The EU Artificial Intelligence Act (AIA) defines four risk categories for AI systems: unacceptable, high, limited, and minimal. However, it lacks a clear methodology for the assessment of these risks in concrete situations. Risks are broadly categorized based on the application areas of AI systems and ambiguous risk factors. This paper suggests a methodology for assessing AI risk magnitudes, focusing on the construction of real-world risk scenarios. To this scope, we propose to integrate the AIA with (...) a framework developed by the Intergovernmental Panel on Climate Change (IPCC) reports and related literature. This approach enables a nuanced analysis of AI risk by exploring the interplay between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We further refine the proposed methodology by applying a proportionality test to balance the competing values involved in AI risk assessment. Finally, we present three uses of this approach under the AIA: to implement the Regulation, to assess the significance of risks, and to develop internal risk management systems for AI deployers. (shrink)

A growing number of decision theorists have, in recent years, defended the view that rationality is permissive under risk: Different rational agents may be more or less risk-averse or risk-inclined. This can result in them making different choices under risk even if they value outcomes in exactly the same way. One pressing question that arises once we grant such permissiveness is what attitude to risk we should implement when choosing on behalf of other people. Are (...) we permitted to implement any of the rationally permissible risk attitudes, is there some specific risk attitude that is required when choosing for others, or are we required to defer to the risk attitudes of the people on whose behalf we are choosing? This article elaborates on this question, explains its wider practical and theoretical significance, provides an overview of existing answers, and explores how to go about providing a more systematic account of how to choose on behalf of others in risky contexts. (shrink)

A natural view in distributive ethics is that everyone's interests matter, but the interests of the relatively worse off matter more than the interests of the relatively better off. I provide a new argument for this view. The argument takes as its starting point the proposal, due to Harsanyi and Rawls, that facts about distributive ethics are discerned from individual preferences in the "original position." I draw on recent work in decision theory, along with an intuitive principle about risk-taking, (...) to derive the view. (shrink)

The paper explores how the framework of systemic risks can help govern the risks imposed through solar climate engineering research. The central argument is that a systemic perspective of risk is a useful tool for analysing and assessing the risks imposed through Stratospheric Aerosol Injection (SAI) research. SAI is a form of climate engineering that could cool the planet by enhancing its albedo through the injection of aerosols into the stratosphere. Researching such a technology creates systemic risks with a (...) strong sociotechnical component. This component consists of the potential societal harm that a developing or new technology might cause to existing norms, values, institutions, and politics. The systemic risk framework is a valuable heuristic for this case, given the complex interdependencies of societal systems, infrastructures, markets, etc. At the same time, the systemic risk framework can be enhanced through the inclusion of a more robust and reflected ethical considerations on technological risks. Consequently, this article seeks to supplement the systemic risk governance framework with insights from technology ethics. Specifically, the paper offers an ethically reflective conception of societal value dynamism and stakeholder engagement and participation, tying it to existing systemic risk governance approaches. (shrink)

When our choice affects some other person and the outcome is unknown, it has been argued that we should defer to their risk attitude, if known, or else default to use of a risk avoidant risk function. This, in turn, has been claimed to require the use of a risk avoidant risk function when making decisions that primarily affect future people, and to decrease the desirability of efforts to prevent human extinction, owing to the significant (...) risks associated with continued human survival. I raise objections to the claim that respect for others’ risk attitudes requires risk avoidance when choosing for future generations. In particular, I argue that there is no known principle of interpersonal aggregation that yields acceptable results in variable population contexts and is consistent with a plausible ideal of respect for others’ risk attitudes in fixed population cases. GPI Working Paper No . 20-2022. (shrink)

I have claimed that risk-weighted expected utility maximizers are rational, and that their preferences cannot be captured by expected utility theory. Richard Pettigrew and Rachael Briggs have recently challenged these claims. Both authors argue that only EU-maximizers are rational. In addition, Pettigrew argues that the preferences of REU-maximizers can indeed be captured by EU theory, and Briggs argues that REU-maximizers lose a valuable tool for simplifying their decision problems. I hold that their arguments do not succeed and that my (...) original claims still stand. However, their arguments do highlight some costs of REU theory. (shrink)

This paper examines a recent argument in favour of strong precautionary action—possibly including working to hasten human extinction—on the basis of a decision-theoretic view that accommodates the risk-attitudes of all affected while giving more weight to the more risk-averse attitudes. First, we dispute the need to take into account other people’s attitudes towards risk at all. Second we argue that a version of the non-identity problem undermines the case for doing so in the context of future people. (...) Lastly, we suggest that we should not work to hasten human extinction, even if significant risk aversion is warranted. (shrink)

