This paper explores artificial intelligence (AI) ethics from an Islamic perspective at a critical time for AI ethical norm-setting. It advocates for a pluralist approach to ethical AI benchmarking. As rapid advancements in AI technologies pose challenges surrounding autonomy, privacy, fairness, and transparency, the prevailing ethical discourse has been predominantly Western or Eurocentric. To address this imbalance, this paper delves into the Islamic ethical traditions to develop a framework that contributes to the global debate on optimal norm setting for designing (...) and using AI technologies.The paper outlines Islamic parameters for ethical values and moral actions in the context of AI's ethical uncertainties. It emphasizes the significance of both textual and non-textual Islamic sources in addressing these uncertainties while placing a strong emphasis on the notion of "good" or "maṣlaḥa" as a normative guide for AI's ethical evaluation. Defining maṣlaḥa as an ethical state of affairs in harmony with divine will, the paper highlights the coexistence of two interpretations of maṣlaḥa: welfarist/utility-based and duty-based. Islamic jurisprudence allows for arguments supporting ethical choices that prioritize building the technical infrastructure for AI to maximize utility. Conversely, it also supports choices that reject consequential utility calculations as the sole measure of value in determining ethical responses to AI advancements. (shrink)

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)

Default reasoning occurs when the available information does not deductively guarantee the truth of the conclusion; and the conclusion is nonetheless correctly arrived at. The formalisms that have been developed in Artificial Intelligence to capture this mode of reasoning have suffered from a lack of agreement as to which non-monotonic inferences should be considered correct; and so Lifschitz 1989 produced a set of “Nonmonotonic Benchmark Problems” which all future formalisms are supposed to honor. The present work investigates the extent to (...) which humans follow the prescriptions set out in these Benchmark Problems. (shrink)

Purpose Digital transformation leverages digital technologies to change current processes and introduce new processes in any organisation’s business model, customer/user experience and operational processes. Artificial intelligence plays a significant role in achieving DT. As DT is touching each sphere of humanity, AI led DT is raising many fundamental questions. These questions raise concerns for the systems deployed, how they should behave, what risks they carry, the monitoring and evaluation control we have in hand, etc. These issues call for the need (...) to integrate ethics in AI led DT. The purpose of this study is to develop an “AI led ethical digital transformation framework”. Design/methodology/approach Based on the literature survey, various existing business ethics decision-making models were synthesised. The authors mapped essential characteristics such as intensity and the individual, organisational and opportunity factors of ethics models with the proposed AI led ethical DT. The DT framework is evaluated using a thematic analysis of 23 expert interviews with relevant AI ethics personas from industry and society. The qualitative data of the interviews and opinion data has been analysed using MAXQDA software. Findings The authors have explored how AI can drive the ethical DT framework and have identified the core constituents of developing an AI led ethical DT framework. Backed by established ethical theories, the paper presents how DT pillars are related and sequenced to ethical factors. This research provides the potential to examine theoretically sequenced ethical factors with practical DT pillars. Originality/value The study establishes deduced and induced ethical value codes based on thematic analysis to develop guidelines for the pursuit of ethical DT. The authors identify four unique induced themes, namely, corporate social responsibility, perceived value, standard benchmarking and learning willingness. The comprehensive findings of this research, supported by a robust theoretical background, have substantial implications for academic research and corporate applicability. The proposed AI led ethical DT framework is unique and can be used for integrated social, technological and economic ethical research. (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)

Artificial Intelligence is making rapid and remarkable progress in the development of more sophisticated and powerful systems. However, the acknowledgement of several problems with modern machine learning approaches has prompted a shift in AI benchmarking away from task-oriented testing towards ability-oriented testing, in which AI systems are tested on their capacity to solve certain kinds of novel problems. The Animal-AI Environment is one such benchmark which aims to apply the ability-oriented testing used in comparative psychology to AI systems. Here, we (...) present the first direct human-AI comparison in the Animal-AI Environment, using children aged 6–10. We found that children of all ages were significantly better than a sample of 30 AIs across most of the tests we examined, as well as performing significantly better than the two top-scoring AIs, “ironbar” and “Trrrrr,” from the Animal-AI Olympics Competition 2019. While children and AIs performed similarly on basic navigational tasks, AIs performed significantly worse in more complex cognitive tests, including detour tasks, spatial elimination tasks, and object permanence tasks, indicating that AIs lack several cognitive abilities that children aged 6–10 possess. Both children and AIs performed poorly on tool-use tasks, suggesting that these tests are challenging for both biological and non-biological machines. (shrink)

