The Chinese government and technology companies assume a proactive stance towards digital technologies and AI and their roles in users’—and more generally, people’s—lives. This vision of ‘Tech for Good’, i.e., the development of good digital technologies and AI or the application of them for good, is also shared by major technology companies in the globe, e.g., Google, Microsoft, and Facebook. Interestingly, these initiatives have invited a number of critiques for their feasibility and desirability, particularly in relation to (...) the social and political conditions of liberal democratic societies. In this article, I discuss whether these critiques also apply to the Chinese context and contend that Confucian philosophy provides the normative resources to answer these critiques. This cross-cultural analysis, therefore, allows us to formulate a different account of AI4SG, which I shall call ‘AI for Datong’, and helps us in our reimagining of the normative vision for AI. (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)

The idea of artificial intelligence for social good is gaining traction within information societies in general and the AI community in particular. It has the potential to tackle social problems through the development of AI-based solutions. Yet, to date, there is only limited understanding of what makes AI socially good in theory, what counts as AI4SG in practice, and how to reproduce its initial successes in terms of policies. This article addresses this gap by identifying seven (...) ethical factors that are essential for future AI4SG initiatives. The analysis is supported by 27 case examples of AI4SG projects. Some of these factors are almost entirely novel to AI, while the significance of other factors is heightened by the use of AI. From each of these factors, corresponding best practices are formulated which, subject to context and balance, may serve as preliminary guidelines to ensure that well-designed AI is more likely to serve the social good. (shrink)

Artificial Intelligence (AI) has become a driving force in modern research, industry and public administration and the European Union (EU) is embracing this technology with a view to creating societal, as well as economic, value. This effort has been shared by EU Member States which were all encouraged to develop their own national AI strategies outlining policies and investment levels. This study focuses on how EU Member States are approaching the promise to develop and use AI for the good (...) of society through the lens of their national AI strategies. In particular, we aim to investigate how European countries are investing in AI and to what extent the stated plans contribute to the good of people and society as a whole. Our contribution consists of three parts: (i) a conceptualization of AI for social good highlighting the role of AI policy, in particular, the one put forward by the European Commission (EC); (ii) a qualitative analysis of 15 European national strategies mapping investment plans and suggesting their relation to the social good (iii) a reflection on the current status of investments in socially good AI and possible steps to move forward. Our study suggests that while European national strategies incorporate money allocations in the sphere of AI for social good (e.g. education), there is a broader variety of underestimated actions (e.g. multidisciplinary approach in STEM curricula and dialogue among stakeholders) that can boost the European commitment to sustainable and responsible AI innovation. (shrink)

This introduction sets out the aims and scope of the Special Issue and provides an overview of each of the research articles and commentaries that follow.

Initiatives relying on artificial intelligence (AI) to deliver socially beneficial outcomes—AI for social good (AI4SG)—are on the rise. However, existing attempts to understand and foster AI4SG initiatives have so far been limited by the lack of normative analyses and a shortage of empirical evidence. In this Perspective, we address these limitations by providing a definition of AI4SG and by advocating the use of the United Nations’ Sustainable Development Goals (SDGs) as a benchmark for tracing the scope and spread (...) of AI4SG. We introduce a database of AI4SG projects gathered using this benchmark, and discuss several key insights, including the extent to which different SDGs are being addressed. This analysis makes possible the identification of pressing problems that, if left unaddressed, risk hampering the effectiveness of AI4SG initiatives. (shrink)

Traditional medical practices and relationships are changing given the widespread adoption of AI-driven technologies across the various domains of health and healthcare. In many cases, these new technologies are not specific to the field of healthcare. Still, they are existent, ubiquitous, and commercially available systems upskilled to integrate these novel care practices. Given the widespread adoption, coupled with the dramatic changes in practices, new ethical and social issues emerge due to how these systems nudge users into making decisions and (...) changing behaviours. This article discusses how these AI-driven systems pose particular ethical challenges with regards to nudging. To confront these issues, the value sensitive design approach is adopted as a principled methodology that designers can adopt to design these systems to avoid harming and contribute to the social good. The AI for Social Good factors are adopted as the norms constraining maleficence. In contrast, higher-order values specific to AI, such as those from the EU High-Level Expert Group on AI and the United Nations Sustainable Development Goals, are adopted as the values to be promoted as much as possible in design. The use case of Amazon Alexa's Healthcare Skills is used to illustrate this design approach. It provides an exemplar of how designers and engineers can begin to orientate their design programs of these technologies towards the social good. (shrink)

Value Sensitive Design (VSD) is an established method for integrating values into technical design. It has been applied to different technologies and, more recently, to artificial intelligence (AI). We argue that AI poses a number of challenges specific to VSD that require a somewhat modified VSD approach. Machine learning (ML), in particular, poses two challenges. First, humans may not understand how an AI system learns certain things. This requires paying attention to values such as transparency, explicability, and accountability. Second, ML (...) may lead to AI systems adapting in ways that ‘disembody’ the values embedded in them. To address this, we propose a threefold modified VSD approach: 1) integrating a known set of VSD principles (AI4SG) as design norms from which more specific design requirements can be derived; 2) distinguishing between values that are promoted and respected by the design to ensure outcomes that not only do no harm but also contribute to good; and 3) extending the VSD process to encompass the whole life cycle of an AI technology in order to monitor unintended value consequences and redesign as needed. We illustrate our VSD for AI approach with an example use case of a SARS-CoV-2 contact tracing app. (shrink)