Abstract: This research paper presents a novel approach to predict audit risks using a neural network model. The dataset used for this study was obtained from Kaggle and comprises 774 samples with 18 features, including Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, History_score, score, and Risk. The proposed neural network architecture consists of three layers, including one input layer, one hidden layer, and one output layer. The neural network model was trained and validated, (...) achieving an impressive accuracy of 100% and an average error of 0.000015, indicating its robust predictive capability. Moreover, we conducted feature importance analysis to identify the most influential features for predicting audit risk. The key features found to be critical for classifying fraudulent activities in audit risk prediction are Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, and History_score. This research contributes to the field of audit risk prediction by demonstrating the effectiveness of a neural network-based approach and highlighting the importance of specific features in detecting fraudulent activities. The findings have significant implications for auditors and organizations seeking to enhance their audit risk assessment processes, ultimately leading to improved financial transparency and fraud detection. (shrink)

There has been a movement aiming to teach agents about their privilege by making the information about their privilege as costless as possible. However, some argue that in risk-sensitive frameworks, such as Lara Buchak’s (2013), it can be rational for privileged agents to shield themselves from learning about their privilege, even if the information is costless and relevant. This threatens the efficacy of these information-access efforts in alleviating the problem of elite-group ignorance. In response, I show that even within (...) the same framework, in this case David Kinney and Liam Kofi Bright’s (2021), the rationality of this information avoidance rests on shaky ground in practice. In this framework, whether an agent should avoid information depends on the precise details of (1) how relevant they expect the information to be, (2) their priors about the value of various options, and (3) their risk attitudes. The model suggests that the rationality of elite-group ignorance is a function of structural factors that are pervasive but nonetheless not insurmountable, thus offering a way out of pessimism about elite group education. (shrink)

This article argues that Lara Buchak’s risk-weighted expected utility (REU) theory fails to offer a true alternative to expected utility theory. Under commonly held assumptions about dynamic choice and the framing of decision problems, rational agents are guided by their attitudes to temporally extended courses of action. If so, REU theory makes approximately the same recommendations as expected utility theory. Being more permissive about dynamic choice or framing, however, undermines the theory’s claim to capturing a steady choice disposition in (...) the face of risk. I argue that this poses a challenge to alternatives to expected utility theory more generally. (shrink)

A small but growing number of studies have aimed to understand, assess and reduce existential risks, or risks that threaten the continued existence of mankind. However, most attention has been focused on known and tangible risks. This paper proposes a heuristic for reducing the risk of black swan extinction events. These events are, as the name suggests, stochastic and unforeseen when they happen. Decision theory based on a fixed model of possible outcomes cannot properly deal with this kind of (...) event. Neither can probabilistic risk analysis. This paper will argue that the approach that is referred to as engineering safety could be applied to reducing the risk from black swan extinction events. It will also propose a conceptual sketch of how such a strategy may be implemented: isolated, self-sufficient, and continuously manned underground refuges. Some characteristics of such refuges are also described, in particular the psychosocial aspects. Furthermore, it is argued that this implementation of the engineering safety strategy safety barriers would be effective and plausible and could reduce the risk of an extinction event in a wide range of possible scenarios. Considering the staggering opportunity cost of an existential catastrophe, such strategies ought to be explored more vigorously. (shrink)

Because of advanced automation, data exchange, and cyber-physical systems, traditional project risk management strategies are losing their effectiveness in the era of Industry 4.0. This study investigates how integrating Industry 4.0 technology into project risk management frameworks can have real-world implications. Using technologies like artificial intelligence (AI), big data analytics, and the Internet of Things (IoT), it proposes a new framework that enhances risk identification, assessment, and mitigation methods. In order to demonstrate how Industry 4.0 is transforming (...) risk management practices, this article examines real-world case studies and industry implementations. The framework addresses significant opportunities and challenges to provide practitioners with useful insights for adapting to the evolving technological context. The findings suggest that incorporating Industry 4.0 technologies can significantly improve risk management efficiency and project outcomes, offering a pathway for organizations to navigate the complexities of modern project environments. (shrink)

With the ascent of modern epidemiology in the Twentieth Century came a new standard model of prediction in public health and clinical medicine. In this article, we describe the structure of the model. The standard model uses epidemiological measures-most commonly, risk measures-to predict outcomes (prognosis) and effect sizes (treatment) in a patient population that can then be transformed into probabilities for individual patients. In the first step, a risk measure in a study population is generalized or extrapolated to (...) a target population. In the second step, the risk measure is particularized or transformed to yield probabilistic information relevant to a patient from the target population. Hence, we call the approach the Risk Generalization-Particularization (Risk GP) Model. There are serious problems at both stages, especially with the extent to which the required assumptions will hold and the extent to which we have evidence for the assumptions. Given that there are other models of prediction that use different assumptions, we should not inflexibly commit ourselves to one standard model. Instead, model pluralism should be standard in medical prediction. (shrink)

