Artificial intelligence (AI) systems will increasingly be used to cause harm as they grow more capable. In fact, AI systems are already starting to help automate fraudulent activities, violate human rights, create harmful fake images, and identify dangerous toxins. To prevent some misuses of AI, we argue that targeted interventions on certain capabilities will be warranted. These restrictions may include controlling who can access certain types of AI models, what they can be used for, whether outputs are filtered or can (...) be traced back to their user, and the resources needed to develop them. We also contend that new restrictions on non-AI capabilities needed to cause harm will be required. For example, concerns about AI-enabled bioweapon acquisition have motivated efforts to introduce DNA synthesis screening. Though capability restrictions risk reducing use more than misuse (resulting in an unfavorable Misuse–Use Tradeoff), we argue that interventions on capabilities are warranted in some circumstances when other interventions are insufficient, the potential harm from misuse is high, and there are targeted interventions. We provide a taxonomy of interventions that can reduce AI misuse, focusing on the specific steps required for a misuse to cause harm (the Misuse Chain), and a framework to determine if an intervention is warranted. We exemplify our framework to three examples: predicting novel toxins, creating harmful images, and automating spear phishing campaigns. (shrink)

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

This study explores the capability of academic staff assisted by the Turnitin Artificial Intelligence (AI) detection tool to identify the use of AI-generated content in university assessments. 22 different experimental submissions were produced using Open AI’s ChatGPT tool, with prompting techniques used to reduce the likelihood of AI detectors identifying AI-generated content. These submissions were marked by 15 academic staff members alongside genuine student submissions. Although the AI detection tool identified 91% of the experimental submissions as containing AI-generated content, only (...) 54.8% of the content was identified as AI-generated, underscoring the challenges of detecting AI content when advanced prompting techniques are used. When academic staff members marked the experimental submissions, only 54.5% were reported to the academic misconduct process, emphasising the need for greater awareness of how the results of AI detectors may be interpreted. Similar performance in grades was obtained between student submissions and AI-generated content (AI mean grade: 52.3, Student mean grade: 54.4), showing the capabilities of AI tools in producing human-like responses in real-life assessment situations. Recommendations include adjusting the overall strategies for assessing university students in light of the availability of new Generative AI tools. This may include reducing the overall reliance on assessments where AI tools may be used to mimic human writing, or by using AI-inclusive assessments. Comprehensive training must be provided for both academic staff and students so that academic integrity may be preserved. (shrink)

Recent AI progress led to a boost in beneficial applications from multiple research areas including VR. Simultaneously, in this newly unfolding deepfake era, ethically and security-relevant disagreements arose in the scientific community regarding the epistemic capabilities of present-day AI. However, given what is at stake, one can postulate that for a responsible approach, prior to engaging in a rigorous epistemic assessment of AI, humans may profit from a self-questioning strategy, an examination and calibration of the experience of their own epistemic (...) agency – especially to counteract both intentional misdirection by unethical actors and unintentional epistemic self-sabotage. In this paper, we expound on a new avenue of utilizing AIVR tools to advance an AI-related misdirection awareness of humans in the deepfake era. Firstly, we harness scientific knowledge from the psychology and neuroscience of magic where the study of misdirection techniques is center stage. Secondly, we connect the latter to creativity research linking human creative potential to inspiration from the seemingly impossible. Overall, AIVR could become an empowering experiential testbed for human epistemic agency enabling a better rational evaluation of AI capabilities. However, a misuse of the same type of tools could yield AIVR safety risks if not counteracted preemptively. (shrink)

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

Ethics has powerful teeth, but these are barely being used in the ethics of AI today – it is no wonder the ethics of AI is then blamed for having no teeth. This article argues that ‘ethics’ in the current AI ethics field is largely ineffective, trapped in an ‘ethical principles’ approach and as such particularly prone to manipulation, especially by industry actors. Using ethics as a substitute for law risks its abuse and misuse. This significantly limits what ethics (...) can achieve and is a great loss to the AI field and its impacts on individuals and society. This article discusses these risks and then highlights the teeth of ethics and the essential value they can – and should – bring to AI ethics now. (shrink)

