Michał Klincewicz

In my dissertation I critically survey existing theories of time consciousness, and draw on recent work in neuroscience and philosophy to develop an original theory. My view depends on a novel account of temporal perception based on the notion of temporal qualities, which are mental properties that are instantiated whenever we detect change in the environment. When we become aware of these temporal qualities in an appropriate way, our conscious experience will feature the distinct temporal phenomenology that is associated with the passing of time. The temporal qualities model of perception makes two predictions about the mechanisms of time perception; one that time perception is modality specific and the other that it can occur without awareness. My argument for this view partially depends on a number of psychophysical experiments that I designed and implemented myself and which investigate subjective time distortions caused by looming visual stimuli. These results show that the mechanisms of conscious experience of time are distinct from the mechanisms of time perception, as my theory of temporal qualities predicts.

In this chapter we identify three potentially morally problematic behaviours that are common among users of dating and hook-up apps (DHAs) and provide arguments as to why they may or may not be considered (a) in a category of their own, distinct from similar behaviours outside of DHAs; (b) caused or facilitated by affordances and business logic of DHAs; (c) as indeed morally wrong. We also consider ways in which morally problematic behaviours can be anticipated, mitigated, or even prevented by analysis of the ethical and moral dimensions of technologies and their afforded uses. Finally, we offer some possible directions for future work on these topics in particular and on the ethical consequences of DHAs in general.

Engineering an artificial intelligence to play an advisory role in morally charged decision making will inevitably introduce meta-ethical positions into the design. Some of these positions, by informing the design and operation of the AI, will introduce risks. This paper offers an analysis of these potential risks along the realism/anti-realism dimension in metaethics and reveals that realism poses greater risks, but, on the other hand, anti-realism undermines the motivation for engineering a moral AI in the first place.

Advocates of moral enhancement through pharmacological, genetic, or other direct interventions sometimes explicitly argue, or assume without argument, that traditional moral education and development is insufficient to bring about moral enhancement. Traditional moral education grounded in a Kohlbergian theory of moral development is indeed unsuitable for that task; however, the psychology of moral development and education has come a long way since then. Recent studies support the view that moral cognition is a higher-order process, unified at a functional level, and that a specific moral faculty does not exist. It is more likely that moral cognition involves a number of different mechanisms, each connected to other cognitive and affective processes. Taking this evidence into account, we propose a novel, empirically informed approach to moral development and education, in children and adults, which is based on a cognitive-affective approach to moral dispositions. This is an interpretative approach that derives from the cognitive-affective personality system (Mischel and Shoda, 1995). This conception individuates moral dispositions by reference to the cognitive and affective processes that realise them. Conceived of in this way, moral dispositions influence an agent's behaviour when they interact with situational factors, such as mood or social context. Understanding moral dispositions in this way lays the groundwork for proposing a range of indirect methods of moral enhancement, techniques that promise similar results as direct interventions whilst posing fewer risks.

Predictions about autonomous weapon systems are typically thought to channel fears that drove all the myths about intelligence embodied in matter. One of these is the idea that the technology can get out of control and ultimately lead to horrifi c consequences, as is the case in Mary Shelley’s classic Frankenstein. Given this, predictions about AWS are sometimes dismissed as science-fiction fear-mongering. This paper considers several analogies between AWS and other weapon systems and ultimately offers an argument that nuclear weapons and their effect on the development of modern asymmetrical warfare are the best analogy to the introduction of AWS. The fi nal section focuses on this analogy and offers speculations about the likely consequences of AWS being hacked. These speculations tacitly draw on myths and tropes about technology and AI from popular fi ction, such as Frankenstein, to project a convincing model of the risks and benefi ts of AWS deployment.

This paper offers a theoretical framework that can be used to derive viable engineering strategies for the design and development of robots that can nudge people towards moral improvement. The framework relies on research in developmental psychology and insights from Stoic ethics. Stoicism recommends contemplative practices that over time help one develop dispositions to behave in ways that improve the functioning of mechanisms that are constitutive of moral cognition. Robots can nudge individuals towards these practices and can therefore help develop the dispositions to, for example, extend concern to others, avoid parochialism, etc.