This book examines the moral luck paradox, relating it to Kantian, consequentialist and virtue-based approaches to ethics. It also applies the paradox to areas in medical ethics, including allocation of scarce medical resources, informed consent to treatment, withholding life-sustaining treatment, psychiatry, reproductive ethics, genetic testing and medical research. If risk and luck are taken seriously, it might seem to follow that we cannot develop any definite moral standards, that we are doomed to moral relativism. However, Dickenson offers strong counter-arguments (...) to this view that enable us to think in terms of universal standards. (shrink)

In addition to protecting agents’ autonomy, consent plays a crucial social role: it enables agents to secure partners in valuable interactions that would be prohibitively morally risk otherwise. To do this, consent must be observable: agents must be able to track the facts about whether they have received a consent-based permission. I argue that this morally justifies a consent-practice on which communicating that one consents is sufficient for consent, but also generates robust constraints on what sorts of behaviors can (...) be taken as consent- communicating. (shrink)

It is widely held that the influence of risk on rational decisions is not entirely explained by the shape of an agent’s utility curve. Buchak (Erkenntnis, 2013, Risk and rationality, Oxford University Press, Oxford, in press) presents an axiomatic decision theory, risk-weighted expected utility theory (REU), in which decision weights are the agent’s subjective probabilities modified by his risk-function r. REU is briefly described, and the global applicability of r is discussed. Rabin’s (Econometrica 68:1281–1292, 2000) calibration (...) theorem strongly suggests that plausible levels of risk aversion cannot be fully explained by concave utility functions; this provides motivation for REU and other theories. But applied to the synchronic preferences of an individual agent, Rabin’s result is not as problematic as it may first appear. Theories that treat outcomes as gains and losses (e.g. prospect theory and cumulative prospect theory) account for risk sensitivity in a way not available to REU. Reference points that mark the difference between gains and losses are subject to framing, many instances of which cannot be regarded as rational. However, rational decision theory may recognize the difference between gains and losses, without endorsing all ways of fixing the point of reference. In any event, REU is a very interesting theory. (shrink)

Catastrophic risk raises questions that are not only of practical importance, but also of great philosophical interest, such as how to define catastrophe and what distinguishes catastrophic outcomes from non-catastrophic ones. Catastrophic risk also raises questions about how to rationally respond to such risks. How to rationally respond arguably partly depends on the severity of the uncertainty, for instance, whether quantitative probabilistic information is available, or whether only comparative likelihood information is available, or neither type of information. Finally, (...) catastrophic risk raises important ethical questions about what to do when catastrophe avoidance conflicts with equity promotion. (shrink)

Rowe has recently argued that pure risk of harm cannot itself be a harm. I respond to Rowe and argue that given an appropriate understanding of objective probabilities, pure objective risk of harm can itself be a harm.

Risk communication has been generally categorized as a warning act, which is performed in order to prevent or minimize risk. On the other side, risk analysis has also underscored the role played by information in reducing uncertainty about risk. In both approaches the safety aspects related to the protection of the right to health are on focus. However, it seems that there are cases where a risk cannot possibly be avoided or uncertainty reduced, this is (...) for instance valid for the declaration of side effects associated with pharmaceutical products or when a decision about drug approval or retirement must be delivered on the available evidence. In these cases, risk communication seems to accomplish other tasks than preventing risk or reducing uncertainty. The present paper analyzes the legal instruments which have been developed in order to control and manage the risks related to drugs – such as the notion of “development risk” or “residual risk” – and relates them to different kinds of uncertainty. These are conceptualized as epistemic, ecological, metric, ethical, and stochastic, depending on their nature. By referring to this taxonomy, different functions of pharmaceutical risk communication are identified and connected with the legal tools of uncertainty management. The purpose is to distinguish the different functions of risk communication and make explicit their different legal nature and implications. (shrink)

The standard argument to the conclusion that artificial intelligence (AI) constitutes an existential risk for the human species uses two premises: (1) AI may reach superintelligent levels, at which point we humans lose control (the ‘singularity claim’); (2) Any level of intelligence can go along with any goal (the ‘orthogonality thesis’). We find that the singularity claim requires a notion of ‘general intelligence’, while the orthogonality thesis requires a notion of ‘instrumental intelligence’. If this interpretation is correct, they cannot (...) be joined as premises and the argument for the existential risk of AI turns out invalid. If the interpretation is incorrect and both premises use the same notion of intelligence, then at least one of the premises is false and the orthogonality thesis remains itself orthogonal to the argument to existential risk from AI. In either case, the standard argument for existential risk from AI is not sound.—Having said that, there remains a risk of instrumental AI to cause very significant damage if designed or used badly, though this is not due to superintelligence or a singularity. (shrink)