The study explores the future of AI-driven media and info-communication as envisioned by experts from all world regions, defining relevant terminology and expectations for 2050. Participants engaged in a 4-week series of surveys, questioning their definitions and projections about AI for the field of media and communication. Their expectations predict universal access to democratically available, automated, personalized and unbiased information determined by trusted narratives, recolonization of information technology and the demystification of the media process. These experts, as technology ambassadors, advocate (...) AI-to-AI solutions to mitigate technology-driven misuse and misinformation. The optimistic scenarios shift responsibility to future generations, relying on AI-driven solutions and finding inspiration in nature. Their present-based forecasts could be construed as being indicative of professional near-sightedness and cognitive dissonance. Visualizing our findings into a Glasses Model of AI Trust, the study contributes to key debates regarding AI policy, developmental trajectories, and academic research in media and info-communication fields. (shrink)

AI systems, which receive vast amounts of information including privacy information, are emerging. Protecting the privacy of the general public is an important issue for democracies. In this study, “Public space watched by AI- connected cameras” is taken as an example of an AI-system that is expected to be used for public purposes and has a relatively high privacy violation risk. It is defined as a wide public area where every point is monitored by multiple AI-connected street cameras. The following (...) four socially acceptable applications of “Public space watched by AI- connected cameras” are discussed: (1) Human/vehicle tracking, (2) Watching over children, (3) Detection of abnormal situations, (4) Criminal and terrorist behavior pattern analysis. As for privacy protection measures for AI systems, in addition to reliable protection itself, reliable recording and auditing to deter misuse and abuse is considered a prerequisite to ensure that the general public can feel secure. In this paper, the concept of “Verifiable record of AI output”, which is a verifiable and tamper-proof record and disclosure of AI output is proposed. “Verifiable record of AI output” is based on the idea that it may be more realistic to apply a simple exit control only for AI output to confirm rule conformance, rather than anonymizing huge AI inputs such as Big Data or controlling internal AI processes that are black-boxed. A concrete example of implementing “Verifiable record of AI output” in “Public space watched by AI-connected cameras” is proposed. In the “Verifiable record of AI output”, the AI system is enclosed in an outbound firewall, and only AI outputs that are confirmed to be in line with “the purpose of use approved in advance through social discussion” are allowed to be transmitted. The process from application to permission of transmission is anonymized and disclosed in a verifiable and tamper-proof manner. This public record can be verified by the general public or an auditing organization to ensure that any misuse or abuse of the AI system will be exposed. The “Verifiable record of AI output” proposed in this paper is characterized by reliable measurement and recording of AI output, and its disclosure for auditing purposes. The possibility of using this as a template to apply to other AI systems that receive Big Data input, including the privacy of the general public, is considered. (shrink)

That AI will have a major impact on society is no longer in question. Current debate turns instead on how far this impact will be positive or negative, for whom, in which ways, in which places, and on what timescale. In order to frame these questions in a more substantive way, in this prolegomena we introduce what we consider the four core opportunities for society offered by the use of AI, four associated risks which could emerge from its overuse or (...) misuse, and the opportunity costs associated with its under use. We then offer a high-level view of the emerging advantages for organisations of taking an ethical approach to developing and deploying AI. Finally, we introduce a set of five principles which should guide the development and deployment of AI technologies. The development of laws, policies and best practices for seizing the opportunities and minimizing the risks posed by AI technologies would benefit from building on ethical frameworks such as the one offered here. (shrink)

The use of counterfactuals for considerations of algorithmic fairness and explainability is gaining prominence within the machine learning community and industry. This paper argues for more caution with the use of counterfactuals when the facts to be considered are social categories such as race or gender. We review a broad body of papers from philosophy and social sciences on social ontology and the semantics of counterfactuals, and we conclude that the counterfactual approach in machine learning fairness and social explainability can (...) require an incoherent theory of what social categories are. Our findings suggest that most often the social categories may not admit counterfactual manipulation, and hence may not appropriately satisfy the demands for evaluating the truth or falsity of counterfactuals. This is important because the wide- spread use of counterfactuals in machine learning can lead to misleading results when applied in high-stakes domains. Accordingly, we argue that even though counterfactuals play an essential part in some causal inferences, their use for questions of algorithmic fairness and social explanations can create more problems than they resolve. Our positive result is a set of tenets about using counterfactuals for fairness and explanations in machine learning. (shrink)