Unlike human soldiers, autonomous weapons systems are unaffected by psychological factors that would cause them to act outside the chain of command. This is a compelling moral justification for their development and eventual deployment in war. To achieve this level of sophistication, the software that runs AWS will have to first solve two problems: the frame problem and the representation problem. Solutions to these problems will inevitably involve complex software. Complex software will create security risks and will make AWS critically vulnerable to hacking. I claim that the political and tactical consequences of hacked AWS far outweigh the purported advantages of AWS not being affected by psychological factors and always following orders. Therefore, one of the moral justifications for the deployment of AWS is undermined

Dating and hookup apps (DHAs) are now widely used and may be transforming our intimate relationships. The apps are beneficial in fostering intimate connections among those who are lonely, who are members of minority or marginalized groups, or who live nomadic lifestyles because of work or recreational travel. However, the wider social and relational changes that DHAs portend are merely beginning to be seriously discussed by academics (Arias et al., 2017). In this chapter, we employ concepts from the philosophy of technology that inform our analysis of these changes. We also collate some of the morally significant impacts of DHAs and present some of the elements of the architecture of DHAs that facilitate them. Although there are many reasons to be concerned about DHAs, we argue that there is room for cautious optimism that DHAs may be vehicles of positive social and moral change.

This paper critically assesses the possibility of moral enhancement with ambient intelligence technologies and artificial intelligence presented in Savulescu and Maslen (2015). The main problem with their proposal is that it is not robust enough to play a normative role in users’ behavior. A more promising approach, and the one presented in the paper, relies on an artifi-cial moral reasoning engine, which is designed to present its users with moral arguments grounded in first-order normative theories, such as Kantianism or utilitarianism, that reason-responsive people can be persuaded by. This proposal can play a normative role and it is also a more promising avenue towards moral enhancement. It is more promising because such a system can be designed to take advantage of the sometimes undue trust that people put in automated technologies. We could therefore expect a well-designed moral reasoner system to be able to persuade people that may not be persuaded by similar arguments from other people. So, all things considered, there is hope in artificial intelli-gence for moral enhancement, but not in artificial intelligence that relies solely on ambient intelligence technologies.

This chapter examines the possibility of using AI technologies to improve human moral reasoning and decision-making, especially in the context of purchasing and consumer decisions. We characterize such AI technologies as artificial ethics assistants (AEAs). We focus on just one part of the AI-aided moral improvement question: the case of the individual who wants to improve their morality, where what constitutes an improvement is evaluated by the individual’s own values. We distinguish three broad areas in which an individual might think their own moral reasoning and decision-making could be improved: one’s actions, character, or other evaluable attributes fall short of one’s values and moral beliefs; one sometimes misjudges or is uncertain about what the right thing to do is in particular situations, given one’s values; one is uncertain about some fundamental moral questions or recognizes a possibility that some of one’s core moral beliefs and values are mistaken. We sketch why one might think that AI tools could be used to support moral improvement in those areas, and describe two types of assistance: preparatory assistance, including advice and training supplied in advance of moral deliberation; and on-the-spot assistance, including on-the-spot advice and facilitation of moral functioning over the course of moral deliberation. Then, we turn to some of the ethical issues that AEAs might raise, looking in particular at three under-appreciated problems posed by the use of AI for moral self-improvement: namely, reliance on sensitive moral data; the inescapability of outside influences on AEAs; and AEA usage prompting the user to adopt beliefs and make decisions without adequate reasons.

Kunstmatige intelligentie (AI) en systemen die met machine learning (ML) werken, kunnen veel onderdelen van het medische besluitvormingsproces ondersteunen of vervangen. Ook zouden ze artsen kunnen helpen bij het omgaan met klinische, morele dilemma’s. AI/ML-beslissingen kunnen zo in de plaats komen van professionele beslissingen. We betogen dat dit belangrijke gevolgen heeft voor de relatie tussen een patiënt en de medische professie als instelling, en dat dit onvermijdelijk zal leiden tot uitholling van het institutionele vertrouwen in de geneeskunde.