A moderately risk averse person may turn down a 50/50 gamble that either results in her winning $200 or losing $100. Such behaviour seems rational if, for instance, the pain of losing $100 is felt more strongly than the joy of winning $200. The aim of this paper is to examine an influential argument that some have interpreted as showing that such moderate risk aversion is irrational. After presenting an axiomatic argument that I take to be the strongest (...) case for the claim that moderate risk aversion is irrational, I show that it essentially depends on an assumption that those who think that risk aversion can be rational should be skeptical of. Hence, I conclude that risk aversion need not be irrational. (shrink)

In this paper we aim to demonstrate the enormous ethical complexity that is prevalent in child obesity cases. This complexity, we argue, favors a cautious approach. Against those perhaps inclined to blame neglectful parents, we argue that laying the blame for child obesity at the feet of parents is simplistic once the broader context is taken into account. We also show that parents not only enjoy important relational prerogatives worth defending, but that children, too, are beneficiaries of that relationship in (...) ways difficult to match elsewhere. Finally, against the backdrop of growing public concern and pressure to intervene earlier in the life cycle, we examine the perhaps unintended stigmatizing effects that labeling and intervention can have and consider a number of risks and potential harms occasioned by state interventions in these cases. (shrink)

Uncertainty, insufficient information or information of poor quality, limited cognitive capacity and time, along with value conflicts and ethical considerations, are all aspects thatmake risk managementand riskcommunication difficult. This paper provides a review of different risk concepts and describes how these influence risk management, communication and planning in relation to forest ecosystem services. Based on the review and results of empirical studies, we suggest that personal assessment of risk is decisive in the management of forest ecosystem (...) services. The results are used together with a reviewof different principles of the distribution of risk to propose an approach to risk communication that is effective aswell as ethically sound. Knowledge of heuristics and mutual information on both beliefs and desires are important in the proposed risk communication approach. Such knowledge provides an opportunity for relevant information exchange, so that gaps in personal knowledge maps can be filled in and effective risk communication can be promoted. (shrink)

Violence risk assessment tools are increasingly used within criminal justice and forensic psychiatry, however there is little relevant, reliable and unbiased data regarding their predictive accuracy. We argue that such data are needed to (i) prevent excessive reliance on risk assessment scores, (ii) allow matching of different risk assessment tools to different contexts of application, (iii) protect against problematic forms of discrimination and stigmatisation, and (iv) ensure that contentious demographic variables are not prematurely removed from risk (...) assessment tools. (shrink)

In an effort to inform the discussion surrounding existential risks from AI, we formulate Extinction-level Goodhart’s Law as “Virtually any goal specification, pursued to the extreme, will result in the extinction of humanity”, and we aim to understand which formal models are suitable for investigating this hypothesis. Note that we remain agnostic as to whether Extinction-level Goodhart’s Law holds or not. As our key contribution, we identify a set of conditions that are necessary for a model that aims to be (...) informative for evaluating specific arguments for Extinction-level Goodhart’s Law. Since each of the conditions seems to significantly contribute to the complexity of the resulting model, formally evaluating the hypothesis might be exceedingly difficult. This raises the possibility that whether the risk of extinction from artificial intelligence is real or not, the underlying dynamics might be invisible to current scientific methods. (shrink)

The orthodox theory of instrumental rationality, expected utility (EU) theory, severely restricts the way in which risk-considerations can figure into a rational individual's preferences. It is argued here that this is because EU theory neglects an important component of instrumental rationality. This paper presents a more general theory of decision-making, risk-weighted expected utility (REU) theory, of which expected utility maximization is a special case. According to REU theory, the weight that each outcome gets in decision-making is not the (...) subjective probability of that outcome; rather, the weight each outcome gets depends on both its subjective probability and its position in the gamble. Furthermore, the individual's utility function, her subjective probability function, and a function that measures her attitude towards risk can be separately derived from her preferences via a Representation Theorem. This theorem illuminates the role that each of these entities plays in preferences, and shows how REU theory explicates the components of instrumental rationality. (shrink)

This volume bridges contemporary philosophical conceptions of risk and responsibility and offers an extensive examination of the topic. It shows that risk and responsibility combine in ways that give rise to new philosophical questions and problems. Philosophical interest in the relationship between risk and responsibility continues to rise, due in no small part due to environmental crises, emerging technologies, legal developments, and new medical advances. Despite such interest, scholars are just now working out how to conceive of (...) the links between risk and responsibility, the implications that risks may have to conceptions of responsibility (and vice versa), as well as how such theorizing might play out in applied cases. With contributions from leading scholars, this volume brings together new work examining the interplay between risk and responsibility, exploring its varied philosophical aspects and applications to contemporary issues in law, bioethics, technology, and environmental ethics. Risk and Responsibility in Context will be of interest to philosophers working in ethics, bioethics, philosophy of law, and philosophy of technology, as well as scholars and practitioners in law, health and science management, public policy, and environmental studies. (shrink)