To become more broadly applicable, positions on AI ethics require perspectives from non-Western regions and cultures such as China and Japan. In this paper, we propose that the addition of the concept of harmony to the discussion on ethical AI would be highly beneficial due to its centrality in East Asian cultures and its applicability to the challenge of designing AI for social good. We first present a synopsis of different definitions of harmony in multiple contexts, such as (...) music and society, which reveals that the concept is, at its core, about well-balanced relationships and appropriate actions which give rise to order, balance, and aesthetically pleasing phenomena. The mediator for these well-balanced relationships isTaktwhich is an ability to act thoughtfully and sensibly according to the specific situation and to put things into proportion and order. We propose that the central challenge of building harmonizing AI is to make intelligent systems tactful and also to design and use them tactfully. For an AI system to become tactful, it needs to be able to have an advanced sensitivity to the specific contexts which it is in and their social and ethical implications and have the capability of approximately inferring the emotional and cognitive states of people with whom it is interacting. (shrink)

AI for social good is a thriving research topic and a frequently declared goal of AI strategies and regulation. This article investigates the requirements necessary in order for AI to actually serve a public interest, and hence be socially good. The authors propose shifting the focus of the discourse towards democratic governance processes when developing and deploying AI systems. The article draws from the rich history of public interest theory in political philosophy and law, and develops a (...) framework for ‘public interest AI’. The framework consists of (1) public justification for the AI system, (2) an emphasis on equality, (3) deliberation/ co-design process, (4) technical safeguards, and (5) openness to validation. This framework is then applied to two case studies, namely SyRI, the Dutch welfare fraud detection project, and UNICEF’s Project Connect, that maps schools worldwide. Through the analysis of these cases, the authors conclude that public interest is a helpful and practical guide for the development and governance of AI for the people. (shrink)

AI is increasingly becoming based upon Internet-dependent systems to handle the massive amounts of data it requires to function effectively regardless of the availability of stable Internet connectivity in every affected community. As such, sustainable development (SD) for rural and mountain communities will require more than just equitable access to broadband Internet connection. It must also include a thorough means whereby to ensure that affected communities gain the education and tools necessary to engage inclusively with new technological advances, whether they (...) be focused on machine learning algorithms or community infrastructure, as they will be increasingly dependent upon the automational capabilities of AI. In this essay, an exploration will be conducted into the means whereby student-engaged learning (SEL) can effectively be utilized to provide targeted, inclusive education to rural and mountain communities regarding the implications of AI for SD. (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)

We discuss the effectiveness of mediated communication (internet communication via a computer tablet) and tacit engagement in a Project on mental health. The project is aimed at improving the wellbeing of adult women living with chronic mental disorders in long-term psychiatric internment. The computer tablets act as "portals" to provide access and conatct with the outside world for patients who have poor (if any) external social support. This support includes a patient-centred psycho-social care, and accompanying clinical and pharmaceutical (...) treatment. Both patients and their relatives accepted the benefits of internet mediation, for very different reasons. For the patient, this is a flash of contact with humanity, and for the relatives the internet communication this proved to be an alternative to the need for physical proximity. As some patients had no relatives or friends to communicate (even remotely) with the outside world, and because there is a school next door to the clinic, we visualized that the communication between these two communities could provide both a therapeutic and poetic act of learning and compassion. The electronic portal could serve as a virtual bridge between two forbidden domains. Although awareness of students of the nearby school was raised about mental health, the use of internet mediation devoid of physical proximity made the students suspicious of the goal of the mediation. From the patient's side, however, each contact was an instance of joy. Several issues were raised from this exercise. (shrink)

The pervasiveness of AI-empowered technologies across multiple sectors has led to drastic changes concerning traditional social practices and how we relate to one another. Moreover, market-driven Big Tech corporations are now entering public domains, and concerns have been raised that they may even influence public agenda and research. Therefore, this chapter focuses on assessing and evaluating what kind of business model is desirable to incentivise the AI for Social Good (AI4SG) factors. In particular, the chapter explores the (...) implications of this discourse for SDG #17 (global partnership) and how this goal may encourage Big Tech corporations to strengthen multi-stakeholder partnerships that promote effective public-private and civil society partnerships and the meaningful co-presence of non-market and market values. In doing so, the chapter proposes an analysis of the sociological notion of "social license to operate" (SLO) elaborated in the mining and extractive industry literature and introduces it into the discourse on sustainable digital business models and responsible management of risks in the digital age. This serves to explore how such a social license can be adopted as a practice by digital business models to foster trust, collaboration and coordination among different actors - AI researchers and initiatives, institutions and civil society at large - for the support of SDGs interrelated targets and goals. (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)

The justification and rationality of this paper is to present some fundamental principles, theories, and concepts that we believe moulds the nucleus of a good artificial intelligence (AI) society. The morally accepted significance and utilitarian concerns that stems from the inception and realisation of an AI’s structural foundation are displayed in this study. This paper scrutinises the structural foundation, fundamentals, and cardinal righteous remonstrations, as well as the gaps in mechanisms towards novel prospects and perils in determining resilient fundamentals, (...) accountability, and AI’s convoluted and responsible implications. We outline a number of salient and practical benefits, in which to place moral norms within the mise en scène of AI, to delineate the rudimentary ethical dilemmas and decorous directions within the realms of AI. (shrink)