In this paper, I offer an account of the dependence relation between perception of change and the subjective flow of time that is consistent with some extant empirical evidence from priming by unconscious change. This view is inspired by the one offered by William James, but it is articulated in the framework of contemporary functionalist accounts of mental qualities and higher-order theories of consciousness. An additional advantage of this account of the relationship between perception of change and subjective time is that is makes sense of instances where we are not consciously aware of changes but still experience the flow of time.

Transcranial magnetic stimulation is used to make inferences about relationships between brain areas and their functions because, in contrast to neuroimaging tools, it modulates neuronal activity. The central aim of this article is to critically evaluate to what extent it is possible to draw causal inferences from repetitive TMS data. To that end, we describe the logical limitations of inferences based on rTMS experiments. The presented analysis suggests that rTMS alone does not provide the sort of premises that are sufficient to warrant strong inferences about the direct causal properties of targeted brain structures. Overcoming these limitations demands a close look at the designs of rTMS studies, especially the methodological and theoretical conditions which are necessary for the functional decomposition of the relations between brain areas and cognitive functions. The main points of this article are that TMS-based inferences are limited in that stimulation-related causal effects are not equivalent to structure-related causal effects due to TMS side effects, the electric field distribution, and the sensitivity of neuroimaging and behavioral methods in detecting structure-related effects and disentangling them from confounds. Moreover, the postulated causal effects can be based on indirect effects. A few suggestions on how to manage some of these limitations are presented. We discuss the benefits of combining rTMS with neuroimaging in experimental reasoning and we address the restrictions and requirements of rTMS control conditions. The use of neuroimaging and control conditions allows stronger inferences to be gained, but the strength of the inferences that can be drawn depends on the individual experiment’s designs. Moreover, in some cases, TMS might not be an appropriate method of answering causality-related questions or the hypotheses have to account for the limitations of this technique. We hope this summary and formalization of the reasoning behind rTMS research can be of use not only for scientists and clinicians who intend to interpret rTMS results causally but also for philosophers interested in causal inferences based on brain stimulation research.

In this paper, we report on an experiment with The Walking Dead (TWD), which is a narrative-driven adventure game with morally charged decisions set in a post-apocalyptic world filled with zombies. This study aimed to identify physiological markers of moral decisions and non-moral decisions using infrared thermal imaging (ITI). ITI is a non-invasive tool used to capture thermal variations due to blood flow in specific body regions that might be caused by sympathetic activity. Results show that moral decisions seem to elicit a significant decrease in temperature in the chin region 20 seconds after participants are presented with a moral decision. However, given the small sample involved, and the lack of significance in other regions, future studies might be needed to confirm the results obtained in this work.

It is easier to talk frankly to a person whom one trusts. It is also easier to agree with a scientist whom one trusts. Even though in both cases the psychological state that underlies the behavior is called ‘trust’, it is controversial whether it is a token of the same psychological type. Trust can serve an affective, epistemic, or other social function, and comes to interact with other psychological states in a variety of ways. The way that the functional role of trust changes across contexts and objects is further complicated when communities and individuals mediate it through technologies, and even more so when that mediation involves artificial intelligence (AI) and machine learning (ML). In this chapter I look at the ways in which trust in institutions, and specifically the medical profession, is affected by the use of AI and ML. There are two key elements of this analysis. The first is a disanalogy between institutional trust in medicine and institutional trust in science (Irzik and Kurtulmus 2021, 2019; Kitcher 2001). I note that as AI and ML become a more prominent part of medicine, trust in a medical institution becomes more like trust in a scientific institution. This is problematic for institutional trust in medicine and the practice of medicine, since institutional trust in science has been undermined by, among other things, the spread of misinformation online and the replication crisis (Romero 2019). There is also a strong analogy between the psychological state of the person who trusts a scientific report or testimony and the psychological state of a patient who trusts individual recommendations made by a medical professional in a clinical setting. In both cases, institutional trust makes it less likely that a mistake or malfeasance will result in reactive attitudes, such as blame or anger, directed at other individual members of that institution. However, it also renders people vulnerable enough to blame the institution itself. This, with time, can erode trust in the institution and naturally leads to policy recommendations that aim to preserve institutional trust. I survey two ways in which that can be done with institutional trust in medicine in the age of AI and ML.

