The Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO). We define the Saliva Ontology (SALO; http://www.skb.ucla.edu/SALO/) as a consensus-based controlled vocabulary of terms and relations dedicated to the salivaomics (...) domain and to saliva-related diagnostics following the principles of the OBO (Open Biomedical Ontologies) Foundry. The Saliva Ontology is an ongoing exploratory initiative. The ontology will be used to facilitate salivaomics data retrieval and integration across multiple fields of research together with data analysis and data mining. The ontology will be tested through its ability to serve the annotation ('tagging') of a representative corpus of salivaomics research literature that is to be incorporated into the SKB. Background Saliva (oral fluid) is an emerging biofluid for non-invasive diagnostics used in the detection of human diseases. The need to advance saliva research is strongly emphasized by the National Institute of Dental and Craniofacial Research (NIDCR), and is included in the NIDCR's 2004- 2009 expert panel long-term research agenda [1]. The ability to monitor health status, disease onset, progression, recurrence and treatment outcome through noninvasive means is highly important to advancing health care management. Saliva is a perfect medium to be explored for personalized individual medicine including diagnostics, offering a non-invasive, easy to obtain means for detecting and monitoring diseases. Saliva testing potentially allows the patient to collect their own saliva samples at home, yielding convenience for the patient and savings in health costs, and facilitating multiple sampling. Specimen collection is less objectionable to patients and easier in children and elderly individuals. Due to these advantages. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Toleration and Justice in the Laozi:Engaging with Tao Jiang's Origins of Moral-Political Philosophy in Early ChinaAi Yuan (bio)IntroductionThis review article engages with Tao Jiang's ground-breaking monograph on the Origins of Moral-Political Philosophy in Early China with particular focus on the articulation of toleration and justice in the Laozi (otherwise called the Daodejing).1 Jiang discusses a naturalistic turn and the re-alignment of values in the Laozi, resulting in a (...) naturalization of justice (impartiality) by rejecting artificial humaneness and rigid hierarchical moral-political structures. In this Laozian just world, there is no room for human intervention and rigid top-down enforcement of values, thus leaving justice to naturalized Heaven or Dao as the ultimate source of the cosmos.Based on Jiang's interpretative context, I show that there is a twofold justification for the "paradoxical" and "elusive"2 value of political toleration.3 A negative expression of toleration focuses on non-interference with choices, and non-enforcement of values toward those whom one reasonably disagrees with and regards as morally wrong. Such an attitude of toleration results in practical advantages such as avoiding entrenchment in bloodshed, abandonment, and deprivation.A positive expression of toleration operating in the Laozian ideal world requires equal protection of people as capable knowers, including those to whom we object. Such protection includes their ways of expression and an unbiased recognition of them as equally capable knowers so that they are heard without bias and prejudice. Such a kind of toleration is revealed as a constituent component of justice since it enables equal contribution toward the shaping of society without discrimination. In other words, toleration is seen as naturalized justice in a Laozian world within which no one is "being wronged with the capacity as a knower"—a kind of epistemic justice articulated by Miranda Fricker (Fricker 2007).4 [End Page 466]This article discusses how Jiang's political reconstruction of Laozian philosophy contains the seeds for a discussion of toleration, a value associated with distributive justice and against epistemic injustice. First, it introduces Jiang's arguments on a naturalization of justice and impartiality as Heavenly attributes in the Laozi. Second, it articulates the value of tolerance in the Laozi. Finally, I compare Laozian toleration with Confucian toleration using youwei- wuwei metaethical criticism.Moral-Political Philosophy in the LaoziJiang's interpretative framework about the origins of distributive justice in the Laozi starts with a discussion of the cosmogonic-mystical worldviews that signal "a new understanding of the nature of the cosmos, as well as a broad reorientation in the Heaven-human relationship in the mid-Warring States period" (p. 192). Differing from the interests in the origins of human culture or civilization in the Ru and Mo traditions, Jiang agrees with Franklin Perkins and identifies a "cosmogonic turn" in the late fourth century b.c. that reveals a demotion of anthropomorphic Heaven. By paring Heaven with earth and accordingly decentering human beings in the intellectual discourse, the Laozi directs equal values to the ten thousand things, with humans as just one among them (Perkins 2016, quoted in Jiang 2021, p. 196).5 Laozi's philosophy thus stands out by rejecting Heaven as caring for human affairs while taking Dao as the ultimate origin of the cosmos, as claimed in chapter 5 of the received text: "Heaven and earth are not humane; they treat ten-thousand things in the world as straw dogs; the sage is not humane; he treats people as straw dogs" (p. 199).With Jiang's reading of a naturalistic heaven, we see a re-alignment between cosmos and the human since sage-rulers follow the Heaven-Dao. The Laozian Dao is "self-so-ing" (ziran 自然), "accommodating" (rong 容), and "impartial" (gong 公), and thus marks a sharp contrast to Confucian familial bias resulting in "actively intervening in human affairs on behalf of the (certain) humans by appointing them to carry out its mission" (p. 200). With the sage emulating the ultimate Dao, the Laozi replaces the Confucian anthropocentric Heaven, which focuses on partiality (qin 親) and humaneness (ren 仁).This Laozian understanding of the cosmos also grounds its philosophical departure from Mohism despite both having a political... (shrink)

