To realize the coordinated and sustainable development of hydropower projects and regional society, comprehensively evaluating hydropower projects’ influence is critical. Usually, hydropower project development has an impact on environmental geology and social and regional cultural development. Based on comprehensive consideration of complicated geological conditions, fragile ecological environment, resettlement of reservoir area, and other factors of future hydropower development in each country, we have constructed a comprehensive evaluation index system of hydropower projects, including 4 first-level indicators of social economy, environment, (...) class='Hi'>safety, and fairness, which contain 26 second-level indicators. To solve the problem that existing models cannot evaluate dynamic nonlinear optimization, a projection pursuit model is constructed by using rough set reduction theory to simplify the index. Then, an accelerated genetic algorithm based on real number coding is used to solve the model and empirical study is carried out with the Y hydropower station as a sample. The evaluation results show that the evaluation index system and assessment model constructed in our paper effectively reduce the subjectivity of index weight. Applying our model to the social impact assessment of related international hydropower projects can not only comprehensively analyze the social impact of hydropower projects but also identify important social influencing factors and effectively analyze the social impact level of each dimension. Furthermore, SIA assessment can be conducive to project decision-making, avoiding social risks and social stability. (shrink)

One objection to conventional AI ethics is that it slows innovation. This presentation responds by reconfiguring ethics as an innovation accelerator. The critical elements develop from a contrast between Stability AI’s Diffusion and OpenAI’s Dall-E. By analyzing the divergent values underlying their opposed strategies for development and deployment, five conceptions are identified as common to acceleration ethics. Uncertainty is understood as positive and encouraging, rather than discouraging. Innovation is conceived as intrinsically valuable, instead of worthwhile only as mediated by social (...) effects. AI problems are solved by more AI, not less. Permissions and restrictions governing AI emerge from a decentralized process, instead of a unified authority. The work of ethics is embedded in AI development and application, instead of functioning from outside. Together, these attitudes and practices remake ethics as provoking rather than restraining artificial intelligence. (shrink)

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

Machine ethics and robot rights are quickly becoming hot topics in artificial intelligence and robotics communities. We will argue that attempts to attribute moral agency and assign rights to all intelligent machines are misguided, whether applied to infrahuman or superhuman AIs, as are proposals to limit the negative effects of AIs by constraining their behavior. As an alternative, we propose a new science of safety engineering for intelligent artificial agents based on maximizing for what humans value. In particular, we (...) challenge the scientific community to develop intelligent systems that have human-friendly values that they provably retain, even under recursive self-improvement. (shrink)

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

As AI technologies increase in capability and ubiquity, AI accidents are becoming more common. Based on normal accident theory, high reliability theory, and open systems theory, we create a framework for understanding the risks associated with AI applications. This framework is designed to direct attention to pertinent system properties without requiring unwieldy amounts of accuracy. In addition, we also use AI safety principles to quantify the unique risks of increased intelligence and human-like qualities in AI. Together, these two fields (...) give a more complete picture of the risks of contemporary AI. By focusing on system properties near accidents instead of seeking a root cause of accidents, we identify where attention should be paid to safety for current generation AI systems. (shrink)

This paper offers an overview of the prospects and ethics of using AI to achieve human enhancement, and more broadly what we call intellectual augmentation (IA). After explaining the central notions of human enhancement, IA, and AI, we discuss the state of the art in terms of the main technologies for IA, with or without brain-computer interfaces. Given this picture, we discuss potential ethical problems, namely inadequate performance, safety, coercion and manipulation, privacy, cognitive liberty, authenticity, and fairness in more (...) detail. We conclude that while there are very significant technical hurdles to real human enhancement through AI, and significant ethical problems, there are also significant benefits that may realistically be achieved in ways that are consonant with a rights-based ethics as well. We also highlight the specific concerns that apply particularly to applications of AI for "sheer" IA (more realistic in the near term), and to enhancement applications, respectively. (shrink)

I explain the shutdown problem: the problem of designing artificial agents that (1) shut down when a shutdown button is pressed, (2) don’t try to prevent or cause the pressing of the shutdown button, and (3) otherwise pursue goals competently. I prove three theorems that make the difficulty precise. These theorems show that agents satisfying some innocuous-seeming conditions will often try to prevent or cause the pressing of the shutdown button, even in cases where it’s costly to do so. And (...) patience trades off against shutdownability: the more patient an agent, the greater the costs that agent is willing to incur to manipulate the shutdown button. I end by noting that these theorems can guide our search for solutions. (shrink)

