights three examples of harmful practices that occur upstream in the AI supply chain and impact the environment, labour, and data exploitation. It then reviews assurance mechanisms used in adjacent industries to mitigate similar harms, evaluating their strengths, weaknesses, and how effectively they are being applied to AI. Finally, it provides recommendations as to how a broader approach to AI assurance can be implemented to mitigate harms more effectively across the whole AI supply chain. (shrink)

With the increasing degrees A.I.s and machines are applied across different social contexts, the need for implementing ethics in A.I.s is pressing. In this paper, we argue for a pluralist hybrid model for the implementation of moral A.I.s. We first survey current approaches to moral A.I.s and their inherent limitations. Then we propose the pluralist hybrid approach and show how these limitations of moral A.I.s can be partly alleviated by the pluralist hybrid approach. The core ethical decision-making capacity of an (...) A.I. based on the pluralist hybrid approach consists of two systems. The first is a deterministic algorithm system that incorporates different moral rules for making explicit moral decisions. The second is a machine learning system that calculates the value of the variables required by the application of moral principles. The pluralist hybrid system is better than the existing proposals as (i) it better addresses the moral disagreement problem of the top-down approach by including different moral principles, while (ii) it reduces the opacity of ethical decision-making compared with a bottom-up system by implementing explicit moral principles for moral decision making. (shrink)

Following the publication of numerous ethical principles and guidelines, the concept of 'Trustworthy AI' has become widely used. However, several AI ethicists argue against using this concept, often backing their arguments with decades of conceptual analyses made by scholars who studied the concept of trust. In this paper, I describe the historical-philosophical roots of their objection and the premise that trust entails a human quality that technologies lack. Then, I review existing criticisms about 'Trustworthy AI' and the consequence of ignoring (...) these criticisms: if the concept of 'Trustworthy AI' is kept being used, we risk attributing responsibilities to agents who cannot be held responsible, and consequently, deteriorate social structures which regard accountability and liability. Nevertheless, despite suggestions to shift the paradigm from 'Trustworthy AI' to 'Reliable AI', I argue that, realistically, this concept will be kept being used. I end by arguing that, ultimately, AI ethics is also about power, social justice, and scholarly activism. Therefore, I propose that community-driven and social justice-oriented ethicists of AI and trust scholars further focus on (a) democratic aspects of trust formation; and (b) draw attention to critical social aspects highlighted by phenomena of distrust. This way, it will be possible to further reveal shifts in power relations, challenge unfair status quos, and suggest meaningful ways to keep the interests of citizens. (shrink)

Robots as social companions in close proximity to humans have a strong potential of becoming more and more prevalent in the coming years, especially in the realms of elder day care, child rearing, and education. As human beings, we have the fascinating ability to emotionally bond with various counterparts, not exclusively with other human beings, but also with animals, plants, and sometimes even objects. Therefore, we need to answer the fundamental ethical questions that concern human-robot-interactions per se, and we need (...) to address how we conceive of »good lives«, as more and more of the aspects of our daily lives will be interwoven with social robots. (shrink)

Since Chinese scholars are playing an increasingly important role in shaping the national landscape of discussion on AI ethics, understanding their ethical concerns and preferred solutions is essential for global cooperation on governance of AI. This article, therefore, provides the first elaborated analysis on the discourse on AI ethics in Chinese academia, via a systematic literature review. This article has three main objectives. to identify the most discussed ethical issues of AI in Chinese academia and those being left out ; (...) to analyze the solutions proposed and preferred by Chinese scholars ; and to map out whose voices are dominating and whose are in the marginal. Findings suggest that in terms of short-term implications, Chinese scholars’ concerns over AI resemble predominantly the content of international ethical guidelines. Yet in terms of long-term implications, there are some significant differences needed to be further addressed in a cultural context. Further, among a wide range of solution proposals, Chinese scholars seem to prefer strong-binding regulations to those weak ethical guidelines. In addition, this article also found that the Chinese academic discourse was dominated by male scholars and those who are from elite universities, which arguably is not a unique phenomenon in China. (shrink)

In the last few years, we have witnessed a surge in scholarly interest and scientific evidence of how algorithms can produce discriminatory outcomes, especially with regard to gender and race. However, the analysis of fairness and bias in AI, important for the debate of AI for social good, has paid insufficient attention to the category of age and older people. Ageing populations have been largely neglected during the turn to digitality and AI. In this article, the concept of AI ageism (...) is presented to make a theoretical contribution to how the understanding of inclusion and exclusion within the field of AI can be expanded to include the category of age. AI ageism can be defined as practices and ideologies operating within the field of AI, which exclude, discriminate, or neglect the interests, experiences, and needs of older population and can be manifested in five interconnected forms: (1) age biases in algorithms and datasets (technical level), (2) age stereotypes, prejudices and ideologies of actors in AI (individual level), (3) invisibility of old age in discourses on AI (discourse level), (4) discriminatory effects of use of AI technology on different age groups (group level), (5) exclusion as users of AI technology, services and products (user level). Additionally, the paper provides empirical illustrations of the way ageism operates in these five forms. (shrink)