Upon previous research, the matrilineal system seems to oppose Islamic teaching. However, the matrilineal system practiced by the Minangkabau society in West Sumatra, Indonesia has its uniqueness. Thus, this study aims to examine the correlation between the Qur’an and gender roles within the context of Minangkabau customs, specifically focusing on the matrilineal aspect. The present study employs qualitative methods for conducting library research through critical analysis. This study discovered that the matrilineal system practiced by the Minangkabau society aligns with Qur’anic (...) teachings that respect the position of women in Islam. In the matrilineal system of Minangkabau, women hold significant strength and play an influential role in cultural and traditional practices. The Minangkabau tribe’s matrilineal system encompasses three key elements: marriage, inheritance and offspring. The marriage system is exogamous. Additionally, the practice of marriage is localised, with the husband typically residing in the wife’s household. Their inheritance is divided into pusako tinggi [from ancestors from generation to generation] and pusako randah (from the efforts of married couple). Women get pusako tinggi as they serve as protectors and a symbol of their community. In comparison, men take the responsibility of overseeing its preservation to prevent any form of misuse. Pusako randah is distributed as Islamic teaching. Lineage determination is primarily based on paternal lineage, aligning with Islamic teaching. Upon comprehensive analysis, it becomes evident that there is no conflict between the matrilineal system and Islamic teachings, particularly concerning principles of gender equality. Both women and men have significant roles, as the Qur’an provides extensive explanations regarding gender equality.Contribution: This research holds significant findings in establishing a great relation between Islamic teachings based on the Qur’an and the matrilineal principles practiced by the Minangkabau tribe. (shrink)

Member States of the United Nations proposed Seventeen Sustainable Development Goals (SDGs) in 2015, emphasizing the well-being of people, planet, prosperity, peace, and partnership. Countries are expected to work diligently to achieve these goals by the year 2030. The paths chosen to achieve the SDGs depend on each country’s specific needs, challenges, and opportunities. This contribution conducts a bibliometric study of selected SDG research related to hunger and climate change among countries of the North Asia Pacific region. A review of (...) literature related to the seventeen SDGs is provided to update the current research in SDGs as a background for the regional bibliometric search. The academic published literature related to this work were collected and text mined to create a research database for trend analysis. In this research, the natural language model KeyBERT and a text mining algorithm for normalized term frequency were used to extract key terminologies for bibliometric analysis. The study provides an overview of the seventeen SDGs in the literature review and then focuses on SDG 2 (zero hunger) and SDG 13 (climate action) publication trends. These two SDGs are in close alignment with sustainable food systems and agricultural development. The conclusion indicates that greater cooperation (SDG 17) is critical to improving the quality of life within the region and in building an ethical framework that restricts external exploitation of resources that benefit newly developed economies over developing economies. (shrink)

Tae Wan Kim, John Hooker, and Thomas Donaldson make an attempt, in recent articles, to solve the alignment problem. As they define the alignment problem, it is the issue of how to give AI systems moral intelligence. They contend that one might program machines with a version of Kantian ethics cast in deontic modal logic. On their view, machines can be aligned with human values if such machines obey principles of universalization and autonomy, as well as a deontic (...) utilitarian principle. Programming machines to do so might be useful, in their view, for applications such as future autonomous vehicles. Their proposal draws both on traditional logic-based and contemporary connectionist approaches, to fuse factual information with normative principles. I will argue that this approach makes demands of machines that go beyond what is currently feasible, and may extend past the limits of the possible for AI. I also argue that a deontological ethics for machines should place greater stress on the formula of humanity of the Kantian categorical imperative. On this principle, one ought never treat a person as a mere means. Recognition of what makes a person a person requires ethical insight. Similar insight is needed to tell treatment as a means from treatment as a mere means. The resources in Kim, Hooker, and Donaldson’s approach is insufficient for this reason. Hesitation regarding deployment of autonomous machines is warranted in light of these alignment concerns. (shrink)

Foundation models such as GPT-4 are fine-tuned to avoid unsafe or otherwise problematic behavior, so that, for example, they refuse to comply with requests for help with committing crimes or with producing racist text. One approach to fine-tuning, called reinforcement learning from human feedback, learns from humans' expressed preferences over multiple outputs. Another approach is constitutional AI, in which the input from humans is a list of high-level principles. But how do we deal with potentially diverging input from humans? How (...) can we aggregate the input into consistent data about "collective" preferences or otherwise use it to make collective choices about model behavior? In this paper, we argue that the field of social choice is well positioned to address these questions, and we discuss ways forward for this agenda, drawing on discussions in a recent workshop on Social Choice for AI Ethics and Safety held in Berkeley, CA, USA in December 2023. (shrink)

When discussing AI alignment, we usually refer to the problem of teaching or training advanced autonomous AI systems to make decisions that are aligned with human values or preferences. Proponents of this approach believe it can be employed as means to stay in control over sophisticated intelligent systems, thus avoiding certain existential risks. We identify three general obstacles on the path to implementation of value alignment: a technological/technical obstacle, a normative obstacle, and a calibration problem. Presupposing, for the (...) purposes of this discussion, that the technical and normative problems are solved, we focus on the problem of how to calibrate a system, for a specific value, to be on a specific location within a spectrum stretching between righteous and normal or average human behavior. Calibration, or more specifically mis-calibration, also raises the issue of trustworthiness. If we cannot trust AI systems to perform tasks the way we intended, we would not use them on our roads and at our homes. In an era where we strive to construct autonomous machines endowed with common sense, reasoning abilities and a connection to the world, so they would be able to act in alignment with human values, such mis-calibrations can make the difference between trustworthy and untrustworthy systems. (shrink)

This paper offers three contributions to the burgeoning movements of AI for Social Good (AI4SG) and AI and the United Nations Sustainable Development Goals (SDGs). First, we introduce the SDG-Intense Evaluation framework (SDGIE) that aims to situate variegated automated/AI models in a larger ecosystem of computational approaches to advance the SDGs. To foster knowledge collaboration for solving complex social and environmental problems encompassed by the SDGs, the SDGIE framework details a benchmark structure of data-algorithm-output to effectively standardize AI approaches to (...) the SDGs. Second, as a specific instantiation of the SDGIE framework, the SDG Impact Intensity Model (SDGIIM) is theoretically and operationally established. SDGIIM embeds expert decision-making and SDG keyword banks in textual data processing to determine overall SDG “impact intensity.” Ideally, SDGIIM can be applied to textual data sets from any sector or discipline: academia, business, government, non-profit, civil society, etc. Third, the SDGIIM instantiation is applied to the specific domain of academic journal rating systems as a case study. Traditionally, academic journals have been evaluated on loosely conceived and empirically shaky notions of ‘quality.’ Aligned with the trend of AI4SG and broader calls to action, ‘impact’ is rapidly becoming the primary normative consideration for assessing academic journals. We hypothesize and demonstrate that SDGIIM is capable of producing evaluations aligned with experts’ expectations of SDG impact intensity; the consistent analysis and rating of textual data sets that embody the SDGs with varying degrees of meaning and, ultimately, promote positive impact on the actual material conditions of the world. (shrink)