In this paper we discuss the views on the Turing test of four influential thinkers who belong to the tradition of analytic philosophy: Ludwig Wittgenstein, Noam Chomsky, Hilary Putnam and John Searle. Based on various beliefs about philosophical and/or linguistic matters, they arrive at different assessments of both the significance and suitability of the imitation game for the development of cognitive science and AI models. Nevertheless, they share a rejection of the idea that one can treat Turing test as a (...) test for “machine thinking.” This seems to stem from a concern for the proper use of language —one that is a fundamental methodological commitment of analytic philosophy. (shrink)

This article argues that due to the difficulty in governing AI, it is essential to develop measures implemented early in the AI research process. The goal of dual use considerations is to create robust strategies that uphold AI’s integrity while protecting societal interests. The challenges of applying dual use frameworks to AI research are examined and dual use and dual use research of concern (DURC) are defined while highlighting the difficulties in balancing the technology’s benefits and risks. AI’s dual use (...) potential is discussed, particularly in areas like NLP and LLMs, and the need for early consideration of dual use risks to ensure ethical and secure development is underscored. In the section on shared responsibilities in AI research and avenues for mitigation strategies the importance of early-stage risk assessments and ethical guidelines to mitigate misuse is emphasized, accentuating self-governance within scientific communities and structured measures like checklists and pre-registration to promote responsible research practices. The final section argues that research ethics committees play a crucial role in evaluating the dual use implications of AI technologies within the research pipeline. The need for tailored ethics review processes is articulated, drawing parallels with medical research ethics committees. (shrink)

This paper explores the intersection of art history and AI technology. Special attention is paid to Generative Adversarial Networks (GANs), a machine learning technology widely used in AI art. This technology is particularly interesting to art history and the philosophy of art because it raises enduring questions about the creative process of artmaking, especially what constitutes a new and original work of art. While this is a relatively new area, it is possible to discern emerging directions where art and AI (...) intersect. One pressing issue concerns the use (or misuse) of algorithmically enabled technologies and datasets to perpetuate longstanding art-historical biases. The critical position adopted in this paper shares this concern. I examine a notable example of GAN technology that claims to create original art styles. While such experiments in computational creativity are technically interesting, their artistic aims can be questionable. I argue a mechanical model of artistic development based solely on the autonomy of style is problematic for at least two reasons: 1) it uncritically reinstates a formalist view of art history; 2) it recreates a narrow style-centric ‘historical modernism.’ However, despite voicing these reservations, I also discuss the potential of this technology to engage with topics of critical relevance to art history and the philosophy of art. (shrink)

Algorithms used in the public sector, e.g., for allocating social benefits or predicting fraud, often require involvement from multiple stakeholders at various phases of the algorithm’s life-cycle. This paper focuses on the communication issues between diverse stakeholders that can lead to misinterpretation and misuse of algorithmic systems. Ethnographic research was conducted via 11 semi-structured interviews with practitioners working on algorithmic systems in the Dutch public sector, at local and national levels. With qualitative coding analysis, we identify key elements of (...) the communication processes that underlie fairness-related human decisions. More specifically, we analyze the division of roles and tasks, the required skills, and the challenges perceived by diverse stakeholders. Three general patterns emerge from the coding analysis: (1) Policymakers, civil servants, and domain experts are less involved compared to developers throughout a system’s life-cycle. This leads to developers taking on the role of decision-maker and policy advisor, while they potentially miss the required skills. (2) End-users and policy-makers often lack the technical skills to interpret a system’s output, and rely on actors having a developer role for making decisions concerning fairness issues. (3) Citizens are structurally absent throughout a system’s life-cycle. This may lead to unbalanced fairness assessments that do not include key input from relevant stakeholders. We formalize the underlying communication issues within such networks of stakeholders and introduce the phase-actor-role-task-skill (PARTS) model. PARTS can both (i) represent the communication patterns identified in the interviews, and (ii) explicitly outline missing elements in communication patterns such as actors who miss skills or collaborators for their tasks, or tasks that miss qualified actors. The PARTS model can be extended to other use cases and used to analyze and design the human organizations responsible for assessing fairness in algorithmic systems. It can be further extended to explore communication issues in other use cases, design potential solutions, and organize accountability with a common vocabulary. (shrink)