Which risks are bad? This is not an easy question to answer in any non-circular way. Not only are risks sought out for various reasons, but risks are plainly discounted in many situations. What may seem "risky" when examined all by itself, may not seem risky when encountered in a real lived situation. Thus risks that are imposed by others, in particular, might seem horrendous when considered in abstraction, but quite acceptable when encountered in life. What we need to do, (...) among other things, is to examine this "seeming." Are people being deluded or distracted when they fail to take seriously the riskiness of activities undertaken by others, or does this make some rational sense? There can be no doubt that people sometimes are deluded, of course. We are all fallible. But risks are not unalloyed evils. They are typically taken because of their association with something positive. So questions about whether people are making wise judgments when they don’t care about certain risks that they take, or, on the other side of the coin, when they become absolutely hysterical when confronted with a risk that appears to others to be insignificant, hinge on a web of further factors. (shrink)

International regulation of autonomous weapon systems (AWS) is increasingly conceived as an exercise in risk management. This requires a shared approach for assessing the risks of AWS. This paper presents a structured approach to risk assessment and regulation for AWS, adapting a qualitative framework inspired by the Intergovernmental Panel on Climate Change (IPCC). It examines the interactions among key risk factors—determinants, drivers, and types—to evaluate the risk magnitude of AWS and establish risk tolerance thresholds through (...) a risk matrix informed by background knowledge of event likelihood and severity. Further, it proposes a methodology to assess community risk appetite, emphasizing that such assessments and resulting tolerance levels should be determined through deliberation in a multistakeholder forum. The paper highlights the complexities of applying risk-based regulations to AWS internationally, particularly the challenge of defining a global community for risk assessment and regulatory legitimization. (shrink)

If the intelligence of artificial systems were to surpass that of humans significantly, this would constitute a significant risk for humanity. Time has come to consider these issues, and this consideration must include progress in AI as much as insights from the theory of AI. The papers in this volume try to make cautious headway in setting the problem, evaluating predictions on the future of AI, proposing ways to ensure that AI systems will be beneficial to humans – and (...) critically evaluating such proposals. (shrink)

We investigate risk attitudes when the underlying domain of payoffs is finite and the payoffs are, in general, not numerical. In such cases, the traditional notions of absolute risk attitudes, that are designed for convex domains of numerical payoffs, are not applicable. We introduce comparative notions of weak and strong risk attitudes that remain applicable. We examine how they are characterized within the rank-dependent utility model, thus including expected utility as a special case. In particular, we characterize (...) strong comparative risk aversion under rank-dependent utility. This is our main result. From this and other findings, we draw two novel conclusions. First, under expected utility, weak and strong comparative risk aversion are characterized by the same condition over finite domains. By contrast, such is not the case under non-expected utility. Second, under expected utility, weak comparative risk aversion is characterized by the same condition when the utility functions have finite range and when they have convex range. By contrast, such is not the case under non-expected utility. Thus, considering comparative risk aversion over finite domains leads to a better understanding of the divide between expected and non-expected utility, more generally, the structural properties of the main models of decision-making under risk. (shrink)

Philosophy &Public Affairs, Volume 51, Issue 4, Page 373-412, Fall 2023.

Can it be rational to be risk-averse? It seems plausible that the answer is yes—that normative decision theory should accommodate risk aversion. But there is a seemingly compelling class of arguments against our most promising methods of doing so. These long-run arguments point out that, in practice, each decision an agent makes is just one in a very long sequence of such decisions. Given this form of dynamic choice situation, and the (Strong) Law of Large Numbers, they conclude (...) that those theories which accommodate risk aversion end up delivering the same verdicts as risk-neutral theories in nearly all practical cases. If so, why not just accept a simpler, risk-neutral theory? The resulting practical verdicts seem to be much the same. In this paper, I show that these arguments do not in fact condemn those risk-aversion-accommodating theories. Risk aversion can indeed survive the long run. (shrink)

Why can testimony alone be enough for findings of liability? Why statistical evidence alone can’t? These questions underpin the “Proof Paradox” (Redmayne 2008, Enoch et al. 2012). Many epistemologists have attempted to explain this paradox from a purely epistemic perspective. I call it the “Epistemic Project”. In this paper, I take a step back from this recent trend. Stemming from considerations about the nature and role of standards of proof, I define three requirements that any successful account in line with (...) the Epistemic Project should meet. I then consider three recent epistemic accounts on which the standard is met when the evidence rules out modal risk (Pritchard 2018), normic risk (Ebert et al. 2020), or relevant alternatives (Gardiner 2019 2020). I argue that none of these accounts meets all the requirements. Finally, I offer reasons to be pessimistic about the prospects of having a successful epistemic explanation of the paradox. I suggest the discussion on the proof paradox would benefit from undergoing a ‘value-turn’. (shrink)