Providing meaningful and actionable explanations for end-users is a situated problem requiring the intersection of multiple disciplines to address social, operational, and technical challenges. However, the explainable artificial intelligence community has not commonly adopted or created tangible design tools that allow interdisciplinary work to develop reliable AI-powered solutions. This paper proposes a formative architecture that defines the explanation space from a user-inspired perspective. The architecture comprises five intertwined components to outline explanation requirements for a task: (1) the end-users’ mental models, (...) (2) the end-users’ cognitive process, (3) the user interface, (4) the Human-Explainer Agent, and (5) the agent process. We first define each component of the architecture. Then, we present the Abstracted Explanation Space, a modeling tool that aggregates the architecture’s components to support designers in systematically aligning explanations with end-users’ work practices, needs, and goals. It guides the specifications of what needs to be explained (content: end-users’ mental model), why this explanation is necessary (context: end-users’ cognitive process), to delimit how to explain it (format: Human-Explainer Agent and user interface), and when the explanations should be given. We then exemplify the tool’s use in an ongoing case study in the aircraft maintenance domain. Finally, we discuss possible contributions of the tool, known limitations or areas for improvement, and future work to be done. (shrink)

Some researchers have recognized that privileged communities dominate the discourse on AI Ethics, and other voices need to be heard. As such, we identify the current ethics milieu as arising from WEIRD (Western, Educated, Industrialized, Rich, Democratic) contexts, and aim to expand the discussion to non-WEIRD global communities, who are also stakeholders in global sociotechnical systems. We argue that accounting for honor, along with its values and related concepts, would better approximate a global ethical perspective. This complex concept already underlies (...) some of the WEIRD discourse on AI ethics, but certain cultural forms of honor also bring overlooked issues and perspectives to light. We first describe honor according to recent empirical and philosophical scholarship. We then review “consensus” principles for AI ethics framed from an honor-based perspective, grounding comparisons and contrasts via example settings such as content moderation, job hiring, and genomics databases. A better appreciation of the marginalized concept of honor could, we hope, lead to more productive AI value alignment discussions, and to AI systems that better reflect the needs and values of users around the globe. (shrink)

How can one build AI systems such that they pursue the goals their designers want them to pursue? This is the alignment problem. Numerous authors have raised concerns that, as research advances and systems become more powerful over time, misalignment might lead to catastrophic outcomes, perhaps even to the extinction or permanent disempowerment of humanity. In this paper, I analyze the severity of this risk based on current instances of misalignment. More specifically, I argue that contemporary large language models (...) and game-playing agents are sometimes misaligned. These cases suggest that misalignment tends to have a variety of features: misalignment can be hard to detect, predict and remedy, it does not depend on a specific architecture or training paradigm, it tends to diminish a system’s usefulness and it is the default outcome of creating AI via machine learning. Subsequently, based on these features, I show that the risk of AI alignment magnifies with respect to more capable systems. Not only might more capable systems cause more harm when misaligned, aligning them should be expected to be more difficult than aligning current AI. (shrink)

The AGI alignment problem has a bimodal distribution of outcomes with most outcomes clustering around the poles of total success and existential, catastrophic failure. Consequently, attempts to solve AGI alignment should, all else equal, prefer false negatives (ignoring research programs that would have been successful) to false positives (pursuing research programs that will unexpectedly fail). Thus, we propose adopting a policy of responding to points of philosophical and practical uncertainty associated with the alignment problem by limiting and (...) choosing necessary assumptions to reduce the risk of false positives. Herein we explore in detail two relevant points of uncertainty that AGI alignment research hinges on---meta-ethical uncertainty and uncertainty about mental phenomena---and show how to reduce false positives in response to them. (shrink)

This paper looks at philosophical questions that arise in the context of AI alignment. It defends three propositions. First, normative and technical aspects of the AI alignment problem are interrelated, creating space for productive engagement between people working in both domains. Second, it is important to be clear about the goal of alignment. There are significant differences between AI that aligns with instructions, intentions, revealed preferences, ideal preferences, interests and values. A principle-based approach to AI alignment, (...) which combines these elements in a systematic way, has considerable advantages in this context. Third, the central challenge for theorists is not to identify ‘true’ moral principles for AI; rather, it is to identify fair principles for alignment that receive reflective endorsement despite widespread variation in people’s moral beliefs. The final part of the paper explores three ways in which fair principles for AI alignment could potentially be identified. (shrink)

This paper explores the potential of integrating ancient educational principles from diverse eastern cultures into modern AI ethics curricula. It draws on the rich educational traditions of ancient China, India, Arabia, Persia, Japan, Tibet, Mongolia, and Korea, highlighting their emphasis on philosophy, ethics, holistic development, and critical thinking. By examining these historical educational systems, the paper establishes a correlation with modern AI ethics principles, advocating for the inclusion of these ancient teachings in current AI development and education. The proposed integration (...) aims to provide a comprehensive education that not only encompasses foundational knowledge but also advanced learning, thereby equipping future AI professionals with the necessary tools to develop AI systems that are ethically responsible, culturally aware, and aligned with human values such as fairness, safety, transparency, and collaboration. This approach not only addresses the AI alignment problem but also fosters cultural harmony and global understanding, which are crucial in an increasingly interconnected world. The paper posits that the wisdom of ancient educational systems, when harmonized with modern AI ethics, can guide the development of AI technologies that are beneficial for humanity, ensuring these advancements are not just technologically sound but also ethically and culturally informed. (shrink)