The existential risk posed by ubiquitous artificial intelligence (AI) is a subject of frequent discussion with descriptions of the prospect of misuse, the fear of mass destruction, and the singularity. In this paper I address an under-explored category of existential risk posed by AI, namely emotional risk. Values are a main source of emotions. By challenging some of our most essential values, AI systems are therefore likely to expose us to emotional risks such as loss of care and loss (...) of meaning. Part one presents a study of a leading bank in Germany where an AI system was implemented to replace humans in decision-making processes. Part two explains why humans actively make use of values to make decisions. Part three shows the connection between values and emotions. Part four relates parts two and three to the bank study by giving concrete examples of how the employees saw their roles as workers, and how the relationships to their customers changed emotionally after the AI system was implemented to make decisions. (shrink)

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

Artificial intelligence (AI) plays a rapidly increasing role in clinical care. Many of these systems, for instance, deep learning-based applications using multilayered Artificial Neural Nets, exhibit epistemic opacity in the sense that they preclude comprehensive human understanding. In consequence, voices from industry, policymakers, and research have suggested trust as an attitude for engaging with clinical AI systems. Yet, in the philosophical and ethical literature on medical AI, the notion of trust remains fiercely debated. Trust skeptics hold that talking about trust (...) in nonhuman agents constitutes a category error and worry about the concept being misused for ethics washing. Proponents of trust have responded to these worries from various angles, disentangling different concepts and aspects of trust in AI, potentially organized in layers or dimensions. Given the substantial disagreements across these accounts of trust and the important worries about ethics washing, we embrace a diverging strategy here. Instead of aiming for a positive definition of the elements and nature of trust in AI, we proceed ex negativo, that is we look at cases where trust or distrust are misplaced. Comparing these instances with trust expedited in doctor–patient relationships, we systematize these instances and propose a taxonomy of both misplaced trust and distrust. By inverting the perspective and focusing on negative examples, we develop an account that provides useful ethical constraints for decisions in clinical as well as regulatory contexts and that highlights how we should not engage with medical AI. (shrink)

In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that do (...) not consider multiple stakeholders or the broader social context. In this article, we present an auditing framework to guide the ethical assessment of an algorithm. The audit instrument itself is comprised of three elements: a list of possible interests of stakeholders affected by the algorithm, an assessment of metrics that describe key ethically salient features of the algorithm, and a relevancy matrix that connects the assessed metrics to stakeholder interests. The proposed audit instrument yields an ethical evaluation of an algorithm that could be used by regulators and others interested in doing due diligence, while paying careful attention to the complex societal context within which the algorithm is deployed. (shrink)

As artificial intelligence (AI) continues to proliferate into every area of modern life, there is no doubt that society has to think deeply about the potential impact, whether negative or positive, that it will have. Whilst scholars recognise that AI can usher in a new era of personal, social and economic prosperity, they also warn of the potential for it to be misused towards the detriment of society. Deliberate strategies are therefore required to ensure that AI can be safely integrated (...) into society in a manner that would maximise the good for as many people as possible, whilst minimising the bad. One of the most urgent societal expectations of artificial agents is the need for them to behave in a manner that is morally relevant, i.e. to become artificial moral agents (AMAs). In this article, I will argue that exemplarism, an ethical theory based on virtue ethics, can be employed in the building of computationally rational AMAs with weak machine ethics. I further argue that three features of exemplarism, namely grounding in moral exemplars, meeting community expectations and practical simplicity, are crucial to its uniqueness and suitability for application in building AMAs that fit the ethos of AI4SG. (shrink)

Artificial intelligence-based (AI) technologies such as machine learning (ML) systems are playing an increasingly relevant role in medicine and healthcare, bringing about novel ethical and epistemological issues that need to be timely addressed. Even though ethical questions connected to epistemic concerns have been at the center of the debate, it is going unnoticed how epistemic forms of injustice can be ML-induced, specifically in healthcare. I analyze the shortcomings of an ML system currently deployed in the USA to predict patients’ likelihood (...) of opioid addiction and misuse (PDMP algorithmic platforms). Drawing on this analysis, I aim to show that the wrong inflicted on epistemic agents involved in and affected by these systems’ decision-making processes can be captured through the lenses of Miranda Fricker’s account of hermeneutical injustice. I further argue that ML-induced hermeneutical injustice is particularly harmful due to what I define as an automated hermeneutical appropriation from the side of the ML system. The latter occurs if the ML system establishes meanings and shared hermeneutical resources without allowing for human oversight, impairing understanding and communication practices among stakeholders involved in medical decision-making. Furthermore and very much crucially, an automated hermeneutical appropriation can be recognized if physicians are strongly limited in their possibilities to safeguard patients from ML-induced hermeneutical injustice. Overall, my paper should expand the analysis of ethical issues raised by ML systems that are to be considered epistemic in nature, thus contributing to bridging the gap between these two dimensions in the ongoing debate. (shrink)