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

Mobility robots may soon be among us, triggering a need for safety regulation. Robot safety regulation, however, remains underexplored, with only a few articles analyzing what regulatory approaches could be feasible. This article offers an account of the available regulatory strategies and attempts to theorize the effects of simulation-based safety regulation. The article first discusses the distinctive features of mobility robots as regulatory targets and argues that emergent behavior constitutes the key regulatory concern in designing robot (...) class='Hi'>safety regulation regimes. In contrast to many accounts, the article posits that emergent behavior dynamics do not arise from robot autonomy, learning capability, or code unexplainability. Instead, they emerge from the complexity of robot technological constitutions coupled with near-infinite environmental variability and non-linear performance dynamics of the machine learning components. Second, the article reviews rules-based and performance-based regulation and argues that both will fail adequately constrain emergent robot behaviors. The article claims that controlling mobility robots requires a simulation-based regulatory approach. Simulation-based regulation is a novelty with significant theoretical and practical implications. The article argues that the approach signifies a radical break in regulatory forms of knowledge and temporalities. Simulations enact virtual futures to create a new regulatory knowledge type. Practically, the novel safety knowledge type may destabilize the existing conceptual space of safety politics and liability allocation patterns. (shrink)

The philosophical–ethical literature and the public debate on autonomous vehicles have been obsessed with ethical issues related to crashing. In this article, these discussions, including more empirical investigations, will be critically assessed. It is argued that a related and more pressing issue is questions concerning safety. For example, what should we require from autonomous vehicles when it comes to safety? What do we mean by ‘safety’? How do we measure it? In response to these questions, the article (...) will present a foundation for a continued discussion on these issues and an argument for why discussions about safety should be prioritized over ethical concerns related to crashing. (shrink)

AI research is growing rapidly raising various ethical issues related to safety, risks, and other effects widely discussed in the literature. We believe that in order to adequately address those issues and engage in a productive normative discussion it is necessary to examine key concepts and categories. One such category is anthropomorphism. It is a well-known fact that AI’s functionalities and innovations are often anthropomorphized. The general public’s anthropomorphic attitudes and some of their ethical consequences have been widely discussed (...) in the literature. However, how anthropomorphism permeates AI research itself, and what the epistemological and ethical consequences of this might be have received less attention. In this paper we explore this issue. We first set the methodological/theoretical stage, making a distinction between a normative and a conceptual approach to the issues. Next, after a brief analysis of anthropomorphism and its manifestations in the public, we explore its presence within AI research with a particular focus on brain-inspired AI. Finally, on the basis of our analysis, we identify some potential epistemological and ethical consequences of the use of anthropomorphic language and discourse within the AI research community, thus reinforcing the need of complementing the practical with a conceptual analysis. (shrink)

After 50 years, the fields of artificial intelligence and robotics capture the imagination of the general public while, at the same time, engendering a great deal of fear and skepticism. Isaac Asimov recognized this deep-seated misconception of technology and created the Three Laws of Robotics. The first part of this paper examines the underlying fear of intelligent robots, revisits Asimov’s response, and reports on some current opinions on the use of the Three Laws by practitioners. Finally, an argument against robotic (...) rebellion is made along with a call for personal responsibility and suggestions for implementing safety constraints in intelligent robots. (shrink)

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

The development and deployment of artificial intelligence (AI) is and will profoundly reshape human society, the culture and the composition of civilisations which make up human kind. All technological triggers tend to drive a hype curve which over time is realised by an output which is often unexpected, taking both pessimistic and optimistic perspectives and actions of drivers, contributors and enablers on a journey where the ultimate destination may be unclear. In this paper we hypothesise that this journey is not (...) dissimilar to the personal journey described by the Kubler-Ross change curve and illustrate this by commentary on the potential of AI for drug discovery, development and healthcare and as an enabler for deep space exploration and colonisation. Recent advances in the call for regulation to ensure development of safety measures associated with machine-based learning are presented which, together with regulation of the rapidly emerging digital after-life industry, should provide a platform for realising the full potential benefit of AI for the human species. (shrink)