Artificial Intelligence (AI) must be directed at humane ends. The development of AI has produced great uncertainties of ensuring AI alignment with human values (AI value alignment) through AI operations from design to use. For the purposes of addressing this problem, we adopt the phenomenological theories of material values and technological mediation to be that beginning step. In this paper, we first discuss the AI value alignment from the relevant AI studies. Second, we briefly present what are (...) material values and technological mediation and reflect on the AI value alignment through the lenses of these theories. We conclude that a set of finite human values can be defined and adapted to the stable life tasks that AI systems will be called upon to accomplish. The AI value alignment can also be fostered between designers and users through technological mediation. Upon that foundation, we propose a set of common principles to understand the AI value alignment through phenomenological theories. This paper contributes the unique knowledge of phenomenological theories to the discourse on AI alignment with human values. (shrink)

Humans and AI systems are usually portrayed as separate sys- tems that we need to align in values and goals. However, there is a great deal of AI technology found in non-autonomous systems that are used as cognitive tools by humans. Under the extended mind thesis, the functional contributions of these tools become as essential to our cognition as our brains. But AI can take cognitive extension towards totally new capabil- ities, posing new philosophical, ethical and technical chal- lenges. To (...) analyse these challenges better, we define and place AI extenders in a continuum between fully-externalized systems, loosely coupled with humans, and fully-internalized processes, with operations ultimately performed by the brain, making the tool redundant. We dissect the landscape of cog- nitive capabilities that can foreseeably be extended by AI and examine their ethical implications. We suggest that cognitive extenders using AI be treated as distinct from other cognitive enhancers by all relevant stakeholders, including developers, policy makers, and human users. (shrink)

This paper explores the important role of critical science, and in particular of post-colonial and decolonial theories, in understanding and shaping the ongoing advances in artificial intelligence. Artificial intelligence is viewed as amongst the technological advances that will reshape modern societies and their relations. While the design and deployment of systems that continually adapt holds the promise of far-reaching positive change, they simultaneously pose significant risks, especially to already vulnerable peoples. Values and power are central to this discussion. Decolonial theories (...) use historical hindsight to explain patterns of power that shape our intellectual, political, economic, and social world. By embedding a decolonial critical approach within its technical practice, AI communities can develop foresight and tactics that can better align research and technology development with established ethical principles, centring vulnerable peoples who continue to bear the brunt of negative impacts of innovation and scientific progress. We highlight problematic applications that are instances of coloniality, and using a decolonial lens, submit three tactics that can form a decolonial field of artificial intelligence: creating a critical technical practice of AI, seeking reverse tutelage and reverse pedagogies, and the renewal of affective and political communities. The years ahead will usher in a wave of new scientific breakthroughs and technologies driven by AI research, making it incumbent upon AI communities to strengthen the social contract through ethical foresight and the multiplicity of intellectual perspectives available to us, ultimately supporting future technologies that enable greater well-being, with the goal of beneficence and justice for all. (shrink)

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)

This paper considers challenges resulting from the use of advanced artificial judicial intelligence (AAJI). We argue that these challenges should be considered through the lens of value alignment. Instead of discussing why specific goals and values, such as fairness and nondiscrimination, ought to be implemented, we consider the question of how AAJI can be aligned with goals and values more generally, in order to be reliably integrated into legal and judicial systems. This value alignment framing draws on AI (...) safety and alignment literature to introduce two otherwise neglected considerations for AAJI safety: specification and assurance. We outline diverse research directions and suggest the adoption of assurance and specification mechanisms as the use of AI in the judiciary progresses. While we focus on specification and assurance to illustrate the value of the AI safety and alignment literature, we encourage researchers in law and philosophy to consider what other lessons may be drawn. (shrink)

Many researchers and intellectuals warn about extreme risks from artificial intelligence. However, these warnings typically came without systematic arguments in support. This paper provides an argument that AI will lead to the permanent disempowerment of humanity, e.g. human extinction, by 2100. It rests on four substantive premises which it motivates and defends: first, the speed of advances in AI capability, as well as the capability level current systems have already reached, suggest that it is practically possible to build AI systems (...) capable of disempowering humanity by 2100. Second, due to incentives and coordination problems, if it is possible to build such AI, it will be built. Third, since it appears to be a hard technical problem to build AI which is aligned with the goals of its designers, and many actors might build powerful AI, misaligned powerful AI will be built. Fourth, because disempowering humanity is useful for a large range of misaligned goals, such AI will try to disempower humanity. If AI is capable of disempowering humanity and tries to disempower humanity by 2100, then humanity will be disempowered by 2100. This conclusion has immense moral and prudential significance. (shrink)

As artificial intelligence (AI) continues to advance, one key challenge is ensuring that AI aligns with certain values. However, in the current diverse and democratic society, reaching a normative consensus is complex. This paper delves into the methodological aspect of how AI ethicists can effectively determine which values AI should uphold. After reviewing the most influential methodologies, we detail an intuitionist research agenda that offers guidelines for aligning AI applications with a limited set of reliable moral intuitions, each underlying a (...) refined cooperative view of AI. We discuss appropriate epistemic tools for collecting, filtering, and justifying moral intuitions with the aim of reducing cognitive and social biases. The proposed methodology facilitates a large collective participation in AI alignment, while ensuring the reliability of the considered moral judgments. (shrink)

Human beings are internally inconsistent in various ways. One way to develop this thought involves using the language of value alignment: the values we hold are not always aligned with our behavior, and are not always aligned with each other. Because of this self-misalignment, there is room for potential projects of human enhancement that involve achieving a greater degree of value alignment than we presently have. Relatedly, discussions of AI ethics sometimes focus on what is known as the (...) value alignment problem, the challenge of how to build AI that acts in accordance with our human values. We argue that there is an especially close connection between solving the value alignment problem in AI ethics and using AI to pursue certain forms of human enhancement. But in addition, we also argue that there are important limits to what kinds of human enhancement can be pursued in this way, because some forms of human enhancement—namely moral revolutions—involve a kind of value misalignment rather than alignment. (shrink)