Artificial intelligence (AI) is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. Artificial intelligence can be both a blessing and a curse, and potentially a double-edged sword if not carefully wielded. While it holds massive potential benefits to humans—particularly in healthcare by assisting in treatment of diseases, surgeries, record keeping, and easing the lives of both patients and doctors, its misuse has potential for harm through impact of biases, unemployment, breaches (...) of privacy, and lack of accountability to mention a few. In this article, we discuss the fourth industrial revolution, through a focus on the core of this phenomenon, artificial intelligence. We outline what the fourth industrial revolution is, its basis around AI, and how this infiltrates human lives and society, akin to a transcendence. We focus on the potential dangers of AI and the ethical concerns it brings about particularly in developing countries in general and conflict zones in particular, and we offer potential solutions to such dangers. While we acknowledge the importance and potential of AI, we also call for cautious reservations before plunging straight into the exciting world of the future, one which we long have heard of only in science fiction movies. (shrink)

‘Disruptive technologies’ is a euphemism for new technologies released lacking adequate regulation, causing significant unemployment and costly, inefficient additional labour. So it stands with LLMs. They output lookalikes of authored writing. Most output remixes existing materials, effectively stealing, since lacking understanding and intention original meaning is not added. LLMs enable low-cost, high-reward dishonesty. Students attempt to submit these products as their own texts. Some in education propose to use LLMs to allow students to generate text and then revise it. This (...) is feasible in groups small enough to ensure that students can be monitored continuously and substantially rewrite the texts while reflecting on the editing process. However, such highlighting of what is important about rewriting tasks reveals that LLMs do not add value to such activities. The recommended policy is to restrict and regulate LLMs. This protects jobs; it ensures that students have to develop skills that enhance their agency and their character development; and alleviates the problem of massive scale intellectual property misuse. Resistance to AI is urgent: no company promoting it will back down from doing so, no matter the risks of their products. This is a case of a game where one agent will never cooperate, and both agents endorse incompatible value sets. Consequently, no group potentially adversely affected should have qualms about intense opposition and has a duty to lobby for regulation and conditional supervised rollout of LLMs. (shrink)

Machine learning, and more broadly artificial intelligence (AI), is a fascinating technology and can be considered as the closest approximation to the Cartesian “thinking thing” that humans have ever created. Just as the industrial revolution required a new ethos, the age of intelligent machines will create its own, challenging the established moral, economic, and political presuppositions. This paper discusses the relationship between AI and society; it presents several thought experiments to explore the complexity of the relationship and highlights the insufficiency (...) of the current normative paradigm in addressing technological expansion. I argue that many of the externalities, such as security risks, loss of privacy, and economic instability will result from trying to fit the emerging technologies into the existing frame of efficiency and utility, by redefining the notions of human value, identity, autonomy, purity, and truth, among others. The age of the intelligent machine is elevating alienation to new levels, treating the individual as mere patterns in data—its primary commodity. I further argue that while the possibility of unintended consequences, due to the potential misuse of AI is ever present, the intelligent machine per se is unlikely to engage in a zero-sum game for power on its own initiative. I question whether singularity is at all attainable and argue that technology will forever remain a proxy for human interests. I conclude by posing questions for charting the path forward. Through this analysis, I aim to provide a more nuanced understanding of the complex relationship between the AI and humans. (shrink)

In informationally mature societies, almost all organisations record, generate, process, use, share and disseminate data. In particular, the rise of AI and autonomous systems has corresponded to an improvement in computational power and in solving complex problems. However, the resulting possibilities have been coupled with an upsurge of ethical risks. To avoid the misuse, underuse, and harmful use of data and data-based systems like AI, we should use an ethical framework appropriate to the object of its reasoning. Unfortunately, in (...) recent years, the space for data-related ethics has not been precisely defined in organisations. As a consequence, there has been an overlapping of responsibilities and a void of clear accountabilities. Ethical issues have, therefore, been dealt with using inadequate levels of abstraction (e.g. legal, technical). Yet, if building an ethical infrastructure requires the collaboration of each body, addressing ethical issues related to data requires leaving room for the appropriate level of abstraction. This paper first aims to show how the space of data ethics is already latent in organisations. It then highlights how to redefine roles (chief data ethics officer, data ethics committee, etc.) and codes (code of data ethics) to create and maintain an environment where ethical reasoning about data, information, and AI systems may flourish. (shrink)