This paper provides an analysis of the way in which two foundational principles of medical ethics–the trusted doctor and patient autonomy–can be undermined by the use of machine learning (ML) algorithms and addresses its legal significance. This paper can be a guide to both health care providers and other stakeholders about how to anticipate and in some cases mitigate ethical conflicts caused by the use of ML in healthcare. It can also be read as a road map as to what needs to be done to achieve an acceptable level of explainability in an ML algorithm when it is used in a healthcare context.

In this study, we extracted facial action units (AUs) data during a Hearthstone tournament to investigate behavioural differences between expert, intermediate, and novice players. Our aim was to obtain insights into the nature of expertise and how it may be tracked using non-invasive methods such as AUs. These insights may shed light on the endogenous responses in the player and at the same time may provide information to the opponents during a competition. Our results show that player expertise may be characterised by specific patterns in facial expressions. More specifically, AU17 (chin raiser), AU25 (lips apart), and AU26 (jaws drop) intensity responses during gameplay may vary according to players' expertise. Such results were obtained by training a random forest classifier to test whether we can use these three AUs alone to accurately detect players' expertise. The classifier reached 0.75 accuracy on 5-fold cross-validation, after balancing the class weights, and 0.85 after having applied the Synthetic Minority Over-sampling Technique (SMOTE) function. These results suggest that AUs can be effectively used to discriminate different levels of expertise in competitive video game players.

This study aimed to evaluate if eye blinks can be used to discriminate players with different performance in a session of Nintendo Entertainment System (NES) Tetris. To that end, we developed a state-of-the-art method for blink extraction from EAR measures, which is robust enough to be used with data collected by a low-grade webcam such as the ones widely available on laptop computers. Our results show a significant decrease in blink rate per minute (blinks/m) during the first minute of playing Tetris. After having defined 3 groups of proficiency based on in-game performance (Novices, Intermediates, and Experts) we found out that expert players display a significantly lower decrease in blinks/m compared to novices during the first minute of gameplay, which shows that Tetris players' proficiency can be detected by looking at eye blinks/m variations during the early phase of a game session. This difference in blinks/m is observed throughout the entire game session, which supports the general conclusion that proficient Tetris players have a lower decrease in blinks/m, even when playing more difficult levels. Finally, we offer some interpretations of this effect and the relationship that our results may have with the visual cognitive workload experienced during the gameplay.

It is not clear to what the projects of creating an artificial intelligence (AI) that does ethics, is moral, or makes moral judgments amounts. In this paper we discuss some of the extant metaethical theories and debates in moral philosophy by which such projects should be informed, specifically focusing on the project of creating an AI that makes moral judgments. We argue that the scope and aims of that project depend a great deal on antecedent metaethical commitments. Metaethics, therefore, plays the role of an Archimedean fulcrum in this context, very much like the Archimedean role that it is often taken to take in context of normative ethics (Dworkin 1996; Dreier 2002; Fantl 2006; Ehrenberg 2008).

In this chapter we take stock of some of the complexities of the sprawling field of AI ethics. We consider questions like "what is the proper scope of AI ethics?" And "who counts as an AI ethicist?" At the same time, we flag several potential uses and abuses of AI ethics. These include challenges for the AI ethicist, including what qualifications they should have; the proper place and extent of futuring and speculation in the field; and the dilemmas concerning how we use our public and academic resources.

In this study, we analyzed the blinking behavior of players in a video game tournament. Our aim was to test whether spontaneous blink patterns differ across levels of expertise. We used blink rate, blink duration, blink frequency, and eyelid movements represented by the Eye Aspect Ratio (EAR) to train a machine learning classifier to discriminate between different levels of expertise. Classifier performance was highly influenced by features such as the mean, standard deviation and median EAR. Moreover, further analysis suggests that blinking rate and blink duration are likely to increase along with the level of expertise. We speculate this may be indicative of a reduction in cognitive load and lowered stress of expert players. In general, our results suggest that EAR and blink patterns can be used to identify different levels of expertise of video game players.