Special Issue “Risks of artificial general intelligence”, Journal of Experimental and Theoretical Artificial Intelligence, 26/3 (2014), ed. Vincent C. Müller. http://www.tandfonline.com/toc/teta20/26/3# - Risks of general artificial intelligence, Vincent C. Müller, pages 297-301 - Autonomous technology and the greater human good - Steve Omohundro - pages 303-315 - - - The errors, insights and lessons of famous AI predictions – and what they mean for the future - Stuart Armstrong, Kaj Sotala & Seán S. Ó hÉigeartaigh - pages 317-342 - - (...) - The path to more general artificial intelligence - Ted Goertzel - pages 343-354 - - - Limitations and risks of machine ethics - Miles Brundage - pages 355-372 - - - Utility function security in artificially intelligent agents - Roman V. Yampolskiy - pages 373-389 - - - GOLEM: towards an AGI meta-architecture enabling both goal preservation and radical self-improvement - Ben Goertzel - pages 391-403 - - - Universal empathy and ethical bias for artificial general intelligence - Alexey Potapov & Sergey Rodionov - pages 405-416 - - - Bounding the impact of AGI - András Kornai - pages 417-438 - - - Ethics of brain emulations - Anders Sandberg - pages 439-457. (shrink)

Deontological theories face difficulties in accounting for situations involving risk; the most natural ways of extending deontological principles to such situations have unpalatable consequences. In extending ethical principles to decision under risk, theorists often assume the risk must be incorporated into the theory by means of a function from the product of probability assignments to certain values. Deontologists should reject this assumption; essentially different actions are available to the agent when she cannot know that a certain act (...) is in her power, so we cannot simply understand her choice situation as a “risk-weighted” version of choice under certainty. (shrink)

Papers from the conference on AI Risk (published in JETAI), supplemented by additional work. --- If the intelligence of artificial systems were to surpass that of humans, humanity would face significant risks. The time has come to consider these issues, and this consideration must include progress in artificial intelligence (AI) as much as insights from AI theory. -- Featuring contributions from leading experts and thinkers in artificial intelligence, Risks of Artificial Intelligence is the first volume of collected chapters dedicated (...) to examining the risks of AI. The book evaluates predictions of the future of AI, proposes ways to ensure that AI systems will be beneficial to humans, and then critically evaluates such proposals. 1 Vincent C. Müller, Editorial: Risks of Artificial Intelligence - 2 Steve Omohundro, Autonomous Technology and the Greater Human Good - 3 Stuart Armstrong, Kaj Sotala and Sean O’Heigeartaigh, The Errors, Insights and Lessons of Famous AI Predictions - and What they Mean for the Future - 4 Ted Goertzel, The Path to More General Artificial Intelligence - 5 Miles Brundage, Limitations and Risks of Machine Ethics - 6 Roman Yampolskiy, Utility Function Security in Artificially Intelligent Agents - 7 Ben Goertzel, GOLEM: Toward an AGI Meta-Architecture Enabling Both Goal Preservation and Radical Self-Improvement - 8 Alexey Potapov and Sergey Rodionov, Universal Empathy and Ethical Bias for Artificial General Intelligence - 9 András Kornai, Bounding the Impact of AGI - 10 Anders Sandberg, Ethics and Impact of Brain Emulations 11 Daniel Dewey, Long-Term Strategies for Ending Existential Risk from Fast Takeoff - 12 Mark Bishop, The Singularity, or How I Learned to Stop Worrying and Love AI -. (shrink)

Under what conditions does machine learning (ML) model opacity inhibit the possibility of explaining and understanding phenomena? In this article, I argue that nonepistemic values give shape to the ML opacity problem even if we keep researcher interests fixed. Treating ML models as an instance of doing model-based science to explain and understand phenomena reveals that there is (i) an external opacity problem, where the presence of inductive risk imposes higher standards on externally validating models, and (ii) an internal (...) opacity problem, where greater inductive risk demands a higher level of transparency regarding the inferences the model makes. (shrink)

An axiom of medical research ethics is that a protocol is moral only if it has a “favorable risk-benefit ratio”. This axiom is usually interpreted in the following way: a medical research protocol is moral only if it has a positive expected value -- that is, if it is likely to do more good (to both subjects and society) than harm. I argue that, thus interpreted, the axiom has two problems. First, it is unusable, because it requires us to (...) know more about the potential outcomes of research than we ever could. Second, it is false, because it conflicts with the so-called “soft paternalist” principles of liberal democracy. In place of this flawed rule I propose a new way of making risk-benefit assessments, one that does comport with the principles of liberalism. I argue that a protocol is moral only if it would be entered into by competent subjects who are informed about the protocol. The new rule this eschews all pseudo-utilitarian calculation about the protocol’s likely harms and benefits. (shrink)