Fairness in machine learning (ML) is an ever-growing field of research due to the manifold potential for harm from algorithmic discrimination. To prevent such harm, a large body of literature develops new approaches to quantify fairness. Here, we investigate how one can divert the quantification of fairness by describing a practice we call “fairness hacking” for the purpose of shrouding unfairness in algorithms. This impacts end-users who rely on learning algorithms, as well as the broader community interested in fair AI (...) practices. We introduce two different categories of fairness hacking in reference to the established concept of p-hacking. The first category, intra-metric fairness hacking, describes the misuse of a particular metric by adding or removing sensitive attributes from the analysis. In this context, countermeasures that have been developed to prevent or reduce p-hacking can be applied to similarly prevent or reduce fairness hacking. The second category of fairness hacking is inter-metric fairness hacking. Inter-metric fairness hacking is the search for a specific fair metric with given attributes. We argue that countermeasures to prevent or reduce inter-metric fairness hacking are still in their infancy. Finally, we demonstrate both types of fairness hacking using real datasets. Our paper intends to serve as a guidance for discussions within the fair ML community to prevent or reduce the misuse of fairness metrics, and thus reduce overall harm from ML applications. (shrink)

Where there is data there are questions of ownership, leaks, and worries about misuse. When what’s at stake is data on our brains, the stakes are high. This book brings together philosophical analysis and neuroscientific insights to develop an account of ‘brain data’: what it is, how it is used, and how we ought to take care of it. Emerging trends in neuroscience appear to make mental activity legible, through sophisticated processing of signals recorded from the brain. This can (...) include Artificial Intelligence (AI), with algorithms classifying brain signals for further processing. These developments will have ramifications for concepts of the brain, the self, and the mind. They will also affect clinical practices like psychiatry, by modifying concepts of mental health and introducing AI-based diagnostic and treatment strategies. The issues arising are vastly complicated, little understood, but of high importance. Philosophical Perspectives on Brain Data clarifies complex intersections of philosophical and neuroscientific interest, presenting an account of brain data that is comprehensible. This account can be the basis for evaluating practices based on brain data. As such, the book aims to open a novel space for evaluating hitherto arcane areas of academic research in order to provide the necessary scope for understanding their real-world consequences. These consequences will include personal, socio-political, and public health dimensions. It is therefore vital that they are understood if their impacts upon aspects of everyday life can be evaluated adequately. (shrink)

This paper will analyze how and to what extent terrorists and violent extremistshave interacted with generative artificial intelligence so far and identify potentialways in which they could misuse generative AI in the future. It is therefore necessaryto question assumptions that terrorist and violent extremist actors will quickly adoptgenerative AI solely based on an assessment of the capabilities it may offer. Althoughresearch has shown that technological capability and availability are key drivers of terroristinnovation, terrorist and violent extremist actors also assess (...) any new technologyon its compatibility (with both their modus operandi and ideology), relative complexity,cost, and the context in which they are operating. Understanding these factors, both inisolation and in relation to one another, will be a key aspect in monitoring the extent towhich terrorists and violent extremists adopt generative AI tools. In doing so, the paperwill seek to identify a likely trajectory for the abuse of this technology by terrorist actorsas well as conclude with some initial recommendations for policymakers. (shrink)

This paper investigates the importance of standardising and maintaining the transparency of advanced driver-assistance systems (ADAS) functions nomenclature, designs, and operations in all categories up until fully autonomous vehicles. The aim of this paper is to reveal the discrepancies in ADAS functions across automakers and discuss the underlying issues and potential solutions. In this pilot study, user manuals of various brands are reviewed systematically and critical analyses of common ADAS functions are conducted. The result shows that terminologies used to describe (...) ADAS functions vary widely across manufacturers and sometimes do not reflect their fundamental functions intuitively. Operational conditions and control procedures also vary across the selected models under this study. Due to this lack of consensus across the industry, drivers are not aware or well informed about ADAS functions in their vehicles, leading to a very low utilization rate and may lead to misuse of those functions. This paper provides insightful suggestions for the transport industry, Artificial Intelligence (AI) experts, and regulators to design frameworks and guidelines in governing the naming convention, operating conditions, control procedures, and information disclosure of ADAS. Such guidelines can be the foundations for regulating future AI-based self-driving functions. (shrink)