With the increased use of Artificial Intelligence (AI) in wildlife conservation, issues around whether AI-based monitoring tools in wildlife conservation comply with standards regarding AI Ethics are on the rise. This review aims to summarise current debates and identify gaps as well as suggest future research by investigating (1) current AI Ethics and AI Ethics issues in wildlife conservation, (2) Initiatives Stakeholders in AI for wildlife conservation should consider integrating AI Ethics in wildlife conservation. We find that the existing literature (...) weakly focuses on AI Ethics and AI Ethics in wildlife conservation while at the same time ignores AI Ethics integration in AI systems for wildlife conservation. This paper formulates an ethically aligned AI system framework and discusses pre-eminent on-demand AI systems in wildlife conservation. The proposed framework uses agile software life cycle methodology to implement guidelines towards the ethical upgrade of any existing AI system or the development of any new ethically aligned AI system. The guidelines enforce, among others, the minimisation of intentional harm and bias, diversity in data collection, design compliance, auditing of all activities in the framework and ease of code inspection. This framework will inform AI developers, users, conservationists, and policymakers on what to consider when integrating AI Ethics into AI-based systems for wildlife conservation. (shrink)

The paper demonstrates that social alignment is distinct from value alignment as it is currently understood in the AI safety literature, and argues that social alignment is an important research agenda. Work provides an important example for the argument, since work is a cooperative endeavor, and it is part of the larger manifold of social cooperation. These cooperative aspects of work are individually and socially valuable, and so they must be given a central place when evaluating the (...) impact of AI upon work. Workplace technologies are not simply instruments for achieving productive goals, but ways of mediating interpersonal relations. They are aspects of a cooperative interface i.e. the infrastructure by which we engage cooperative behavior with others. The concept of the cooperative interface suggests two conjectures to foreground in the social alignment agenda, motivated by the experience of algorithmic trading and social robotics: that AI impacts cooperation through its effects on social networks, and through its effects on social norms. (shrink)

Motivated by the question of responsible AI and value alignment, I seek to offer a uniquely Foucauldian reconstruction of the problem as the emergence of an ethical subject in a disciplinary setting. This reconstruction contrasts with the strictly human-oriented programme typical to current scholarship that often views technology in instrumental terms. With this in mind, I problematise the concept of a technological subjectivity through an exploration of various aspects of ChatGPT in light of Foucault’s work, arguing that current systems (...) lack the reflexivity and self-formative characteristics inherent in the notion of the subject. By drawing upon a recent dialogue between Foucault and phenomenology, I suggest four techno-philosophical desiderata that would address the gaps in this search for a technological subjectivity: embodied self-care, embodied intentionality, imagination and reflexivity. Thus I propose that advanced AI be reconceptualised as a subject capable of “technical” self-crafting and reflexive self-conduct, opening new pathways to grasp the intertwinement of the human and the artificial. This reconceptualization holds the potential to render future AI technology more transparent and responsible in the circulation of knowledge, care and power. (shrink)

In this paper we discuss what we believe to be one of the most important features of near-future AIs, namely their capacity to behave in a friendly manner to humans. Our analysis of what it means for an AI to behave in a friendly manner does not presuppose that proper friendships between humans and AI systems could exist. That would require reciprocity, which is beyond the reach of near-future AI systems. Rather, we defend the claim that social AIs should be (...) programmed to behave in a manner that mimics a sufficient number of aspects of proper friendship. We call this “as-if friendship”. The main reason for why we believe that ‘as if friendship’ is an improvement on the current, highly submissive behavior displayed by AIs is the negative effects the latter can have on humans. We defend this view partly on virtue ethical grounds and we argue that the virtue-based approach to AI ethics outlined in this paper, which we call “virtue alignment”, is an improvement on the traditional “value alignment” approach. (shrink)

AI systems that demonstrate significant bias or lower than claimed accuracy, and resulting in individual and societal harms, continue to be reported. Such reports beg the question as to why such systems continue to be funded, developed and deployed despite the many published ethical AI principles. This paper focusses on the funding processes for AI research grants which we have identified as a gap in the current range of ethical AI solutions such as AI procurement guidelines, AI impact assessments and (...) AI audit frameworks. We highlight the responsibilities of funding bodies to ensure investment is channelled towards trustworthy and safe AI systems and provides case studies as to how other ethical funding principles are managed. We offer a first sight of two proposals for funding bodies to consider regarding procedures they can employ. The first proposal is for the inclusion of a Trustworthy AI Statement’ section in the grant application form and offers an example of the associated guidance. The second proposal outlines the wider management requirements of a funding body for the ethical review and monitoring of funded projects to ensure adherence to the proposed ethical strategies in the applicants Trustworthy AI Statement. The anticipated outcome for such proposals being employed would be to create a ‘stop and think’ section during the project planning and application procedure requiring applicants to implement the methods for the ethically aligned design of AI. In essence it asks funders to send the message “if you want the money, then build trustworthy AI!”. (shrink)

Background While the theoretical benefits and harms of Artificial Intelligence (AI) have been widely discussed in academic literature, empirical evidence remains elusive regarding the practical ethical challenges of developing AI for healthcare. Bridging the gap between theory and practice is an essential step in understanding how to ethically align AI for healthcare. Therefore, this research examines the concerns and challenges perceived by experts in developing ethical AI that addresses the healthcare context and needs. Methods We conducted semi-structured interviews with 41 (...) AI experts and analyzed the data using reflective thematic analysis. Results We developed three themes that expressed the considerations perceived by experts as essential for ensuring AI aligns with ethical practices within healthcare. The first theme explores the ethical significance of introducing AI with a clear and purposeful objective. The second theme focuses on how experts are concerned about the tension that exists between economic incentives and the importance of prioritizing the interests of doctors and patients. The third theme illustrates the need to develop context-sensitive AI for healthcare that is informed by its underlying theoretical foundations. Conclusions The three themes collectively emphasized that beyond being innovative, AI must genuinely benefit healthcare and its stakeholders, meaning AI also aligns with intricate and context-specific healthcare practices. Our findings signal that instead of narrow product-specific AI guidance, ethical AI development may need a systemic, proactive perspective that includes the ethical considerations (objectives, actors, and context) and focuses on healthcare applications. Ethically developing AI involves a complex interplay between AI, ethics, healthcare, and multiple stakeholders. (shrink)