A recent study in Australasia (Selvaratnam & Venaruzzo, 2023) revealed some challenges and gaps in the governance of AI and data in higher education, mainly from the human-centeredness perspectives of accessibility, inclusivity and wellbeing. This paper is a narrative review to discern principles of a human-centered approach to the governance of artificial intelligence (AI), benchmarking literature, policies and practice across diverse geopolitical contexts for higher education, synthesizing the review results to provide guiding principles that can support this.

The ML community recognizes the importance of anticipating and mitigating the potential negative impacts of benchmark research. In this position paper, we argue that more attention needs to be paid to areas of ethical risk that lie at the technical and scientific core of ML benchmarks. We identify overlooked structural similarities between human IQ and ML benchmarks. Human intelligence and ML benchmarks share similarities in setting standards for describing, evaluating and comparing performance on tasks relevant to intelligence. (...) This enables us to unlock lessons from feminist philosophy of science scholarship that need to be considered by the ML benchmark community. Finally, we outline practical recommendations for benchmark research ethics and ethics review. (shrink)

This open access book explains how leading business organizations attempt to achieve the responsible and ethical use of artificial intelligence (AI) and other advanced information technologies. These technologies can produce tremendous insights and benefits. But they can also invade privacy, perpetuate bias, and otherwise injure people and society. To use these technologies successfully, organizations need to implement them responsibly and ethically. The question is: how to do this? Data ethics management, and this book, provide some answers. -/- The authors interviewed (...) and surveyed data ethics managers at leading companies. They asked why these experts see data ethics as important and how they seek to achieve it. This book conveys the results of that research on a concise, accessible way. -/- Much of the existing writing on data and AI ethics focuses either on macro-level ethical principles, or on micro-level product design and tooling. The interviews showed that companies need a third component: data ethics management. This third element consists of the management structures, processes, training and substantive benchmarks that companies use to operationalize their high-level ethical principles and to guide and hold accountable their developers. Data ethics management is the connective tissue makes ethical principles real. It is the focus of this book. -/- This book should be of use to organizations that wish to improve their own data ethics management efforts, legislators and policymakers who hope to build on existing management practices, scholars who study beyond compliance business behavior, and members of the public who want to understand better the threats that AI poses and how to reduce them. (shrink)

IGGA (Industrial Guidelines/policy statements for Generative AIs) is a comprehensive dataset comprising 160 guidelines and policy statements pertaining to the use of generative AIs and large language models across 14 industry sectors. These guidelines were systematically selected and gathered from official company websites and reliable sources spanning six continents. The dataset, containing 295,692 words, is designed to support various natural language processing tasks, including language modeling, sentiment analysis, semantic analysis, model synthesis, classification, and topic labeling. Additionally, it serves as a (...) benchmark for tasks such as ambiguity detection and requirements categorization. This resource aims to advance research in AI governance within industrial settings, fostering a deeper understanding of how AI technologies are integrated into industry practices. (2024-11-17). (shrink)

AGGA (Academic Guidelines for Generative AIs) is a dataset of 80 academic guidelines for the usage of generative AIs and large language models in academia, selected systematically and collected from official university websites across six continents. Comprising 181,225 words, the dataset supports natural language processing tasks such as language modeling, sentiment and semantic analysis, model synthesis, classification, and topic labeling. It can also serve as a benchmark for ambiguity detection and requirements categorization. This resource aims to facilitate research on AI (...) governance in educational contexts, promoting a deeper understanding of the integration of AI technologies in academia. (shrink)