This paper delves into the significance and challenges of Artificial Intelligence (AI) ethics and justice in terms of Common Good and Well-being, fairness and non-discrimination, rational public deliberation, and autonomy and control. Initially, the paper establishes the groundwork for subsequent discussions using the Academia Sinica LLM incident and the AI Technology R&D Guidelines of the National Science and Technology Council(NSTC) as a starting point. In terms of justice and ethics in AI, this research investigates whether AI can fulfill human (...) common interests and welfare. Taking AI injustice as an example, I analyze the practical assessment of AI regarding regional, industrial, and social impacts. Further, this paper discusses the challenges of fairness and non-discrimination in AI, specifically addressing the issue of training on biased data, discussing the acquisition of bias by AI and post-processing supervision issues, and emphasizing the importance of rational public deliberation in this process. Then, this research examines the challenges and countermeasures the rational public faces in public deliberation, such as the importance of education in STEM scientific literacy and technological capabilities. Finally, in discussing AI and autonomy, I propose a 'Human-Centered Approach’ rather than relying solely on the 'Technological Utility Maximization' brought by AI to achieve substantial AI justice. -/- Keywords: AI Ethics and Justice, Fairness and Non-Discrimination, Biased Data Training, Public Deliberation, Autonomy, Human-Centered Approach . (shrink)

This paper presents social data and knowledge collective intelligence platform for TRaining Ethical AI Models (STREAM) to address the challenge of aligning AI models with human moral values, and to provide ethics datasets and knowledge bases to help promote AI models “follow good advice as naturally as a stream follows its course”. By creating a comprehensive and representative platform that accurately mirrors the moral judgments of diverse groups including humans and AIs, we hope to effectively portray cultural and (...) group variations, and capture the dynamic evolution of moral judgments over time, which in turn will facilitate the Establishment, Evaluation, Embedding, Embodiment, Ensemble, and Evolvement (6Es) of the moral capabilities of AI models. Currently, STREAM has already furnished a comprehensive collection of ethical scenarios, and amassed substantial moral judgment data annotated by volunteers and various popular Large Language Models, collectively portraying the moral preferences and performances of both humans and AIs across a range of moral contexts. This paper will outline the current structure and construction of STREAM, explore its potential applications, and discuss its future prospects. (shrink)

In this paper, I argue that AI literacy should be added to the list of primary goods developed by political philosopher John Rawls. Primary goods are the necessary resources all citizens need to exercise their two moral powers, namely their sense of justice and their sense of the good. These goods are advantageous for citizens since without them citizens will not be able to fully develop their moral powers. I claim the lack of AI literacy impacts citizens’ ability to (...) exercise their sense of justice and their sense of the good. Without citizens having the ability to understand how AI technology works – including being aware of the social and political implications and the limits and possibilities of this technology broadly speaking – this could impact their ability to participate in a free, equal and fair society and their ability to carry out their conception of the good. Thus this paper is a call for AI literacy to be regarded as a basic good in a liberal constitutional democracy in order for citizens to be able to exercise their freedom and equality. (shrink)

The philosophical, legal, and HCI literature concerning artificial intelligence (AI) has explored the ethical implications and values that these systems will impact on. One aspect that has been only partially explored, however, is the role of deception. Due to the negative connotation of this term, research in AI and Human–Computer Interaction (HCI) has mainly considered deception to describe exceptional situations in which the technology either does not work or is used for malicious purposes. Recent theoretical and historical work, however, has (...) shown that deception is a more structural component of AI than it is usually acknowledged. AI systems that enter in communication with users, in fact, forcefully invite reactions such as attributions of gender, personality and empathy, even in the absence of malicious intent and often also with potentially positive or functional impacts on the interaction. This paper aims to operationalise the Human-Centred AI (HCAI) framework to develop the implications of this body of work for practical approaches to AI ethics in HCI and design. In order to achieve this goal, we take up the analytical distinction between “banal” and “strong” deception, originally proposed in theoretical and historical scholarship on AI (Natale in Deceitful media: artificial intelligence and social life after the turing test, Oxford University Press, New York, 2021), as a starting point to develop ethical reflections that will empower designers and developers with practical ways to solve the problems raised by the complex relationship between deception and communicative AI. The paper considers how HCAI can be applied to conversational AI (CAI) systems in order to design them to develop banal deception for social good and, at the same time, to avoid its potential risks. (shrink)

Big Data and Artificial Intelligence have a symbiotic relationship. Artificial Intelligence needs to be trained on Big Data to be accurate, and Big Data's value is largely realized through its use by Artificial Intelligence. As a result, Big Data and Artificial Intelligence practices are tightly intertwined in real life settings, as are their impacts on society. Unethical uses of Artificial Intelligence are therefore a Big Data problem, at least to some degree. Efforts to address this problem have been dominated by (...) the documentation of Ethical Artificial Intelligence principles and the creation of technical tools that address specific aspects of those principles. However, there is mounting evidence that Ethical Artificial Intelligence principles and technical tools have little impact on the Artificial Intelligence that is created in practice, sometimes in very public ways. The goal of this commentary is to highlight four interconnected areas society can invest in to close this Ethical Artificial Intelligence publication-to-practice gap, maximizing the positive impact Artificial Intelligence and Big Data have on society. For Ethical Artificial Intelligence to become a reality, I argue that these areas need to be addressed holistically in a way that acknowledges their interdependencies. Progress will require iteration, compromise, and transdisciplinary collaboration, but the result of our investments will be the realization of Artificial Intelligence's and Big Data's tremendous potential for social good, in practice rather than in just our hopes and aspirations. (shrink)