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

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

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

In this study, we analyse the public perception of military AI in Estonia, a techno-optimistic country with high support for science and technology. This study involved quantitative survey data from 2021 on the public’s attitudes towards AI-based technology in general, and AI in developing and using weaponised unmanned ground systems (UGS) in particular. UGS are a technology that has been tested in militaries in recent years with the expectation of increasing effectiveness and saving manpower in dangerous military tasks. However, developing (...) weaponised systems often raises questions about their safety and accountability. While there have been public debates about the possible outcomes of AI-based civilian technologies like self-driving cars, equivalent discussions have not been held about military technologies. A discourse on potentially sensitive technologies can diminish biased representations and the distribution of worst-case scenarios. The results of this study reveal that an overall positive attitude towards AI-driven technologies is related to a positive perception of military AI systems. However, trustworthiness and human control are the main aspects that are expected from military technologies. (shrink)

A growing number of consumer technology companies are aiming to convince older people that humanoid robots make helpful tools to support aging-in-place. As hybrid devices, socially assistive robots (SARs) are situated between health monitoring tools, familiar digital assistants, security aids, and more advanced AI-powered devices. Consequently, they implicate older people’s privacy in complex ways. Such devices are marketed to perform functions common to smart speakers (e.g., Amazon Echo) and smart home platforms (e.g., Google Home), while other functions are more specific (...) to older people, including health and safety monitoring and serving as companions to mitigate loneliness. Privacy is a key value central to debates about the ethics of using SARs in aged care, yet there has been very little interchange between these debates and the robust theoretical discussion in the legal literature about the future of privacy and AI governance. Drawing on two qualitative studies of older people’s views on consumer SARs, the paper contributes novel findings about older people’s thinking on privacy and data governance at the intersection of their experiences with present day digital technologies and projections for future AI systems, and places their views in dialogue with debates about the future of privacy protection and AI governance. (shrink)

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

When is it justified to use opaque artificial intelligence (AI) output in medical decision-making? Consideration of this question is of central importance for the responsible use of opaque machine learning (ML) models, which have been shown to produce accurate and reliable diagnoses, prognoses, and treatment suggestions in medicine. In this article, I discuss the merits of two answers to the question. According to the Explanation View, clinicians must have access to an explanation of why an output was produced. According to (...) the Validation View, it is sufficient that the AI system has been validated using established standards for safety and reliability. I defend the Explanation View against two lines of criticism, and I argue that within the framework of evidence-based medicine mere validation seems insufficient for the use of AI output. I end by characterizing the epistemic responsibility of clinicians and point out how a mere AI output cannot in itself ground a practical conclusion about what to do. (shrink)

With the introduction of artificial intelligence (AI) to healthcare, there is also a need for professional guidance to support its use. New (2022) reports from National Health Service AI Lab & Health Education England focus on healthcare workers’ understanding and confidence in AI clinical decision support systems (AI-CDDSs), and are concerned with developing trust in, and the trustworthiness of these systems. While they offer guidance to aid developers and purchasers of such systems, they offer little specific guidance for the clinical (...) users who will be required to use them in patient care.This paper argues that clinical, professional and reputational safety will be risked if this deficit of professional guidance for clinical users of AI-CDDSs is not redressed. We argue it is not enough to develop training for clinical users without first establishing professional guidance regarding the rights and expectations of clinical users.We conclude with a call to action for clinical regulators: to unite to draft guidance for users of AI-CDDS that helps manage clinical, professional and reputational risks. We further suggest that this exercise offers an opportunity to address fundamental issues in the use of AI-CDDSs; regarding, for example, the fair burden of responsibility for outcomes. (shrink)

Whereas using artificial intelligence (AI) to predict natural hazards is promising, applying a predictive policing algorithm (PPA) to predict human threats to others continues to be debated. Whereas PPAs were reported to be initially successful in Germany and Japan, the killing of Black Americans by police in the US has sparked a call to dismantle AI in law enforcement. However, although PPAs may statistically associate suspects with economically disadvantaged classes and ethnic minorities, the targeted groups they aim to protect are (...) often vulnerable populations as well (e.g., victims of human trafficking, kidnapping, domestic violence, or drug abuse). Thus, determining how to enhance the benefits of PPA while reducing bias through better management is important. In this paper, we propose a policy schema to address this issue. First, after clarifying relevant concepts, we examine major criticisms of PPAs and argue that some of them should be addressed. If banning AI or making it taboo is an unrealistic solution, we must learn from our errors to improve AI. We next identify additional challenges of PPAs and offer recommendations from a policy viewpoint. We conclude that the employment of PPAs should be merged into broader governance of the social safety net and audited publicly by parliament and civic society so that the unjust social structure that breeds bias can be revised. (shrink)