The Turing test, which is a verbal test of the indistinguishability of machine and human intelligence, is a historically important idea that has set a way of thinking about the AI (artificial intelligence) project that is still relevant today. According to it, the benchmark/blueprint for AI is human intelligence, and the key skill of AI should be its communicative proficiency – which includes explaining decisions made by the machine. Passing the original Turing test by a machine does not guarantee that (...) the machine is, from the point of view of a human, trustworthy; on the contrary, the idea of a machine capable of “fooling” or “outsmarting” a human is inherent within the concept of the test. We postulate, that a test that guarantees a high level of trust should be: a) non-imitative, b) non-behavioral, c) focused on the ability to explain, taking into account, however, the design and rules of operation of the machine (and not just human expectations), d) focused on the machine’s ability to learn. (shrink)

The field of AI has undergone a series of transformations, each marking a new phase of development. The initial phase emphasized curation of symbolic models which excelled in capturing reasoning but were fragile and not scalable. The next phase was characterized by machine learning models—most recently large language models (LLMs)—which were more robust and easier to scale but struggled with reasoning. Now, we are witnessing a return to symbolic models as complementing machine learning. Successes of LLMs contrast with their inscrutability, (...) inaccuracy, and hallucinations, which underwrite concerns over the reliability and trustworthiness of these systems, motivating investigations into commonsense reasoning, AI explainability, and formal verification techniques. Moreover, proper assessments of hybrid machine learning/symbolic systems require novel strategies to facilitate comparisons of performance and guide future AI progress. The EDGeS AAAI 2023 Spring symposium brought together researchers focusing on novel assessments and benchmarks for evaluating hybrid and artificial general intelligence (AGI) systems. This symposium revealed what was already suspected: research concerning evaluation of machine learning/symbolic hybrids and AGI is, unfortunately, lacking. Even so, the discussion was fruitful. (shrink)

Accurate skin lesion segmentation is an important task in dermatology for facilitating early diagnosis and treatment planning. The challenges in skin lesion segmentation comprehend the variability in lesion, low contrast, heterogeneous backgrounds, overlapping or connected lesions, noise and certain artifacts. Despite of these challenges, Deep learning models accomplish remarkable results for skin lesion segmentation by automatically learning discriminative features. The current research introduces a novel approach utilizing the ASSP-based Deeplabv3+ for skin lesion segmentation along with other UNET-based learners while employing (...) VGG-16, VGG-19 and Dense nets as encoders. In addition, an analysis is conducted on GAN-UNET to evaluate the potential of Generative Artificial Intelligence in generating segmented images of skin lesions. Three benchmark medical image datasets, namely ISIC-2016, ISIC-2018, and HAM10000 Lesion Boundary Segmentation, are used to evaluate all five models. The models are trained exclusively on the ISIC-2018 dataset. A comparative analysis is performed, comparing the performance of these models against state-of-the-art segmentation methods, focusing on standard computer vision metrics. The proposed Deeplabv3+ model outperforms by showcasing its ability to accurately delineate skin lesions and surpassing existing techniques in terms of segmentation accuracy as 0.97, Jaccard coefficient as 0.84 and dice coefficient as 0.91. (shrink)

In the last 20 years the Turing test has been left further behind by new developments in artificial intelligence. At the same time, however, these developments have revived some key elements of the Turing test: imitation and adversarialness. On the one hand, many generative models, such as generative adversarial networks, build imitators under an adversarial setting that strongly resembles the Turing test. The term “Turing learning” has been used for this kind of setting. On the other hand, AI benchmarks (...) are suffering an adversarial situation too, with a ‘challenge-solve-and-replace’ evaluation dynamics whenever human performance is ‘imitated’. The particular AI community rushes to replace the old benchmark by a more challenging benchmark, one for which human performance would still be beyond AI. These two phenomena related to the Turing test are sufficiently distinctive, important and general for a detailed analysis. This is the main goal of this paper. After recognising the abyss that appears beyond superhuman performance, we build on Turing learning to identify two different evaluation schemas: Turing testing and adversarial testing. We revisit some of the key questions surrounding the Turing test, such as ‘understanding’, commonsense reasoning and extracting meaning from the world, and explore how the new testing paradigms should work to unmask the limitations of current and future AI. Finally, we discuss how behavioural similarity metrics could be used to create taxonomies for artificial and natural intelligence. Both testing schemas should complete a transition in which humans should give way to machines—not only as references to be imitated but also as judges—when pursuing and measuring machine intelligence. (shrink)