Synthetic content, which has been produced by generative artificial intelligence, is beginning to spread through the public sphere. Increasingly, we find ourselves exposed to convincing ‘deepfakes’ and powerful chatbots in our online environments. How should we mitigate the emerging risks to individuals and society? This article argues that labelling synthetic content in public forums is an essential first step. While calls for labelling have already been growing in volume, no principled argument has yet been offered to justify this measure (which (...) inevitably comes with some additional costs). Rectifying that deficit, I conduct a close examination of our epistemic and expressive interests in identifying synthetic content as such. In so doing, I develop a cumulative case for social media platforms to enforce a labelling duty. I argue that this represents an important element of good platform governance, helping to shore up the integrity of our contemporary public discourse, which takes place increasingly online. (shrink)

Advances in artificial intelligence and robotics stand to change many aspects of our lives, including our values. If trends continue as expected, many industries will undergo automation in the near future, calling into question whether we can still value the sense of identity and security our occupations once provided us with. Likewise, the advent of social robots driven by AI, appears to be shifting the meaning of numerous, long-standing values associated with interpersonal relationships, like friendship. Furthermore, powerful actors’ and (...) institutions’ increasing reliance on AI to make decisions that may affect how people live their lives may have a significant impact on privacy while also raising issues about algorithmic transparency and human control. In this paper, building and expanding on previous works, we will look at how the deployment of Artificial Intelligence technology may lead to changes in identity, security, and other crucial values (such as friendship, fairness, and privacy). We will discuss what challenges we may face in the process, while critically reflecting on whether such changes may be desirable. Finally, drawing on a series of considerations underlying virtue ethics, we will formulate a set of preliminary suggestions, which—we hope—can be used to more carefully guide the future roll out of AI technologies for human flourishing; that is, for social and moral good. (shrink)

In this article, drawing upon a feminist epistemology, we examine the critical roles that philosophical standpoint, historical usage, gender, and language play in a knowledge arena which is increasingly opaque to the general public. Focussing on the language dimension in particular, in its historical and social dimensions, we explicate how some keywords in use across artificial intelligence (AI) discourses inform and misinform non-expert understandings of this area. The insights gained could help to imagine how AI technologies could be better (...) conceptualised, explained, and governed, so that they are leveraged for social good. (shrink)

Artificial intelligence (AI) technologies are increasingly creeping into the work sphere, thereby gradually questioning and/or disturbing the long-established moral concepts and norms communities have been using to define what makes work good. Each community, and Muslims make no exception in this regard, has to revisit their moral world to provide well-thought frameworks that can engage with the challenging ethical questions raised by the new phenomenon of AI-mediated work. For a systematic analysis of the broad topic of AI-mediated work ethics (...) from an Islamic perspective, this article focuses on presenting an accessible overview of the “moral world” of work in the Islamic tradition. Three main components of this moral world were selected due to their relevance to the AI context, namely (1) Work is inherently good for humans, (2) Practising a religiously permitted profession and (c) Maintaining good relations with involved stakeholders. Each of these three components is addressed in a distinct section, followed by a sub-section highlighting the relevance of the respective component to the particular context of AI-mediated work. The article argues that there are no unsurmountable barriers in the Islamic tradition against the adoption of AI technologies in work sphere. However, important precautions should be considered to ensure that embracing AI will not be at the cost of work-related moral values. The article also highlights how important lessons can be learnt from the positive historical experience of automata that thrived in the Islamic civilization. (shrink)

I agree with Jecker et al that “the headline-grabbing nature of existential risk (X-risk) diverts attention away from immediate artificial intelligence (AI) threats…”1 Focusing on very long-term speculative risks associated with AI is both ethically distracting and ethically dangerous, especially in a healthcare context. More specifically, AI in healthcare is generating healthcare justice challenges that are real, imminent and pervasive. These are challenges generated by AI that deserve immediate ethical attention, more than any X-risk issues in the distant future. Almost (...) 50 years ago, John Knowles edited a volume titled Doing Better and Feeling Worse: Health in the United States. We are ‘doing better’ because numerous advances in medical technologies are saving more lives and improving the quality of our lives. But we are ‘feeling worse’ because the additional costs are unsustainable, are threatening funding for other social goods and are increasing injustices regarding the allocation of those resources. This is precisely the situation we are faced with regarding AI in medicine today. Exploding healthcare costs have generated increasing pressure to control those costs, often with unjust consequences. AI is touted as a critical mechanism for controlling healthcare costs, often with little thought given to justice-relevant consequences. Consider, for example, …. (shrink)