With automation of routine decisions coupled with more intricate and complex information architecture operating this automation, concerns are increasing about the trustworthiness of these systems. These concerns are exacerbated by a class of artificial intelligence that uses deep learning, an algorithmic system of deep neural networks, which on the whole remain opaque or hidden from human comprehension. This situation is commonly referred to as the black box problem in AI. Without understanding how AI reaches its conclusions, it is an open (...) question to what extent we can trust these systems. The question of trust becomes more urgent as we delegate more and more decision-making to and increasingly rely on AI to safeguard significant human goods, such as security, healthcare, and safety. Models that “open the black box” by making the non-linear and complex decision process understandable by human observers are promising solutions to the black box problem in AI but are limited, at least in their current state, in their ability to make these processes less opaque to most observers. A philosophical analysis of trust will show why transparency is a necessary condition for trust and eventually for judging AI to be trustworthy. A more fruitful route for establishing trust in AI is to acknowledge that AI is situated within a socio-technical system that mediates trust, and by increasing the trustworthiness of these systems, we thereby increase trust in AI. (shrink)

The article reflects on where AI is headed and the world along with it, considering trust, ethics and safety. Implicit in artificial thinking and doomsday appraisals is the engineered divorce from reality of sublime human embodiment. Jeffrey White, Dietrich Brandt, Jan Soeffner, and Larry Stapleton, four scholars associated with AI & Society, address these issues, and more, in the following exchange.

There is no strong reason to believe that human-level intelligence represents an upper limit of the capacity of artificial intelligence, should it be realized. This poses serious safety issues, since a superintelligent system would have great power to direct the future according to its possibly flawed motivation system. Solving this issue in general has proven to be considerably harder than expected. This paper looks at one particular approach, Oracle AI. An Oracle AI is an AI that does not act (...) in the world except by answering questions. Even this narrow approach presents considerable challenges. In this paper, we analyse and critique various methods of controlling the AI. In general an Oracle AI might be safer than unrestricted AI, but still remains potentially dangerous. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Martin Heidegger and OntologyEmmanuel Levinas (bio)The prestige of Martin Heidegger 1 and the influence of his thought on German philosophy marks both a new phase and one of the high points of the phenomenological movement. Caught unawares, the traditional establishment is obliged to clarify its position on this new teaching which casts a spell over youth and which, overstepping the bounds of permissibility, is already in vogue. For once, (...) Fame has picked one who deserves it and, for that matter, one who is still living. Anyone who has studied philosophy cannot, when confronted by Heidegger’s work, fail to recognize how the originality and force of his achievements, stemming from genius, are combined with an attentive, painstaking, and close working-out of the argument—with that craftsmanship of the patient artisan in which phenomenologists take such pride. In this study, it is important for us to understand, above all, the true intentions of our author, to illuminate what he thinks really needs to be said, and to surmise what is most critical for him.To get to the heart of Heidegger’s system, it seems fitting to begin with a problem that is generally familiar. We choose the problem of knowledge, a deeper understanding of which takes us to the very threshold of Heidegger’s thought. For this problem, central to modern philosophy, is one of the main obstacles of modern philosophy that Heidegger wishes to surmount. Neo-Kantianism, which takes knowledge as the philosophical problem of the first rank, is the movement against which Heidegger rebels with all his strength. We have, thus, every chance of gaining access to his thought by the main door, so to speak. Once inside the system we will try to trace its outlines, 2 reserving, for a second part of our work, the determination of Heidegger’s place in the history of ideas, especially in the phenomenological movement, as well as of his relations with the philosopher to whom he owes so much—Edmund Husserl. 31In its most general form, the problem of the theory of knowledge has a critical significance. It consists in delineating a domain where knowledge can be certain and in determining the criteria for the legitimate scope of knowledge. But this problem, as normal and as simple as it may appear, has deeper roots. That knowledge should need a criterion at all presupposes that truth is not identical to all that is known and that the course [End Page 11] of things can fail to correspond with the course of thought. “How does knowledge correspond to being?” is a more profound formulation of the problem of knowledge.But we are not going to touch anymore on the primordial phenomenon that generates the problem. The problem of correspondence between thing and thought presupposes a free activity of thought and its isolation in relation to the object. It is precisely this presupposition which renders their harmony and even their contact problematic. “How does the subject take leave of itself to attain the object?” is what the problem of knowledge, in the last analysis, boils down to. Its true source is thus the concept of “subject” as elaborated by modern philosophy. The cogito presided over the subject’s birth. The cogito was the affirmation of the privileged nature of the subject’s immanent sphere, of its unique place in existence; hence, the cogito was the specificity of the subject’s connection to the rest of reality, the sui generis nature which opens up the passage from immanence to transcendence, the passage from ideas contained in the thinking substance to 4 their “formal existence.”The concept of the subject, understood as a substance having a specific position in the entire domain of being, presents us with difficulties of two kinds. First, how do we understand this leave-taking from the self which the thinking substance brings about and which displays an entirely original aspect? Indeed, we could say that thought, in reaching out toward objects, does not actually take leave of itself, since its objects—considered as ideas and contents of thought—are, in a certain sense, already within it. In order to make sense of this paradox, Descartes... (shrink)