This paper aims to scrutinize how the notion of risk should be understood and applied to possibly catastrophic cases. I begin with clarifying the standard usage of the notion of risk, particularly emphasizing the conceptual relation between risk and probability. Then, I investigate how to make decisions in the case of seemingly catastrophic disasters by contrasting the precautionary principle with the preventive (prevention) principle. Finally, I examine what kind of causal thinking tends to be actually adopted when (...) we make decisions on emergent cases. My arguments are mainly based on Japan's 2011 massive earthquake and nuclear power station accident. Masaki Ichinose. (shrink)

The EU proposal for the Artificial Intelligence Act (AIA) defines four risk categories: unacceptable, high, limited, and minimal. However, as these categories statically depend on broad fields of application of AI, the risk magnitude may be wrongly estimated, and the AIA may not be enforced effectively. This problem is particularly challenging when it comes to regulating general-purpose AI (GPAI), which has versatile and often unpredictable applications. Recent amendments to the compromise text, though introducing context-specific assessments, remain insufficient. To (...) address this, we propose applying the risk categories to specific AI scenarios, rather than solely to fields of application, using a risk assessment model that integrates the AIA with the risk approach arising from the Intergovernmental Panel on Climate Change (IPCC) and related literature. This model enables the estimation of the magnitude of AI risk by considering the interaction between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We use large language models (LLMs) as an example. (shrink)

This is the editorial for a special volume of JETAI, featuring papers by Omohundro, Armstrong/Sotala/O’Heigeartaigh, T Goertzel, Brundage, Yampolskiy, B. Goertzel, Potapov/Rodinov, Kornai and Sandberg. - If the general intelligence of artificial systems were to surpass that of humans significantly, this would constitute a significant risk for humanity – so even if we estimate the probability of this event to be fairly low, it is necessary to think about it now. We need to estimate what progress we can expect, (...) what the impact of superintelligent machines might be, how we might design safe and controllable systems, and whether there are directions of research that should best be avoided or strengthened. (shrink)

This chapter addresses a general question: What is responsible risking? It explores the notion of "responsible risking" as a thick moral concept, and it argues that the notion can be given moral content that could be action-guiding and add an important tool to our moral toolbox. To impose risks responsibly, on this view, is to take on responsibility in a good way. A core part of responsible risking, this chapter argues, is some version of a Forethought Condition. Such a condition (...) requires us to not make decisions or plans such that we could not deliver on our responsibility obligations. This chapter argues that three moral obligations can be derived from descriptive notions of responsibility. They are (1) the control condition, (2) the responsibility to fix condition, and (3) the responsibility to explain condition. The idea is that in order to impose risks responsibly we ought not act in ways that undermine our ability to remain in control over the risks, undermine our ability to sort out negative outcomes by reparations or compensation, or undermine our prospects of providing an explanation for our actions that also makes sense in hindsight and to those affected. This idea is then tried out on the case of germline gene editing. It is argued that such a moral notion of responsibility is also action-guiding when we do not know for certain what is the morally right or obligatory action all-things-considered due to epistemic gaps. (shrink)

The challenges of the Anthropocene are growing ever more complex and uncertain, underpinned by the emergence of systemic risks. At the same time, the landscape of risk governance has become compartmentalised and siloed, characterized by non-overlapping activities, competing scientific discourses, and distinct responsibilities distributed across diverse public and private bodies. Operating across scales and disciplines, actors tend to work in silos which constitute critical gaps within the interface of science, policy, and practice. Yet, increasingly complex and ‘wicked’ problems require (...) holistic solutions, multi-scalar communication, coordination, collaboration, data interoperability, funding, and stakeholder engagement. To address these problems in a real-world context, we present the Risk-Tandem framework for bridging theory and practice; to guide and structure the integration of disaster risk management (DRM), climate change adaptation (CCA) and systemic risk management through a process of transdisciplinary knowledge co-production. Advancing the frontiers of knowledge in this regard, The Risk-Tandem framework combines risk management approaches and tools with iterative co-production processes as a cornerstone of its implementation, in efforts to promote the co-design of fit-for-purpose solutions, methods and approaches contributing toward strengthened risk governance alongside stakeholders. The paper outlines how the framework is developed, applied, and further refined within selected case study regions, including Denmark, Germany, Italy and the Danube Region. (shrink)