Despite the exceptional performance of large language models (LLMs) on a wide range of tasks involving natural language processing and reasoning, there has been sharp disagreement as to whether their abilities extend to more creative human abilities. A core example is the interpretation of novel metaphors. Here we assessed the ability of GPT-4, a state-of-the-art large language model, to provide natural-language interpretations of a recent AI benchmark (Fig-QA dataset), novel literary metaphors drawn from Serbian poetry and translated into English, and (...) entire novel English poems. GPT-4 outperformed previous AI models on the Fig-QA dataset. For metaphors drawn from Serbian poetry, human judges – blind to the fact that an AI model was involved – rated metaphor interpretations generated by GPT-4 as superior to those provided by a group of college students. In interpreting reversed metaphors, GPT-4, as well as humans, exhibited signs of sensitivity to the Gricean cooperative principle. In addition, for several novel English poems GPT-4 produced interpretations that were rated as excellent or good by a human literary critic. These results indicate that LLMs such as GPT-4 have acquired an emergent ability to interpret literary metaphors, including those embedded in novel poems. (shrink)

Given actual autonomous systems with capacities for harm and the public’s apparent willingness to take moral advice from large language models (LLMs), Einar Duenger Bohn’s (2024) renewed discussion of the Moral Turing Test (MTT) is timely. Bohn’s aim is to defend an unequivocally behavioural test. In this paper, I argue against this direction. Interpreted as testing mere behaviour, the Turing test is a poor test of either intelligence or moral agency, and neither Bohn’s version of the test nor Allen, Varner (...) and Zinser’s influential (2000) version avoids these problems. Also, the MTT’s advantages as advertised by Bohn do not hold up: it is an open empirical question whether embracing a merely behavioural MTT will significantly reduce the challenges in building a computer to pass the test. This issue aside, I argue that Turing’s actual test is superior to current AI benchmarks and to Bohn’s version of the test. A test of moral reasoning or agency in machines that is based on Turing’s actual test has advantages over current tests of moral reasoning in AI, including versions of the MTT. Such a test is an intriguing possibility still to be investigated. (shrink)

Given actual autonomous systems with capacities for harm and the public’s apparent willingness to take moral advice from large language models (LLMs), Einar Duenger Bohn’s (2024) renewed discussion of the Moral Turing Test (MTT) is timely. Bohn’s aim is to defend an unequivocally behavioural test. In this paper, I argue against this direction. Interpreted as testing mere behaviour, the Turing test is a poor test of either intelligence or moral agency, and neither Bohn’s version of the test nor Allen, Varner (...) and Zinser’s influential (2000) version avoids these problems. Also, the MTT’s advantages as advertised by Bohn do not hold up: it is an open empirical question whether embracing a merely behavioural MTT will significantly reduce the challenges in building a computer to pass the test. This issue aside, I argue that Turing’s actual test is superior to current AI benchmarks and to Bohn’s version of the test. A test of moral reasoning or agency in machines that is based on Turing’s actual test has advantages over current tests of moral reasoning in AI, including versions of the MTT. Such a test is an intriguing possibility still to be investigated. (shrink)

Despite the exceptional performance of large language models (LLMs) on a wide range of tasks involving natural language processing and reasoning, there has been sharp disagreement as to whether their abilities extend to more creative human abilities. A core example is the interpretation of novel metaphors. Here we assessed the ability of GPT-4, a state-of-the-art large language model, to provide natural-language interpretations of a recent AI benchmark (Fig-QA dataset), novel literary metaphors drawn from Serbian poetry and translated into English, and (...) entire novel English poems. GPT-4 outperformed previous AI models on the Fig-QA dataset. For metaphors drawn from Serbian poetry, human judges – blind to the fact that an AI model was involved – rated metaphor interpretations generated by GPT-4 as superior to those provided by a group of college students. In interpreting reversed metaphors, GPT-4, as well as humans, exhibited signs of sensitivity to the Gricean cooperative principle. In addition, for several novel English poems GPT-4 produced interpretations that were rated as excellent or good by a human literary critic. These results indicate that LLMs such as GPT-4 have acquired an emergent ability to interpret literary metaphors, including those embedded in novel poems. (shrink)