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

The European Union Commission’s whitepaper on Artificial Intelligence proposes shaping the emerging AI market so that it better reflects common European values. It is a master plan that builds upon the EU AI High-Level Expert Group guidelines. This article reviews the masterplan, from a culture cycle perspective, to reflect on its potential clashes with current societal, technical, and methodological constraints. We identify two main obstacles in the implementation of this plan: the lack of a coherent EU vision to drive future (...) decision-making processes at state and local levels and the lack of methods to support a sustainable diffusion of AI in our society. The lack of a coherent vision stems from not considering societal differences across the EU member states. We suggest that these differences may lead to a fractured market and an AI crisis in which different members of the EU will adopt nation-centric strategies to exploit AI, thus preventing the development of a frictionless market as envisaged by the EU. Moreover, the Commission aims at changing the AI development culture proposing a human-centred and safety-first perspective that is not supported by methodological advancements, thus taking the risks of unforeseen social and societal impacts of AI. We discuss potential societal, technical, and methodological gaps that should be filled to avoid the risks of developing AI systems at the expense of society. Our analysis results in the recommendation that the EU regulators and policymakers consider how to complement the EC programme with rules and compensatory mechanisms to avoid market fragmentation due to local and global ambitions. Moreover, regulators should go beyond the human-centred approach establishing a research agenda seeking answers to the technical and methodological open questions regarding the development and assessment of human-AI co-action aiming for a sustainable AI diffusion in the society. (shrink)

Background Machine learning (ML) is utilized increasingly in health care, and can pose harms to patients, clinicians, health systems, and the public. In response, regulators have proposed an approach that would shift more responsibility to ML developers for mitigating potential harms. To be effective, this approach requires ML developers to recognize, accept, and act on responsibility for mitigating harms. However, little is known regarding the perspectives of developers themselves regarding their obligations to mitigate harms.Methods We conducted 40 semi-structured interviews with (...) developers of ML predictive analytics applications for health care in the United States.Results Participants varied widely in their perspectives on personal responsibility and included examples of both moral engagement and disengagement, albeit in a variety of forms. While most (70%) of participants made a statement indicative of moral engagement, most of these statements reflected an awareness of moral issues, while only a subset of these included additional elements of engagement such as recognizing responsibility, alignment with personal values, addressing conflicts of interests, and opportunities for action. Further, we identified eight distinct categories of moral disengagement reflecting efforts to minimize potential harms or deflect personal responsibility for preventing or mitigating harms.Conclusions These findings suggest possible facilitators and barriers to the development of ethical ML that could act by encouraging moral engagement or discouraging moral disengagement. Regulatory approaches that depend on the ability of ML developers to recognize, accept, and act on responsibility for mitigating harms might have limited success without education and guidance for ML developers about the extent of their responsibilities and how to implement them. (shrink)

In this paper, we explore and describe what is needed to allow connected and automated vehicles to break traffic rules in order to minimise road safety risk and to operate with appropriate transparency. Reviewing current traffic rules with particular reference to two driving situations, we illustrate why current traffic rules are not suitable for CAVs and why making new traffic rules specifically for CAVs would be inappropriate. In defining an alternative approach to achieving safe CAV driving behaviours, we describe (...) the use of ethical goal functions as part of hybrid AI systems, suggesting that functions should be defined by governmental bodies with input from citizens and stakeholders. Ethical goal functions for CAVs would enable developers to optimise driving behaviours for safety under conditions of uncertainty whilst allowing for differentiation of products according to brand values. Such functions can differ between regions according to preferences for safety behaviours within that region and can be updated over time, responding to continual socio-technological feedback loops. We conclude that defining ethical goal functions is an urgent and necessary step from governmental bodies to enable the safe and transparent operation of CAVs and accelerate the reduction in road casualties they promise to achieve. (shrink)