Technology companies are increasingly being asked to take responsibility for the technologies they create. Many of them are rising to the challenge. One way they do this is by implementing “guardrails”: restrictions on functionality that prevent people from misusing their technologies (per some standard of misuse). While there can be excellent reasons for implementing guardrails (and doing so is sometimes morally obligatory), I argue that the unrestricted authority to implement guardrails is incompatible with proper respect for user freedom, and (...) is not something we should welcome. I argue instead that guardrails should be implemented for only two reasons: to prevent accidental misuse of the technology, and as a proportionate means of preventing people from using the technology to violate other people’s rights. If I’m right, then we may have to get more comfortable with developers releasing technologies that can, and to some extent inevitably will, be misused; people using technologies in ways we disagree with is one of the costs of liberty, but it is a cost we have excellent reasons to bear. (shrink)

The purpose of this book, originally published in 1987, was to contribute to the advance of artificial intelligence by clarifying and removing the major sources of philosophical confusion at the time which continued to preoccupy scientists and thereby impede research. Unlike the vast majority of philosophical critiques of AI, however, each of the authors in this volume has made a serious attempt to come to terms with the scientific theories that have been developed, rather than attacking superficial 'straw men' which (...) bear scant resemblance to the complex theories that have been developed. For each is convinced that the philosopher's responsibility is to contribute from his own special intellectual point of view to the progress of such an important field, rather than sitting in lofty judgement dismissing the efforts of their scientific peers. The aim of this book is thus to correct some of the common misunderstandings of its subject. The technical term Artificial Intelligence has created considerable unnecessary confusion because of the ordinary meanings associated with it, and for that very reason, the term is endlessly misused and abused. The essays collected here all aim to expound the true nature of AI, and to remove the ill-conceived philosophical discussions which seek answers to the wrong questions in the wrong ways. Philosophical discussions and decisions about the proper use of AI need to be based on a proper understanding of the manner in which AI-scientists achieve their results; in particular, in their dependence on the initial planning input of human beings. The collection combines the Anglo-Saxon school of analytical philosophy with scientific and psychological methods of investigation. The distinguished authors in this volume represent a cross-section of philosophers, psychologists, and computer scientists from all over the world. The result is a fascinating study in the nature and future of AI, written in a style which is certain to appeal and inform laymen and specialists alike. has created considerable unnecessary confusion because of the ordinary meanings associated with it, and for that very reason, the term is endlessly misused and abused. The essays collected here all aim to expound the true nature of AI, and to remove the ill-conceived philosophical discussions which seek answers to the wrong questions in the wrong ways. Philosophical discussions and decisions about the proper use of AI need to be based on a proper understanding of the manner in which AI-scientists achieve their results; in particular, in their dependence on the initial planning input of human beings. The collection combines the Anglo-Saxon school of analytical philosophy with scientific and psychological methods of investigation. The distinguished authors in this volume represent a cross-section of philosophers, psychologists, and computer scientists from all over the world. The result is a fascinating study in the nature and future of AI, written in a style which is certain to appeal and inform laymen and specialists alike. inform laymen and specialists alike. (shrink)

This study analyzes predominant themes and disciplinary and methodological trends in academic integrity and misconduct research. It utilizes bibliometric analysis to explore prevalent themes and interdisciplinary intersections within discussions based on Scopus metadata. R Studio, which uses _biblioshiny_ software, is employed to visualize trends. The results indicate the presence of 769 final documents (627 on academic integrity and 142 on academic misconduct) related to the research focus up to 2023. Visual representations show complex relationships and theme changes. The analysis uncovers (...) connections between academic integrity and misconduct, emphasizing criteria such as plagiarism and the misuse of artificial intelligence (AI) tools. Distinct thematic clusters emerge, showcasing diverse dimensions and the impact of AI on misconduct. Interconnected research endeavors underscore dominant themes like cheating and ethical considerations. Thematic evolution reflects shifts from integrity-misconduct dichotomies to emergent issues like online fraud. Disciplinary contributions highlight the interdisciplinary nature of the discourse, drawing insights from social sciences, medicine, arts, business, psychology, and computer sciences. The detailed systematic literature review and survey design, which are dominant in research on academic integrity and misconduct, help build audience confidence. Some insights into academic integrity and misconduct via typical categories or terms have also been illustrated for the insightful reader. (shrink)