Traditional medical Artificial Intelligence models, approved for clinical use, restrict themselves to single-modal data e.g. images only, limiting their applicability in the complex, multimodal environment of medical diagnosis and treatment. Multimodal Transformer Models in healthcare can effectively process and interpret diverse data forms such as text, images, and structured data. They have demonstrated impressive performance on standard benchmarks like USLME question banks and continue to improve with scale. However, the adoption of these advanced AI models is not without challenges. (...) While multimodal deep learning models like Transformers offer promising advancements in healthcare, their integration requires careful consideration of the accompanying ethical and environmental challenges. (shrink)

In developing artificial intelligence (AI), researchers often benchmark against human performance as a measure of progress. Is this kind of comparison possible for moral cognition? Given that human moral judgment often hinges on intangible properties like “intention” which may have no natural analog in artificial agents, it may prove difficult to design a “like‐for‐like” comparison between the moral behavior of artificial and human agents. What would a measure of moral behavior for both humans and AI look like? We unravel the (...) complexity of this question by discussing examples within reinforcement learning and generative AI, and we examine how the puzzle of evaluating artificial agents' moral cognition remains open for further investigation within cognitive science. (shrink)

As artificial intelligence (AI) integrates across social domains, prevailing technical paradigms often overlook human relational needs vital for cooperative resilience. Alternative pathways consciously supporting dignity and wisdom warrant consideration. Integrating seminal insights from virtue and care ethics, this article delineates the following four cardinal design principles prioritizing communal health: (1) affirming the sanctity of life; (2) nurturing healthy attachment; (3) facilitating communal wholeness; and (4) safeguarding societal resilience. Grounding my analysis in the rich traditions of moral philosophy, I argue that (...) these principles scaffold sustainable innovation trajectories that consciously center shared welfare advancement over detached technical capabilities or efficiency benchmarks alone. Elucidating connections with pioneering initiatives demonstrates fragments of this vision taking embryonic shape, yet pervasive adoption remains largely aspirational to date. Fulfilling dignity-based artificial intelligence demands ongoing collective commitment beyond firms’ profit motives or governance proceduralism. My conclusions urge technology policies and priorities directed toward empowering the vulnerability of people rather than controlling the optimization of systems. (shrink)

Recent breakthroughs in artificial intelligence have primarily resulted from training deep neural networks (DNNs) with vast numbers of adjustable parameters on enormous datasets. Due to their complex internal structure, DNNs are frequently characterized as inscrutable ``black boxes,'' making it challenging to interpret the mechanisms underlying their impressive performance. This opacity creates difficulties for explanation, safety assurance, trustworthiness, and comparisons to human cognition, leading to divergent perspectives on these systems. This chapter examines recent developments in interpretability methods for DNNs, with a (...) focus on interventionist approaches inspired by causal explanation in philosophy of science. We argue that these methods offer a promising avenue for understanding how DNNs process information compared to merely behavioral benchmarking and correlational probing. We review key interventionist methods and illustrate their application through practical case studies. These methods allow researchers to identify and manipulate specific computational components within DNNs, providing insights into their causal structure and internal representations. We situate these approaches within the broader framework of causal abstraction, which aims to align low-level neural computations with high-level interpretable models. While acknowledging current limitations, we contend that interventionist methods offer a path towards more rigorous and theoretically grounded interpretability research, potentially informing both AI development and computational cognitive neuroscience. (shrink)