Spoken communication between social robotic devices, powered by generative AI tools such as ChatGPT, and the senior population offers great potential for researching social interaction and robot identity perceptions as well as exploring the potential opportunities and challenges when implementing this human-machine interactions in real life situations and health care. In this paper we explore people’s perceptions of the social robot Furhat when administering verbal tasks similar to those used in screening for Alzheimer’s disease. We describe the (...) Slovak system mounted on the robot that includes a speech recognizer, the scenarios powered by generative large language model ChatGPT, and a speech synthesizer. We tested the functionality of the proposed approach with two groups of participants: attendees of a large science fair and a scientific conference. The observations from 87 collected questionnaires suggest good potential and applicability of such an approach for the given task and more positive attitudes of older subjects compared to younger ones. (shrink)

Artificial Intelligence (AI) is taking centre stage in economic growth and business operations alike. Public discourse about the practical and ethical implications of AI has mainly focussed on the societal level. There is an emerging knowledge base on AI risks to human rights around data security and privacy concerns. A separate strand of work has highlighted the stresses of working in the gig economy. This prevailing focus on human rights and gig impacts has been at the expense of a closer (...) look at how AI may be reshaping traditional workplace relations and, more specifically, workplace health and safety. To address this gap, we outline a conceptual model for developing an AI Work Health and Safety (WHS) Scorecard as a tool to assess and manage the potential risks and hazards to workers resulting from AI use in a workplace. A qualitative, practice-led research study of AI adopters was used to generate and test a novel list of potential AI risks to worker health and safety. Risks were identified after cross-referencing Australian AI Ethics Principles and Principles of Good Work Design with AI ideation, design and implementation stages captured by the AI Canvas, a framework otherwise used for assessing the commercial potential of AI to a business. The unique contribution of this research is the development of a novel matrix itemising currently known or anticipated risks to the WHS and ethical aspects at each AI adoption stage. (shrink)

As Artificial Intelligence (AI) becomes more and more pervasive in our everyday life, new questions arise about its ethical and social impacts. Such issues concern all stakeholders involved in or committed to the design, implementation, deployment, and use of the technology. The present document addresses these preoccupations by introducing and discussing a set of practical obligations and recommendations for the development of applications and systems based on AI techniques. With this work we hope to contribute to spreading awareness on (...) the many social challenges posed by AI and encouraging the establishment of good practices throughout the relevant social areas. As points of novelty, the paper elaborates on an integrated view that combines both human rights and ethical concepts to reap the benefits of the two approaches. Moreover, it proposes innovative recommendations, such as those on redress and governance, which add further insight to the debate. Finally, it incorporates a specific focus on the Italian Constitution, thus offering an example of how core legislations of Member States might contribute to further specify and enrich the EU normative framework on AI. (shrink)

Background: Allocation of scarce organs for transplantation is ethically challenging. Artificial intelligence (AI) has been proposed to assist in liver allocation, however the ethics of this remains unexplored and the view of the public unknown. The aim of this paper was to assess public attitudes on whether AI should be used in liver allocation and how it should be implemented. Methods: We first introduce some potential ethical issues concerning AI in liver allocation, before analysing a pilot survey including online responses (...) from 172 UK laypeople, recruited through Prolific Academic. Findings: Most participants found AI in liver allocation acceptable (69.2%) and would not be less likely to donate their organs if AI was used in allocation (72.7%). Respondents thought AI was more likely to be consistent and less biased compared to humans, although were concerned about the “dehumanisation of healthcare” and whether AI could consider important nuances in allocation decisions. Participants valued accuracy, impartiality, and consistency in a decision-maker, more than interpretability and empathy. Respondents were split on whether AI should be trained on previous decisions or programmed with specific objectives. Whether allocation decisions were made by transplant committee or AI, participants valued consideration of urgency, survival likelihood, life years gained, age, future medication compliance, quality of life, future alcohol use and past alcohol use. On the other hand, the majority thought the following factors were not relevant to prioritisation: past crime, future crime, future societal contribution, social disadvantage, and gender. Conclusions: There are good reasons to use AI in liver allocation, and our sample of participants appeared to support its use. If confirmed, this support would give democratic legitimacy to the use of AI in this context and reduce the risk that donation rates could be affected negatively. Our findings on specific ethical concerns also identify potential expectations and reservations laypeople have regarding AI in this area, which can inform how AI in liver allocation could be best implemented. (shrink)

An under-examined aspect of human–robot interaction that warrants further exploration is whether robots should be permitted to influence a user’s behavior for that person’s own good. Yet an even more controversial practice could be on the horizon, which is allowing a robot to “nudge” a user’s behavior for the good of society. In this article, we examine the feasibility of creating companion robots that would seek to nurture a user’s empathy toward other human beings. As more and more (...) computing devices subtly and overtly influence human behavior, it is important to draw attention to whether it would be ethically appropriate for roboticists to pursue this type of design pathway. Our primary focus is on whether a companion robot could encourage humans to perform charitable acts; this design possibility illustrates the range of socially just actions that a robot could potentially elicit from a user and what the associated ethical concerns may be. (shrink)

Robotic and artificially intelligent (AI) systems are becoming prevalent in our day-to-day lives. As human interaction is increasingly replaced by human–computer and human–robot interaction (HCI and HRI), we occasionally speak and act as though we are blaming or praising various technological devices. While such responses may arise naturally, they are still unusual. Indeed, for some authors, it is the programmers or users—and not the system itself—that we properly hold responsible in these cases. Furthermore, some argue that since directing blame or (...) praise at technology itself is unfitting, designing systems in ways that encourage such practices can only exacerbate the problem. On the other hand, there may be good moral reasons to continue engaging in our natural practices, even in cases involving AI systems or robots. In particular, daily interactions with technology may stand to impact the development of our moral practices in human-to-human interactions. In this paper, we put forward an empirically grounded argument in favor of some technologies being designed for social responsiveness. Although our usual practices will likely undergo adjustments in response to innovative technologies, some systems which we encounter can be designed to accommodate our natural moral responses. In short, fostering HCI and HRI that sustains and promotes our natural moral practices calls for a co-developmental process with some AI and robotic technologies. (shrink)