Some of the concerns people have about AI are: its misuses, effect on unemployment, and its potential for dehumanising. Contrary to what most people believe and fear, AI can lead to respect for the enormous power and complexity of the human mind. It is potentially very dangerous for users in the public domain to impute much more inferential power to computer systems, which look common-sensical, than they actually have. No matter how impressive AI programs may be, we must be aware (...) of their limitations and should not abrogate human responsibility to such programs. (shrink)

Certain research strands can yield “forbidden knowledge”. This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics research. This paper makes the case for transferring this discourse to machine learning research. Some machine learning applications can very easily be misused and unfold harmful consequences, for instance, with regard to generative video or text synthesis, (...) personality analysis, behavior manipulation, software vulnerability detection and the like. Up till now, the machine learning research community embraces the idea of open access. However, this is opposed to precautionary efforts to prevent the malicious use of machine learning applications. Information about or from such applications may, if improperly disclosed, cause harm to people, organizations or whole societies. Hence, the goal of this work is to outline deliberations on how to deal with questions concerning the dissemination of such information. It proposes a tentative ethical framework for the machine learning community on how to deal with forbidden knowledge and dual-use applications. (shrink)

In the medieval legend, Doctor Faustus strikes a dark deal with the devil; he obtains vast powers for a limited time in exchange for a priceless possession, his eternal soul. The cautionary tale, perhaps more than ever, provides a provocative lens for examining humankind’s condition, notably its indefatigable faith in knowledge and technology and its predilection toward misusing both. A variety of important questions are raised in this meditation including What is the nature of knowledge today and how does it (...) differ from knowledge in prior times? What is its relation to technology and power? What paths are we heading along and which alternative ones are being avoided? Not insignificantly, we also raise the issue of civic ignorance, including that which is intentionally cultivated and that which is simply a lack of knowledge. We also consider the identity of Doctor Faustus in the twenty-first century and in a more material world like ours, what is the soul that he would lose in the bargain, and what damage might be done to Faustus and to innocent bystanders. Finally since people don’t always live up to the terms of agreements they make, what, if anything, could Faustus do to wriggle out of the bargain, to avoid the loss of his all-important soul. Our response is not to disavow knowledge (as the implicit “lesson” of the original myth might suggest) but to shift to another approach to knowledge that is more collective and more responsive to actual needs of our era. This approach which we call civic intelligence is considered as a way to avoid the possible catastrophes that the Faustian bargain we’ve seemingly struck is likely to bring. (shrink)

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

There is a need recognized by the National Institute of Dental & Craniofacial Research and the National Cancer Institute to advance basic, translational and clinical saliva research. The goal of the Salivaomics Knowledge Base (SKB) is to create a data management system and web resource constructed to support human salivaomics research. To maximize the utility of the SKB for retrieval, integration and analysis of data, we have developed the Saliva Ontology and SDxMart. This article reviews the informatics advances in saliva (...) diagnostics made possible by the Saliva Ontology and SDxMart. (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)

We describe the rationale for an application ontology covering the domain of human body fluids that is designed to facilitate representation, reuse, sharing and integration of diagnostic, physiological, and biochemical data, We briefly review the Blood Ontology (BLO), Saliva Ontology (SALO) and Kidney and Urinary Pathway Ontology (KUPO) initiatives. We discuss the methods employed in each, and address the project of using them as starting point for a unified body fluids ontology resource. We conclude with a description of how the (...) body fluids ontology initiative may provide support to basic and translational science. (shrink)

本书包括:最早的军事智慧之光及其衰落—中国古代近代军事思想;他山之玉和真理源头—外国军事思想;人类最伟大的军事战略—毛泽东军事思想;现代化、正规化革命军队的利器—邓小平新时期军队建设思想;新世纪富国强 兵的行动指南—江泽民国防和军队建设思想五章内容。.