This paper investigates the application of artificial intelligence (AI) language models in writing policy briefing notes within the context of public administration by juxtaposing the technologies’ performance against the traditional reliance on human expertise. Briefing notes are pivotal in informing decision-making processes in government contexts, which generally require high accuracy, clarity, and issue-relevance. Given the increasing integration of AI across various sectors, this study aims to evaluate the effectiveness and acceptability of AI-generated policy briefing notes. Using a structured evaluation-by-experts methodology, (...) the research scrutinizes and compares the output of three leading AI language models—OpenAI’s ChatGPT, Google’s Gemini, and Mistral’s Le Chat—across ten critical dimensions reflective of policy briefing notes quality. These dimensions encompass both structural and content-related aspects, ranging from linguistic precision to the depth and applicability of the generated information. The discussion is anchored in the technology acceptance model (TAM) theory and its extensions, which offer a theoretical framework for understanding the factors influencing the adoption and usefulness of technology in public administration. Our comparative analysis reveals that while AI models exhibit notable competencies in meeting some structural and linguistic benchmarks, they fail to address the nuances and depth required by policy experts for undertaking informed decision-making adequately. This discrepancy underscores the enduring value of human expertise in synthesizing complex information and navigating ethical considerations, even as AI enhances efficiency in certain aspects of the policy briefing note crafting process. (shrink)

State-of-the-art machine learning systems now routinely exceed benchmarks once thought beyond the ken of artificial intelligence (AI). Often these systems accomplish tasks through novel, insightful processes that remain inscrutable to even their human designers. Taking AlphaGo’s 2016 victory over Lee Sedol as a case study, this paper argues that such accomplishments manifest the essential features of achievements as laid out in Bradford’s 2015 book Achievement. Achievements like these are directly attributable to AI systems themselves. They are artificial achievements. This (...) opens the door to a challenge that calls out for further inquiry. Since Bradford grounds the intrinsic value of achievements in the exercise of distinctively human perfectionist capacities, the existence of artificial achievements raises the possibility that some achievements might be valueless. (shrink)

Over the past few years we have seen an increasing number of legal proceedings related to inappropriately implemented technology. At the same time career paths have diverged from the foundation of statistics out to Data Scientist, Machine Learning and AI. All of these new branches being fundamentally branches of statistics and mathematics. This has meant that formal training has struggled to keep up with what is required in the plethora of new roles. Mathematics as a taught subject is still based (...) in decades old teaching specifications and has not been updated centrally as a curriculum to include new technologies, coding or ethics. This subject area is firmly split between ICT and Mathematics in secondary school, continuing on to be split between Computer Science and Mathematics at University. As we move forwards with technology we see these once seperate fields becoming increasingly intertwined. We propose that existing provision for concepts such as ethics and societal responsibility in analysis currently exist but have not been incorporated into the mainstream curriculum of School or University. This is partially due to the split between fields in an educational setting but also the speed with which education is able to keep up with Industry and its requirements. Principles and frameworks of socially responsible modelling beginning at school level means that ethics and real-life modelling are introduced much earlier than normal. Integrating these concepts with philosophical principles of society and politics ensures a suitable background for future modellers and users of technology to draw on. Modelling is currently undertaken in technical sciences at University but the Subject Benchmark Statements are not current (Subject Benchmark Statements describe the nature of study and the academic standards expected of graduates in specific subject areas. They show what graduates might reasonably be expected to know, do and understand at the end of their studies). Even in 2019 the UK did not yet have Benchmark Statements that discuss the learning to be done in Higher Education around AI and advanced Machine Learning. Where there is provision for AI and Data Science within degree courses ethics is not generally highlighted as a key concept. As such there can be a lack of focus on the teaching of modelling or ethics as specific skills. The skills required to use a basic statistical model, for example would not be sufficient to start from scratch and build an ethical model reflecting real-world scenarios with which to inform policy or organisational decision making. This is a skill in itself and includes such aspects as awareness of data quality, ethics, user implementation problems, context and an understanding of the environment that the model is being created in. As the field of analytics has progressed so quickly in the last decade modules such as those covering AI have simply been bolted on to Maths or Computing degrees rather than being fully detailed as key areas of study or driving new, more integrated courses of study. This paper posits that gaps at primary, secondary and tertiary educational levels need to be addressed. Implementing and integrating key concepts from school level is essential so that areas such as assumptions, caveats, quality assurance and answering the right questions with constructive challenge become a cultural fixture. This not only helps developers of technology but users of rapidly developing technology also. In addition, leadership and soft skills as part of this education will ensure that a cultural shift can take place and promote continuous improvement in analysis within organisations. The addition of key concepts throughout the educational system and the updating of potentially outdated curriculums is key to ensuring a functional society. A society where every citizen is a user of tech and those that develop it can be ethical and socially responsible in its development. (shrink)