As artificial intelligence systems are becoming increasingly autonomous and will soon be able to make decisions on their own about what to do, AI researchers have started to talk about the need to align AI with human values. The AI ‘value alignment problem’ faces two kinds of challenges—a technical and a normative one—which are interrelated. The technical challenge deals with the question of how to encode human values in artificial intelligence. The normative challenge is associated with two questions: “Which (...) values or whose values should artificial intelligence align with?” My concern is that AI developers underestimate the difficulty of answering the normative question. They hope that we can easily identify the purposes we really desire and that they can focus on the design of those objectives. But how are we to decide which objectives or values to induce in AI, given that there is a plurality of values and moral principles and that our everyday life is full of moral disagreements? In my paper I will show that although it is not realistic to reach an agreement on what we, humans, really want as people value different things and seek different ends, it may be possible to agree on what we do not want to happen, considering the possibility that intelligence, equal to our own, or even exceeding it, can be created. I will argue for pluralism which is compatible with objectivism. In spite of the fact that there is no uniquely best solution to every moral problem, it is still possible to identify which answers are wrong. And this is where we should begin the value alignment of AI. (shrink)

Practical wisdom enables moral decision-making and action by aligning one’s apprehension of proximate goods with a distal, socially embedded interpretation of a more ultimate Good. A focus on purpose within the overall process mutually informs human moral psychology and moral AI development in their examinations of practical wisdom. AI practical wisdom could ground an AI system’s apprehension of reality in a sociotechnical moral process committed to orienting AI development and action in light of a pluralistic, diverse interpretation of that Good. (...) Defining AI purpose in terms of the human struggle to determine the moral “ought” (versus the way reality is) can overcome practical and normative limitations of principle-based approaches to AI ethics without requiring idiosyncratic moral reasoning or a premature commitment to a single ethical theory, which could hinder navigating the moral implications of future unknowns. Dividing practical wisdom into proximate, mediating, and distal processes enables each division to incorporate the moral dimensions of the situation, individual AI system, and sociotechnical community, respectively. Drawing upon externalist (4E) and ecological cognitive psychology theories to extend Xavier Zubiri’s sentient intelligence, proximate apprehension of what an object affords the perceiver includes affordances for practical and moral purposes. Those apprehensions are structured by moral and other social-cognitive schemas that mediate moral constructs, the apprehended situation, and individual commitments and other motivating identifications in a process Ignacio Ellacuria calls historical reality. Distally, Josiah Royce’s community of interpretation characterizes one way of organizing a sociotechnical process to synthesize diverse interpretations of the Good, orient AI moral practice, and align AI development with human and AI moral purpose. (shrink)

As we build increasingly intelligent machines, we confront difficult questions about how to specify their objectives. One approach, which we call human-centered, tasks the machine with the objective of learning and satisfying human objectives by observing our behavior. This paper considers how human-centered AI should conceive the humans it is trying to help. We argue that an Aristotelian model of human agency has certain advantages over the currently dominant theory drawn from economics.

AI research is being challenged with ensuring that autonomous agents learn to behave ethically, namely in alignment with moral values. Here, we propose a novel way of tackling the value alignment problem as a two-step process. The first step consists on formalising moral values and value aligned behaviour based on philosophical foundations. Our formalisation is compatible with the framework of (Multi-Objective) Reinforcement Learning, to ease the handling of an agent’s individual and ethical objectives. The second step consists in (...) designing an environment wherein an agent learns to behave ethically while pursuing its individual objective. We leverage on our theoretical results to introduce an algorithm that automates our two-step approach. In the cases where value-aligned behaviour is possible, our algorithm produces a learning environment for the agent wherein it will learn a value-aligned behaviour. (shrink)

This paper motivates institutional epistemic trust as an important ethical consideration informing the responsible development and implementation of artificial intelligence (AI) technologies (or AI-inclusivity) in healthcare. Drawing on recent literature on epistemic trust and public trust in science, we start by examining the conditions under which we can have institutional epistemic trust in AI-inclusive healthcare systems and their members as providers of medical information and advice. In particular, we discuss that institutional epistemic trust in AI-inclusive healthcare depends, in part, on (...) the reliability of AI-inclusive medical practices and programs, its knowledge and understanding among different stakeholders involved, its effect on epistemic and communicative duties and burdens on medical professionals and, finally, its interaction and alignment with the public’s ethical values and interests as well as background sociopolitical conditions against which AI-inclusive healthcare systems are embedded. To assess the applicability of these conditions, we explore a recent proposal for AI-inclusivity within the Dutch Newborn Screening Program. In doing so, we illustrate the importance, scope, and potential challenges of fostering and maintaining institutional epistemic trust in a context where generating, assessing, and providing reliable and timely screening results for genetic risk is of high priority. Finally, to motivate the general relevance of our discussion and case study, we end with suggestions for strategies, interventions, and measures for AI-inclusivity in healthcare more widely. (shrink)

The “Value Alignment Problem” is the challenge of how to align the values of artificial intelligence with human values, whatever they may be, such that AI does not pose a risk to the existence of humans. A fundamental feature of how the problem is currently understood is that AI systems do not take the same things to be relevant as humans, whether turning humans into paperclips in order to “make more paperclips” or eradicating the human race to “solve climate (...) change”. Specifically, existing approaches approaches to alignment appear to be concerned with how AI might *solve* problems in the relevant way. This paper presents and explores an approach to alignment rooted in the Enactive Theory of mind. It offers an alternative conception of the alignment as “how do we make relevant to AI what is relevant to humans?” In this conception, alignment is concerned with building AI so that it *discerns* and defines the problem in the relevant way. In this way, the Alignment Problem is shown to be the same problem as the Frame Problem. The paper concludes with a consideration of tradeoffs between these conceptions of the alignment problem. (shrink)