Today, the earth is an interconnected globe humming with electronic transmissions-a chattering planet nestled in the provident silence of space. The ethical question is whether this is contributing to authentic human development and helping individuals and peoples to be true to their transcendent destiny. The new media are powerful tools for education, cultural enrichment, commercial activity, political participation, intercultural dialogue and understanding. They also can serve the cause of religion. Yet the new information technology needs to be informed and guided (...) by solidarity in the service of the common good bridging the digital divide within and among nations. The value-laden message of Western secular culture to people and traditional societies in many cases ill-prepared to evaluate it leads to widespread crisis, for example, in regard to marriage and family life. This technology can be a means for solving human problems, promoting the integral development of persons, creating a world governed by justice, peace, and love. It also can help men and women in their age-old search for self-understanding. Like today's world itself, the world of media, including the Internet, has been brought by Christ, inchoately yet truly, within the boundaries of the kingdom of God and placed in service to the word of salvation. (shrink)

The mobility restrictions related to COVID-19 pandemic have resulted in the biggest disruption to individual mobilities in modern times. The crisis is clearly spatial in nature, and examining the geographical aspect is important in understanding the broad implications of the pandemic. The avalanche of mobile Big Data makes it possible to study the spatial effects of the crisis with spatiotemporal detail at the national and global scales. However, the current crisis also highlights serious limitations in the readiness to take the (...) advantage of mobile Big Data for social good, both within and beyond the interests of health sector. We propose two strategical pathways for the future use of mobile Big Data for societal impact assessment, addressing access to both raw mobile Big Data as well as aggregated data products. Both pathways require careful considerations of privacy issues, harmonized and transparent methodologies, and attention to the representativeness, reliability and continuity of data. The goal is to be better prepared to use mobile Big Data in future crises. (shrink)

The increasing implementation of and reliance on machine-learning (ML) algorithms to perform tasks, deliver services and make decisions in health and healthcare have made the need for fairness in ML, and more specifically in healthcare ML algorithms (HMLA), a very important and urgent task. However, while the debate on fairness in the ethics of artificial intelligence (AI) and in HMLA has grown significantly over the last decade, the very concept of fairness as an ethical value has not yet been sufficiently (...) explored. Our paper aims to fill this gap and address the AI ethics principle of fairness from a conceptual standpoint, drawing insights from accounts of fairness elaborated in moral philosophy and using them to conceptualise fairness as an ethical value and to redefine fairness in HMLA accordingly. To achieve our goal, following a first section aimed at clarifying the background, methodology and structure of the paper, in the second section, we provide an overview of the discussion of the AI ethics principle of fairness in HMLA and show that the concept of fairness underlying this debate is framed in purely distributive terms and overlaps with non-discrimination, which is defined in turn as the absence of biases. After showing that this framing is inadequate, in the third section, we pursue an ethical inquiry into the concept of fairness and argue that fairness ought to be conceived of as an ethical value. Following a clarification of the relationship between fairness and non-discrimination, we show that the two do not overlap and that fairness requires much more than just non-discrimination. Moreover, we highlight that fairness not only has a distributive but also a socio-relational dimension. Finally, we pinpoint the constitutive components of fairness. In doing so, we base our arguments on a renewed reflection on the concept of respect, which goes beyond the idea of equal respect to include respect for individual persons. In the fourth section, we analyse the implications of our conceptual redefinition of fairness as an ethical value in the discussion of fairness in HMLA. Here, we claim that fairness requires more than non-discrimination and the absence of biases as well as more than just distribution; it needs to ensure that HMLA respects persons both as persons and as particular individuals. Finally, in the fifth section, we sketch some broader implications and show how our inquiry can contribute to making HMLA and, more generally, AI promote the social good and a fairer society. (shrink)

Artificial Intelligence (AI) contributes to common goods and common harms in our everyday lives. In light of the Collingridge dilemma, information about both the actual and potential harm of AI is explored and myths about AI are dispelled. Catholic health care is then presented as being in a unique position to exert its influence to model the use of AI systems that minimizes the risk of harm and promotes human flourishing and the common good.

Policy and guideline proposals for ethical artificial intelligence research have proliferated in recent years. These are supposed to guide the socially-responsible development of AI for a common good. However, there typically exist incentives for non-cooperation ; and, these proposals often lack effective mechanisms to enforce their own normative claims. The situation just described constitutes a social dilemma—namely, a situation where no one has an individual incentive to cooperate, though mutual cooperation would lead to the best outcome for all (...) involved. In this paper, we use stochastic evolutionary game dynamics to model this social dilemma in the context of the ethical development of artificial intelligence. This formalism allows us to isolate variables that may be intervened upon, thus providing actionable suggestions for increased cooperation amongst numerous stakeholders in AI. Our results show how stochastic effects can help make cooperation viable in such a scenario. They suggest that coordination for a common good should be attempted in smaller groups in which the cost of cooperation is low, and the perceived risk of failure is high. This provides insight into the conditions under which we should expect such ethics proposals to be successful with regard to their scope, scale, and content. (shrink)

The US is promoting a new vision of a “Good AI Society” through its recent AI Bill of Rights. This offers a promising vision of community-oriented equity unique amongst peer countries. However, it leaves the door open for potential rights violations. Furthermore, it may have some federal impact, but it is non-binding, and without concrete legislation, the private sector is likely to ignore it.