This paper examines the ongoing challenges of interdisciplinary collaboration in Machine Ethics (ME), particularly the integration of ethical decision-making capacities into AI systems. Despite increasing demands for ethical AI, ethicists often remain on the sidelines, contributing primarily to metaethical discussions without directly influencing the development of moral machines. This paper revisits concerns highlighted by Tolmeijer et al. (2020), who identified the pitfall that computer scientists may misinterpret ethical theories without philosophical input. Using the MACHIAVELLI moral benchmark and the Delphi artificial (...) moral agent as case studies, we analyze how these challenges persist. Our analysis indicates that the creators of MACHIAVELLI and Delphi “copy” ethical concepts and embed them in LLMs without questioning or challenging these concepts themselves sufficiently. If an ethical concept causes friction with the computer code, they only reduce and simplify the ethical concept in order to stay as close as possible to the original. We propose that ME should expand its focus to include both interdisciplinary efforts that embed existing ethical work into AI, and transdisciplinary research that fosters new interpretations of ethical concepts. Interdisciplinary and transdisciplinary approaches are crucial for creating AI systems that are not only effective but also socially responsible. To enhance collaboration between ethicists and computer scientists, we recommend the use of Socratic Dialogue as a methodological tool, promoting deeper understanding of key terms and more effective integration of ethics in AI development. (shrink)

The application of artificial intelligence (AI) to judicial decision-making has already begun in many jurisdictions around the world. While AI seems to promise greater fairness, access to justice, and legal certainty, issues of discrimination and transparency have emerged and put liberal democratic principles under pressure, most notably in the context of bail decisions. Despite this, there has been no systematic analysis of the risks to liberal democratic values from implementing AI into judicial decision-making. This article sets out to fill this (...) void by identifying and engaging with challenges arising from artificial judicial decision-making, focusing on three pillars of liberal democracy, namely equal treatment of citizens, transparency, and judicial independence. Methodologically, the work takes a comparative perspective between human and artificial decision-making, using the former as a normative benchmark to evaluate the latter. -/- The chapter first argues that AI that would improve on equal treatment of citizens has already been developed, but not yet adopted. Second, while the lack of transparency in AI decision-making poses severe risks which ought to be addressed, AI can also increase the transparency of options and trade-offs that policy makers face when considering the consequences of artificial judicial decision-making. Such transparency of options offers tremendous benefits from a democratic perspective. Third, the overall shift of power from human intuition to advanced AI may threaten judicial independence, and with it the separation of powers. While improvements regarding discrimination and transparency are available or on the horizon, it remains unclear how judicial independence can be protected, especially with the potential development of advanced artificial judicial intelligence (AAJI). Working out the political and legal infrastructure to reap the fruits of artificial judicial intelligence in a safe and stable manner should become a priority of future research in this area. (shrink)