Human oversight has become a key mechanism for the governance of artificial intelligence (“AI”). Human overseers are supposed to increase the accuracy and safety of AI systems, uphold human values, and build trust in the technology. Empirical research suggests, however, that humans are not reliable in fulfilling their oversight tasks. They may be lacking in competence or be harmfully incentivised. This creates a challenge for human oversight to be effective. In addressing this challenge, this article aims to make three (...) contributions. First, it surveys the emerging laws of oversight, most importantly the European Union’s Artificial Intelligence Act (“AIA”). It will be shown that while the AIA is concerned with the competence of human overseers, it does not provide much guidance on how to achieve effective oversight and leaves oversight obligations for AI developers underdefined. Second, this article presents a novel taxonomy of human oversight roles, differentiated along whether human intervention is constitutive to, or corrective of a decision made or supported by an AI. The taxonomy allows to propose suggestions for improving effectiveness tailored to the type of oversight in question. Third, drawing on scholarship within democratic theory, this article formulates six normative principles which institutionalise distrust in human oversight of AI. The institutionalisation of distrust has historically been practised in democratic governance. Applied for the first time to AI governance, the principles anticipate the fallibility of human overseers and seek to mitigate them at the level of institutional design. They aim to directly increase the trustworthiness of human oversight and to indirectly inspire well-placed trust in AI governance. (shrink)

Developers are often the engine behind the creation and implementation of new technologies, including in the artificial intelligence surge that is currently underway. In many cases these new technologies introduce significant risk to affected stakeholders; risks that can be reduced and mitigated by such a dominant party. This is fully recognized by texts that analyze risks in the current AI transformation, which suggest voluntary adoption of ethical standards and imposing ethical standards via regulation and oversight as tools to compel developers (...) to reduce such risks. However, what these texts usually sidestep is the question of how aware developers are to the risks they are creating with these new AI technologies, and what their attitudes are towards such risks. This paper asks to rectify this gap in research, by analyzing an ongoing case study. Focusing on six Israeli AI startups in the field of radiology, I carry out a content analysis of their online material in order to examine these companies’ stances towards the potential threat their automated tools pose to patient safety and to the work-standing of healthcare professionals. Results show that these developers are aware of the risks their AI products pose, but tend to deny their own role in the technological transformation and dismiss or downplay the risks to stakeholders. I conclude by tying these findings back to current risk-reduction recommendations with regards to advanced AI technologies, and suggest which of them hold more promise in light of developers’ attitudes. (shrink)

Meaningful human control over AI is exalted as a key tool for assuring safety, dignity, and responsibility for AI and automated decision-systems. It is a central topic especially in fields that deal with the use of AI for decisions that could cause significant harm, like AI-enabled weapons systems. This paper argues that discussions regarding meaningful human control commonly fail to identify the purpose behind the call for meaningful human control and that stating that purpose is a necessary step in (...) deciding how best to institutionalize meaningful human control over AI. The paper identifies 5 common purposes for human control and sketches how different purpose translate into different institutional design. (shrink)

Big data and Machine learning Techniques are reshaping the way in which food safety risk assessment is conducted. The ongoing ‘datafication’ of food safety risk assessment activities and the progressive deployment of probabilistic models in their practices requires a discussion on the advantages and disadvantages of these advances. In particular, the low level of trust in EU food safety risk assessment framework highlighted in 2019 by an EU-funded survey could be exacerbated by novel methods of analysis. The (...) variety of processed data raises unique questions regarding the interplay of multiple regulatory systems alongside food safety legislation. Provisions aiming to preserve the confidentiality of data and protect personal information are juxtaposed to norms prescribing the public disclosure of scientific information. This research is intended to provide guidance for data governance and data ownership issues that unfold from the ongoing transformation of the technical and legal domains of food safety risk assessment. Following the reconstruction of technological advances in data collection and analysis and the description of recent amendments to food safety legislation, emerging concerns are discussed in light of the individual, collective and social implications of the deployment of cutting-edge Big Data collection and analysis techniques. Then, a set of principle-based recommendations is proposed by adapting high-level principles enshrined in institutional documents about Artificial Intelligence to the realm of food safety risk assessment. The proposed set of recommendations adopts Safety, Accountability, Fairness, Explainability, Transparency as core principles (SAFETY), whereas Privacy and data protection are used as a meta-principle. (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)