In 2017, Tom Gruber held a TED talk, in which he presented a vision of improving and enhancing humanity with AI technology. Specifically, Gruber suggested that an AI-improved personal memory (APM) would benefit people by improving their “mental gain”, making us more creative, improving our “social grace”, enabling us to do “science on our own data about what makes us feel good and stay healthy”, and, for people suffering from dementia, it “could make a difference between a life (...) of isolation and a life of dignity and connection”. -/- In this paper, Gruber’s idea will be critically assessed. Firstly, it will be argued that most of his pro-arguments for the APM are questionable. Secondly, the APM will also be criticized for other reasons, including the risks and affects to the users’ and other’s privacy and the users’ autonomy. (shrink)

Should you be targeted by police for a crime that AI predicts you will commit? In this paper, we analyse when, and to what extent, the person-based predictive policing (PP) — using AI technology to identify and handle individuals who are likely to breach the law — could be justifiably employed. We first examine PP’s epistemological limits, and then argue that these defects by no means refrain from its usage; they are worse in humans. Next, based on major AI ethics (...) guidelines (IEEE, EU, and RIKEN, etc.), we refine three basic moral principles specific to person-based PP. We also derive further requirements from case studies, including debates in Chicago, New Orleans, San Francisco, Tokyo, and cities in China. Instead of rejecting PP programs, we analyse what necessary conditions should be met for using the tool to achieve social good. While acknowledging its risks, we conclude that the person-based PP could be beneficial in community policing, especially when merging into a larger governance framework of the social safety net. (shrink)

As suicide rates increase worldwide, the mental health industry has reached an impasse in attempts to assess patients, predict risk, and prevent suicide. Traditional assessment tools are no more accurate than chance, prompting the need to explore new avenues in artificial intelligence (AI). Early studies into these tools show potential with higher accuracy rates than previous methods alone. Medical researchers, computer scientists, and social media companies are exploring these avenues. While Facebook leads the pack, its efforts stem from scrutiny (...) following suicides and suicide attempts broadcasted on Facebook Live. The company continues to face scrutiny for ethical, privacy, and safety concerns associated with the quick rollout of proprietary AI technology that seems more focused on the optics of the company than the protection of its billions of users. This paper explores some of these issues, including a lack of transparency, questionable data practices, escalation to law enforcement, little to no regulation, potential for bias, increased viewership of troubling content, and worsening thoughts of suicide among vulnerable Facebook users. Despite AI tools showing promise in improving the accuracy of suicide risk predictions over traditional methods, without regulation, external review, and increased protection of users and data, these tools have the potential to cause more harm than good in the hands of powerful companies like Facebook. (shrink)

Social media have undoubtedly changed our ways of living. Their presence concerns an increasing number of users (over 4,74 billion) and pervasively expands in the most diverse areas of human life. Marketing, education, news, data, and sociality are just a few of the many areas in which social media play now a central role. Recently, some attention toward the link between social media and political participation has emerged. Works in the field of artificial intelligence have already pointed (...) out that there is a close link between the use of machine learning algorithms in social media and possible epistemic isolation, which could lead to political radicalization. The idea supporting this paper is that artificial intelligence for social media can actively put users’ deliberative capacity at risk and foster political extremism. To prove these claims, I proceed along two lines of inquiry. First, I focus on filter bubbles, namely the result of selections made by algorithms that recommend contents that meet users’ expectations and opinions. To analyze this phenomenon, I refer to the Deweyan model of experience. Second, I connect the filter bubbles problem to the Deweyan idea of deliberative and participatory democracy and Nussbaum’s concept of political compassion. The purpose of this paper is to provide a philosophical foundation that can both (1) effectively serve as a method for analyzing machine learning algorithms and their potential problems in relation to political extremism, and (2) be adopted as a standard to counter the danger of extremism associated with social media experience. (shrink)

Since some AI algorithms with high predictive power have impacted human integrity, safety has become a crucial challenge in adopting and deploying AI. Although it is impossible to prevent an algorithm from failing in complex tasks, it is crucial to ensure that it fails safely, especially if it is a critical system. Moreover, due to AI’s unbridled development, it is imperative to minimize the methodological gaps in these systems’ engineering. This paper uses the well-known Box-Jenkins method for statistical modeling as (...) a framework to identify engineering pitfalls in the adjustment and validation of AI models. Step by step, we point out state-of-the-art strategies and good practices to tackle these engineering drawbacks. In the final step, we integrate an internal and external validation scheme that might support an iterative evaluation of the normative, perceived, substantive, social, and environmental safety of all AI systems. (shrink)