The “Value Alignment Problem” is the challenge of how to align the values of artificial intelligence with human values, whatever they may be, such that AI does not pose a risk to the existence of humans. Existing approaches appear to conceive of the problem as "how do we ensure that AI solves the problem in the right way", in order to avoid the possibility of AI turning humans into paperclips in order to “make more paperclips” or eradicating the human (...) race to “solve climate change”. This paper proposes an approach to Alignment rooted in the Enactive theory of mind that reconceptualises it as "how do we make relevant to AI what it relevant to humans". This conceptualisation is supported with a discussion of 4E cognition and goes on to suggest that the Alignment problem and the Frame problem are the same problem. The paper concludes with a discussion of the tradeoffs of the different approaches to value alignment. (shrink)

Intelligibility and interpretability related to artificial intelligence (AI) are crucial for enabling explicability, which is vital for establishing constructive communication and agreement among various stakeholders, including users and designers of AI. It is essential to overcome the challenges of sharing an understanding of the details of the various structures of diverse AI systems, to facilitate effective communication and collaboration. In this paper, we propose four fundamental terms: “I/O,” “Constraints,” “Objectives,” and “Architecture.” These terms help mitigate the challenges associated with intelligibility (...) and interpretability in AI by providing appropriate levels of abstraction to describe structure of AI systems generally, thereby facilitating the sharing of understanding among various stakeholders. The relationship between the Objective of AI designers and the Purpose of AI users is linked to the issues of AI alignment. (shrink)

According to a growing number of researchers, AI may pose catastrophic – or even existential – risks to humanity. Catastrophic risks may be taken to be risks of 100 million human deaths, or a similarly bad outcome. I argue that such risks – while contested – are sufficiently likely to demand rigorous discussion of potential societal responses. Subsequently, I propose four desiderata for approaches to the reduction of catastrophic risks from AI. The quality of such approaches can be assessed by (...) their chance of success, degree of beneficence, degree of non-maleficence, and beneficent side effects. Then, I employ these desiderata to evaluate the promises, limitations and risks of alignment research, timelines research, policy research, halting or slowing down AI research, and compute governance for tackling catastrophic AI risks. While more research is needed, this investigation shows that several approaches for dealing with catastrophic AI risks are available, and where their respective strengths and weaknesses lie. It turns out that many approaches are complementary and that the approaches have a nuanced relationship to approaches to present AI harms. While some approaches are similarly useful for addressing catastrophic risks and present harms, this is not always the case. (shrink)

The creation of machines with intelligence comparable to human beings—so-called "human-level” and “general” intelligence—is often regarded as the Holy Grail of Artificial Intelligence (AI) research. However, many prominent discussions of AI lean heavily on the notion of human-level intelligence to frame AI research, but then rely on conceptions of human cognitive capacities, including “common sense,” that are sketchy, one-sided, philosophically loaded, and highly contestable. Our goal in this essay is to bring into view some underappreciated features of the practical intelligence (...) involved in ordinary human agency. These features of practical intelligence are implicit in the structure of our first-person experience of embodied and situated agency, deliberation, and human interaction. We argue that spelling out these features and their implications reveals a fundamental distinction between two forms of intelligence in action, or what we call “efficient task-completion” versus “intelligent engagement in activity.” This distinction helps us to see what is missing from some widely accepted ways of thinking about human-level intelligence in AI, and how human common sense is actually tied, conceptually, to the ideal of practical wisdom, or good (normative) judgment about how to act and live well. Finally, our analysis, if sound, also has implications for the important ethical question of what it means to have AI systems that are aligned with human values, or the so-called “value alignment” problem for artificial intelligence. (shrink)

Critics currently argue that applied ethics approaches to artificial intelligence (AI) are too principles-oriented and entail a theory–practice gap. Several applied ethical approaches try to prevent such a gap by conceptually translating ethical theory into practice. In this article, we explore how the currently most prominent approaches of AI ethics translate ethics into practice. Therefore, we examine three approaches to applied AI ethics: the embedded ethics approach, the ethically aligned approach, and the Value Sensitive Design (VSD) approach. We analyze each (...) of these three approaches by asking how they understand and conceptualize theory and practice. We outline the conceptual strengths as well as their shortcomings: an embedded ethics approach is context-oriented but risks being biased by it; ethically aligned approaches are principles-oriented but lack justification theories to deal with trade-offs between competing principles; and the interdisciplinary Value Sensitive Design approach is based on stakeholder values but needs linkage to political, legal, or social governance aspects. Against this background, we develop a meta-framework for applied AI ethics conceptions with three dimensions. Based on critical theory, we suggest these dimensions as starting points to critically reflect on the conceptualization of theory and practice. We claim, first, that the inclusion of the dimension of affects and emotions in the ethical decision-making process stimulates reflections on vulnerabilities, experiences of disregard, and marginalization already within the AI development process. Second, we derive from our analysis that considering the dimension of justifying normative background theories provides both standards and criteria as well as guidance for prioritizing or evaluating competing principles in cases of conflict. Third, we argue that reflecting the governance dimension in ethical decision-making is an important factor to reveal power structures as well as to realize ethical AI and its application because this dimension seeks to combine social, legal, technical, and political concerns. This meta-framework can thus serve as a reflective tool for understanding, mapping, and assessing the theory–practice conceptualizations within AI ethics approaches to address and overcome their blind spots. (shrink)