Some experts have argued that patients facing certain types of choices should not be told whether their risk is above or below average, because this information may trigger a bias (Fagerlin et al. 2007). But careful consideration shows that the comparative risk heuristic can usefully guide decisions and improve their quality or rationality. Building on an earlier paper of mine (Schwartz 2009), I will argue here that doctors and decision aids should provide comparative risk information to patients, (...) even while further research is conducted. (shrink)

According to the orthodox treatment of risk preferences in decision theory, they are to be explained in terms of the agent's desires about concrete outcomes. The orthodoxy has been criticised both for conflating two types of attitudes and for committing agents to attitudes that do not seem rationally required. To avoid these problems, it has been suggested that an agent's attitudes to risk should be captured by a risk function that is independent of her utility and probability (...) functions. The main problem with that approach is that it suggests that attitudes to risk are wholly distinct from people's (non-instrumental) desires. To overcome this problem, we develop a framework where an agent's utility function is defined over chance propositions (i.e., propositions describing objective probability distributions) as well as ordinary (non-chance) ones, and argue that one should explain different risk attitudes in terms of different forms of the utility function over such propositions. (shrink)

Abstract: This research focuses on utilizing Artificial Neural Networks (ANNs) to predict Audit Risk accurately, a critical aspect of ensuring financial system integrity and preventing fraud. Our dataset, gathered from Kaggle, comprises 18 diverse features, including financial and historical parameters, offering a comprehensive view of audit-related factors. These features encompass 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' 'History_score,' 'score,' and 'Risk,' with a total of 774 samples. Our proposed neural network architecture, consisting (...) of three layers (1 input, 1 hidden, and 1 output), forms the core of this study. While it may seem simple, the power of ANNs lies not in complexity but in their ability to uncover intricate data patterns. The model underwent rigorous training and validation, resulting in remarkable outcomes—an accuracy of 100% and an average error rate of 0.000015. In addition to performance metrics, our research investigates feature importance, revealing the key contributors to Audit Risk prediction. Notably, 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' and 'History_score' emerged as the most influential factors, shedding light on the elements crucial for precise risk assessment. This study advances Audit Risk prediction models and highlights the potential of ANNs in strengthening financial system integrity and fraud prevention. The insights gained from our findings offer practical guidance for stakeholders in the audit and financial sectors. (shrink)

The author argues for a theory of responsibility for outcomes of imposed risk, based on whether it was permissible to impose the risk. When one tries to apply this persuasive model of responsibility for outcomes of risk imposition to procreation, which is a risk imposing act, one finds that it doesn’t match one’s intuitions about responsibility for outcomes of procreative risk. This mismatch exposes a justificatory gap for procreativity, namely, that procreation cannot avail itself of (...) the shared vulnerability to risks and their constraints—to the balance one is forced to strike between one’s interest in being free to impose risks on others and one’s interest in being safe from harm resulting from the risk imposed by others—which serves to justify risk imposition, generally. Whereas most risk impositions involve trade-offs of liberty and security among people who share the vulnerabilities associated with the taking, imposing, or being constrained from imposing risks, procreation involves the introduction of people into that position of vulnerability in the first place. Thus, when one procreates, one imposes risks in the absence of the shared vulnerability that usually serves as a justification for risk imposition. Procreative risks may not be wrongfully imposed, but they aren’t permissibly imposed in a manner fully comparable to other permissibly imposed risks. This makes procreation a unique form of risk imposition, with unique implications for its justification and for one’s responsibility for its outcomes. This insight can help explain several puzzling procreative asymmetries. (shrink)

In this paper, I explore the prospects for risk-based arguments in favour of anti-natalism, which explain the wrongness of procreation in terms of wrongful risk-imposition on the resultant child. After considering and rejecting two risk-based arguments from the existing literature - David Benatar's and Matti Häyry's - I propose a more promising version that focuses on the lack of appropriate justification for imposing the risks of existence, namely, one that refers to the essential interests of the child (...) on whom those risks are imposed. The paper proceeds in four parts. In Part 2, I set the stage for my discussion by clarifying the basic structure of risk-based arguments and identifying some of their important features. In Parts 3-5, I consider three distinct risk-based arguments for anti-natalism and argue that only the third, justificatory argument has the potential to be successful. (shrink)

Rawls famously argues that the parties in the original position would agree upon the two principles of justice. Among other things, these principles guarantee equal political liberty—that is, democracy—as a requirement of justice. We argue on the contrary that the parties have reason to reject this requirement. As we show, by Rawls’ own lights, the parties would be greatly concerned to mitigate existential risk. But it is doubtful whether democracy always minimizes such risk. Indeed, no one currently knows (...) which political systems would. Consequently, the parties—and we ourselves—have reason to reject democracy as a requirement of justice in favor of political experimentalism, a general approach to political justice which rules in at least some non-democratic political systems which might minimize existential risk. (shrink)