Kate Crawford presents AI as “both reflecting and producing social relations and understandings of the world”; or again, as “a form of exercising power, and a way of seeing… as a manifestation of highly organized capital backed by vast systems of extraction and logistics, with supply chains that wrap around the entire planet”. I interpret these material insights through a Marxist understanding of ideology, with reference to Marx/Engels, Guy Debord and Louis Althusser. In the German Ideology, Marx and Engels (...) present ideology as the alienated and alienating discourse of exchange, dissimulating the material forces of production and its science. Guy Debord presents the spectacle as the further representation of capital. The spectacle is a total Weltanschauung, pure appearance, where “whatever appears is good and where what is good, appears.” Above all, the spectacle is ideology, representing “the impoverishment, enslavement and negation of real life”. Louis Althusser presents ideology as essential for the reproduction of labor-power and capital. His notion of “interpellation” explains the subjective uptake of ideology, how we respond, “yes!” to its call. The algorithms of AI are enactments of surplus value generating capital as data. The AI data-world is the spectacle, “the autonomous movement of non-life” (Debord) where the proletariat/consumer is the producer of their own ideological subjection. Is resistance possible? What form might it take? And if AI is understood as ideology, i.e. as a language that obscures the true conditions of real life, then what are those true conditions? What is “real life”? (shrink)

Will AI take away your job? The answer is probably not. AI systems can be good predictive systems and be very good at pattern recognition. AI systems have a very repetitive approach to sets of data, which can be useful in certain circumstances. However, AI does make obvious mistakes. This is because AI does not have a sense of context. As Humans we have years of experience in the real world. We have vast amounts of contextual data stored (...) in our brains that make it possible to predict and to know boundaries of the real world so that even if we have never been in a particular situation, we are still able to deal with it. The unknown situation is where AI will fall down. In engineering, AI is being developed to monitor and control certain systems, this could extend to Nuclear Power Plants for example. If we examine control systems for Nuclear Power Plants on vessels, the system would likely be programmed using the safety of the reactor as the main priority. Automation is crucial in engineering systems like this, as the likelihood and cost of human error is high and human reaction times are slow in comparison to the system. However, there is always the possibility of unforeseen external factors that may override plant safety which cannot be programmed. If we look at a plant onboard a Submarine, for example, we see such additional factors as ship safety. In one example, the power plant might breach safety parameters, in which case an automatic system may shut down the reactor. However, there might be a greater urgency such as a flood in another compartment or an attack upon the vessel, that would override shutting down the reactor. The AI system can neither observe nor utilise information from the external environment to make this type of judgement, whereas the operator may be completely aware. To programme context into AI systems would be near impossible. We still need operators as we still need to understand the environment around us, which may contain an infinite number of possibilities. In a further example of this; if we examine the ICO (Information Commissioner's Office) guidance, it is clear that in clinical settings a human being will always be required to validate findings and to provide context. Sometimes having a lot of data just doesn't replace social interaction, intuition and experience. (shrink)

Artificial intelligence (AI) technology poses possible threats to existing jobs. These threats extend not just to the number of jobs available but also to their quality. In the future, so some predict, workers could face fewer and potentially worse jobs, at least if society does not embrace reforms that manage the coming AI revolution. This paper uses the example of Daron Acemoglu and Simon Johnson’s recent book—_Power and Progress_ (2023)—to illustrate some of the dilemmas and options for managing the future (...) of work under AI. Acemoglu and Johnson, while warning of the potential negative effects of an AI-driven automation, argue that AI can be used for positive ends. In particular, they argue for its uses in creating more ‘good jobs’. This outcome will depend on democratising AI technology. This paper is critical of the approach taken by Acemoglu and Johnson—specifically, it misses the possibility for using AI to lighten work (i.e., to reduce its duration and improve its quality). This paper stresses the potential benefits of automation as a mechanism for lightening work. Its key arguments aim to advance critical debates focused on creating a future in which AI works for people not just for profits. (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 Christian List (2021) has recently argued, the increasing arrival of powerful AI systems that operate autonomously in high-stakes contexts creates a need for “future-proofing” our regulatory frameworks, i.e., for reassessing them in the face of these developments. One core part of our regulatory frameworks that dominates our everyday moral interactions is blame. Therefore, “future-proofing” our extant regulatory frameworks in the face of the increasing arrival of powerful AI systems requires, among others things, that we ask whether it makes sense (...) to extend our blaming practices to these systems. In the paper, we argue for the admittedly surprising thesis that this question should be answered in the affirmative: contrary to what one might initially think, it can make a lot of sense to blame AI systems, since, as we furthermore argue, many of the important functions that are fulfilled by blaming humans can also be served by blaming AI systems. The paper concludes that this result gives us a good pro tanto reason to extend our blame practices to AI systems. (shrink)

According to how AI has defined itself from its beginning, thinking in non-living matter, i.e., without life, is possible. The premise of symbolic AI is that operating on representations of reality machines can understand it. When this assumption did not work as expected, the mathematical model of the neuron became the engine of artificial “brains.” Connectionism followed. Currently, in the context of Machine Learning success, attempts are made at integrating the symbolic and connectionist paths. There is hope that Artificial General (...) Intelligence (AGI) performance can be achieved. As encouraging as neuro-symbolic AI seems to be, it remains unclear whether AGI is actually a moving target as long as AI itself remains ambiguously defined. This paper makes the argument that the intelligence of machines, expressed in their performance, reflects how adequate the means used for achieving it are. Therefore, energy use and the amount of data necessary qualify as a good metric for comparing natural and artificial performance. (shrink)