This article advocates for a hermeneutic model for children-AI interactions in which the desirable purpose of children’s interaction with artificial intelligence systems is children's growth. The article perceives AI systems with machine-learning components as having a recursive element when interacting with children. They can learn from an encounter with children and incorporate data from interaction, not only from prior programming. Given the purpose of growth and this recursive element of AI, the article argues for distinguishing the interpretation of bias within (...) the artificial intelligence ethics and responsible AI discourse. Interpreting bias as a preference and distinguishing between positive and negative bias is needed as this would serve children's healthy psychological and moral development. The human-centric AI discourse advocates for an alignment of capacities of humans and capabilities of machines by a focus both on the purpose of humans and on the purpose of machines for humans. The emphasis on mitigating negative biases through data protection, AI law, and certain value-sensitive design frameworks demonstrates that the purpose of the machine for humans is prioritized over the purpose of humans. These top–down frameworks often narrow down the purpose of machines to do-no-harm and they miss accounting for the bottom-up views and developmental needs of children. Therefore, applying a growth model for children-AI interactions that incorporates learning from negative AI-mediated biases and amplifying positive ones would positively benefit children’s development and children-centric AI innovation. Consequently, the article explores: What challenges arise from mitigating negative biases and amplifying positive biases in children-AI interactions and how can a growth model address these? To answer this, the article recommends applying a growth model in open AI co-creational spaces with and for children. In such spaces human–machine and human–human value alignment methods can be collectively applied in such a manner that children can become sensitized toward the effects of AI-mediated negative biases on themselves and others; enable children to appropriate and imbue top-down values of diversity, and non-discrimination with their meanings; enforce children’s right to identity and non-discrimination; guide children in developing an inclusive mindset; inform top-down normative AI frameworks by children’s bottom-up views; contribute to design criteria for children-centric AI. Applying such methods under a growth model in AI co-creational spaces with children could yield an inclusive co-evolution between responsible young humans in the loop and children-centric AI systems. (shrink)

A series of recent developments points towards auditing as a promising mechanism to bridge the gap between principles and practice in AI ethics. Building on ongoing discussions concerning ethics-based auditing, we offer three contributions. First, we argue that ethics-based auditing can improve the quality of decision making, increase user satisfaction, unlock growth potential, enable law-making, and relieve human suffering. Second, we highlight current best practices to support the design and implementation of ethics-based auditing: To be feasible and effective, ethics-based auditing (...) should take the form of a continuous and constructive process, approach ethical alignment from a system perspective, and be aligned with public policies and incentives for ethically desirable behaviour. Third, we identify and discuss the constraints associated with ethics-based auditing. Only by understanding and accounting for these constraints can ethics-based auditing facilitate ethical alignment of AI, while enabling society to reap the full economic and social benefits of automation. (shrink)

This paper is addressed to recent theoretical discussions of the Anthropocene, in particular Bernard Stiegler’s Neganthropocene (Open Universities Press, 2018), which argues: “As we drift past tipping points that put future biota at risk, while a post-truth regime institutes the denial of ‘climate change’ (as fake news), and as Silicon Valley assistants snatch decision and memory, and as gene-editing and a financially-engineered bifurcation advances over the rising hum of extinction events and the innumerable toxins and conceptual opiates that Anthropocene Talk (...) fascinated itself with—in short, as ‘the Anthropocene’ discloses itself as a dead-end trap…”. The objective of this paper is therefore twofold: (1) to discuss how the Anthropocene is appropriated to certain ideological discourses (paradoxically) to maintain the hegemony of precisely those systems of production that have most accelerated climate change etc.; (2) to consider how the factography of the Anthropocene is exploited in this process to mask the ideological character of industry-aligned “technocratic” environmental management. The paper is not concerned with specific case studies in terms of government and industry policy, or climate science, but rather with the ways in which the discourse of the Anthropocene has been inflected within the humanities and the broader cultural field—that is to say, ideologically, as a system or logic of meaning. How the Anthropocene “means” is, in this respect, a question of some importance. This paper does not attempt to address all the facets of this question, but focuses upon a central “apocalyptic” strain in the discourse of the Anthropocene drawn particularly from Francis Fukuyama’s millennial posthumanism and centred in the question of “sustainability” as catastrophe management—with the risk that real environmental degradation will become an alibi for a revived neoliberalism. In other words, that the critical Earth system transformations that characterise the Anthropocene are themselves commodities, and that the project of their amelioration is in process of defining a future (opportunistic) “crisis” rhetoric with a global political franchise. The ideological import of the Anthropocene stems precisely from the fact that it is planetary and, while catalysed by human agency, independent in its specific behaviour from it. The Anthropocene objectively presents as the contemporary counterpart of the Cold War doctrine of Mutually Assured Destruction and the most compelling argument for a new kind of technological “arms race.” But it also presents as the condition of an emerging ideological discourse which will determine how this race is run. From the discourse on “energy security” to the widespread “security crackdown” on environmental activists across the so-called developed & developing world, the Anthropocene has come to represent the co-option of a scientific factography for the thinly disguised resurgence of “ideological science” of the Fukuyamaesque variety (post-history, post-human). For Fukuyama, the true meaning of “posthuman” is thus the accomplishment of humanity’s historical mission. As the “End of History” designates an end of ideological struggle, so too the dénouement of the Anthropocene and the “ends of man” represent the accomplished purpose of species warfare: dominion, not simply over the world, but over all possible worlds. According to this narrative, science—like technology—must be uniquely at the service of the maintenance of the global order, organised around a universal appeal to “crisis management.” It is precisely for this reason that what calls itself post-human masks the return of an ever-more-apocalyptic Humanism. (shrink)

A series of recent developments points towards auditing as a promising mechanism to bridge the gap between principles and practice in AI ethics. Building on ongoing discussions concerning ethics-based auditing, we offer three contributions. First, we argue that ethics-based auditing can improve the quality of decision making, increase user satisfaction, unlock growth potential, enable law-making, and relieve human suffering. Second, we highlight current best practices to support the design and implementation of ethics-based auditing: To be feasible and effective, ethics-based auditing (...) should take the form of a continuous and constructive process, approach ethical alignment from a system perspective, and be aligned with public policies and incentives for ethically desirable behaviour. Third, we identify and discuss the constraints associated with ethics-based auditing. Only by understanding and accounting for these constraints can ethics-based auditing facilitate ethical alignment of AI, while enabling society to reap the full economic and social benefits of automation. (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)