This study investigates the intersection of existing computational frameworks for zoonotic disease control within the emerging societal paradigm, Society 5.0. Technologies in human-centric computing can facilitate real-time data collection and analysis, enabling early detection and rapid response to zoonotic disease outbreaks, thereby enhancing surveillance and containment efforts for public health protection. It aims to explore challenges and opportunities within these frameworks and delineate future research directions to serve as a benchmark. Conducting a three-layered analysis, the study identifies high-level technologies, second-layer (...) technologies within those brackets, and minutely analyzes each technology in the second layer. The focus includes a comprehensive examination of the evolving landscape of zoonotic disease control and the influence of computational tools in public health. The findings highlight the potential of leveraging computational frameworks to predict, monitor, and control zoonotic diseases more effectively within Society 5.0. The major identified thrust areas include One Health and governmental integration, digital and data-driven solutions, healthcare and medical technologies, technologies for communication and information dissemination, and socio-cultural and ethical considerations. The integration of large-scale healthcare services offer a robust data source for AI-driven models, enabling early detection and dynamic response to zoonotic threats using data acquired from IoT surveillance and data acquisition infrastructures. However, challenges such as data privacy and equitable healthcare access need addressing. The study emphasizes the opportunities arising from the integration of computational frameworks with Society 5.0 for zoonotic disease control, proposing potential benefits in terms of improved disease surveillance and response. To fully realize this potential, the study suggests exploring specific technological landscapes for further investigation in the context of zoonotic disease control. (shrink)

A growing body of research can be found in which roboticists are designing for reciprocity as a key construct for successful human–robot interaction (HRI). Given the centrality of reciprocity as a component for our moral lives (for moral development and maintaining the just society), this paper confronts the possibility of what things would look like if the benchmark to achieve perceived reciprocity were accomplished. Through an analysis of the value of reciprocity from the care ethics tradition the richness of reciprocity (...) as an inherent value is revealed: on the micro-level, as mutual care for immediate care givers, and on the macro-level, as foundational for a just society. Taking this understanding of reciprocity into consideration, it becomes clear that HRI cannot achieve this bidirectional value of reciprocity; a robot must deceive users into believing it is capable of reciprocating to humans or is deserving of reciprocation from humans. Moreover, on the macro-level, designing social robots for reciprocity threatens the ability and willingness to reciprocate to human care workers across society. Because of these concerns, I suggest re-thinking the goals of reciprocity in social robotics. Designing for reciprocity in social robotics should be dedicated to the design of robots to enhance the ability to mutually care for those that provide us with care, as opposed to designing for reciprocity between human and robot. (shrink)

This collection of quotes demonstrates the elegant simplicity of Ai Weiwei's thoughts on key aspects of his art, politics, and life. A master at communicating powerful ideas in astonishingly few words, Ai Weiwei is known for his innovative use of social media to disseminate his views. The book is organized into six categories: freedom of expression; art and activism; government, power, and moral choices; the digital world; history, the historical moment, and the future; and personal reflections. Together, these quotes span (...) some of the most revealing moments of Ai Weiwei's eventful career-from his risky investigation into student deaths in the 2008 Sichuan earthquake to his arbitrary arrest in 2011-providing a window into the mind of one of the world's most electrifying and courageous contemporary artists. Ai Weiwei is one of China's most influential and inspiring figures. Artist, architect, curator, and activist, he has been an outspoken critic of the Chinese government's stance on human rights and democracy. (shrink)

In this paper, we will report annotation experiments which show the advantage of applying a formal ontological methodology for constructing a schema for semantic annotation to mark up terms in the...

Objective: This study aimed to explore the relationship among cognitive fusion, experiential avoidance, and obsessive–compulsive symptoms in patients with obsessive–compulsive disorder.Methods: A total of 118 outpatient and inpatient patients with OCD and 109 healthy participants, gender- and age-matched, were selected using cognitive fusion questionnaire, acceptance and action questionnaire−2nd edition, Yale–Brown scale for obsessive–compulsive symptoms, Hamilton anxiety scale, and Hamilton depression scale for questionnaire testing and data analysis.Results: The levels of cognitive fusion and experiential avoidance in the OCD group were significantly (...) higher than those in the healthy control group. Regression analysis results showed that, in predicting the total score of obsessive–compulsive symptoms, AAQ-II and CFQ entered the equation, which explained 17.1% variance. In predicting anxiety, only AAQ-II entered the equation, which explained 13% variance. In the prediction of depression, AAQ-II entered the equation, which explained 17.7% variance.Conclusion: Cognitive fusion and experiential avoidance may be important factors for the maintenance of OCD, and experiential avoidance can positively predict the anxiety and depression of OCD patients. (shrink)