Impact of Artificial Intelligence

Three directions for the AI avant-garde are sketched against the background of time. Posthumanism changes what we are, and belongs to the radical future. Transhumanism changes how we are, and corresponds with the radical past. Genhumanism changes who we are, and exists in the radical present. While developing the concepts, this essay intersects in two ways with theoretical debates about humanism in the face of technological advance. First, it describes how temporal divisions may cleanly differentiate post- and transhumanism. Second, the (...) essay introduces generative humanism, which contributes to discussions about AI and society by delineating a novel humanistic response to contemporary technology. Finally, grounds are provided for a practical project, one where philosophers work with AI engineers in the area of genhumanism. Contemporary AI research into serendipity in recommendation engines provides natural support for the shared research. (shrink)

Since Clark and Chalmers advanced “The Extended Mind” in 1998, a persistent dispute evolved on how our tool interactions shape the kind of cognition we have. Extended cognition generally views us as cognitively augmented and enhanced by our tool practices, which shall render our cognitive constitution extended to those tools. Bounded and embedded cognition have primarily criticized this metaphysical claim. However, another contender may arise from considering how we use more intelligent tools. We arguably employ advanced technologies that capture, mimic, (...) and then replace our cognitive skills, which we then no longer need to exercise ourselves. This precedes any metaphysical debate, since such practices might stand in a more fundamental conflict with extended cognition. The counter-hypothesis of extracted cognition states that we primarily tend to use tools that initially attain and eventually displace our cognitive responsibilities and involvements. This paper evaluates extended and extracted cognition by comparing theoretical, practical, and ethical arguments respectively. If extracted cognition describes most convincingly how such tool interactions shape our kind of cognition, then we may also endorse “The Extracted Mind”. (shrink)

Beauty filters are automated photo editing tools that use artificial intelligence and computer vision to detect facial features and modify them, allegedly improving a face’s physical appearance and attractiveness. Widespread use of these filters has raised concern due to their potentially damaging psychological effects. In this paper, I offer an account that examines the effect that interacting with such filters has on self-perception. I argue that when looking at digitally-beautified versions of themselves, individuals are looking at AI-curated distorted mirrors. This (...) helps identify two potential cognitive effects of this behavior. First, it can elicit affective attitudes that change how individuals feel when looking at their unfiltered self-images. Second, exposure to filtered self-images might cause a perceptual normalization of such images. Finally, I argue that this form of distorted mirror gazing is a novel cultural practice for self-perception, and I highlight some ways in which this practice could be critically evaluated and ultimately changed. (shrink)

Advances in Artificial Intelligence (AI) have driven the evolution of reasoning in modern AI models, particularly with the development of Large Language Models (LLMs) and their "Think and Answer" paradigm. This paper explores the influence of human reinforcement on AI reasoning and its potential to enhance decision-making through dynamic human interaction. It analyzes the roots of bias and fairness in AI, arguing that these issues often stem from human data and reflect inherent human biases. The paper is structured as follows: (...) first, it frames reasoning as a mechanism for ethical reflection, grounded in dynamic learning and feedback loops; second, it discusses how scaling laws suggest that reinforcement learning (RL) can mitigate biases and promote fairness; third, it examines how RL allows models to view algorithmic bias and fairness as approximation problems. Through an experimental study, the paper demonstrates how AI models, empowered by RL, have successfully identified biases across various domains, including gender and socio-economic contexts, highlighting how reasoning improves algorithmic fairness. Ultimately, this work emphasizes the role of RL in mitigating algorithmic bias and enhancing fairness in AI systems. (shrink)

Many social media platforms enable (nearly) anyone to post (nearly) anything. One clear downside of this permissiveness is that many people appear bad at determining who to trust online. Hacks, quacks, climate change deniers, vaccine skeptics, and election deniers have all gained massive followings in these free markets of ideas, and many of their followers seem to genuinely trust them. At the same time, there are many cases in which people seem to reliably determine who to trust online. Consider, for (...) example, Do It Yourself (DIY) content about how to play guitar, bake, fix one’s plumbing, or repair one’s car. For these topics, those who have the largest accounts and the most popular content typically possess significant expertise. That is, social media users seem to reliably pick out DIY experts. We thus have a puzzle: why are social media users seemingly competent at identifying DIY experts, but not climate science or vaccine experts? In what follows, we solve this puzzle. We begin by identifying a novel wisdom of the crowds phenomenon: specifically we argue that the crowd (in combination with social media search and recommendation algorithms) reliably picks out DIY experts and serves as a credentialing institution for DIY content. Next, we argue that (a) there are five epistemic factors that determine whether the crowd can succeed at recognizing experts on social media platforms, and (b) while many of those factors are satisfied to a sufficiently high degree by DIY content, they are mostly lacking for content about climate change or vaccines. (shrink)

Nick Bostrom is perhaps best known for his 2014 book Superintelligence, which explores the existential threat posed to humans by superintelligent AI. In his new book, Deep Utopia, he shifts gears to explore the existentialist threat that superintelligent AI poses. Suppose that humans successfully navigate the technological, moral, and political challenges that come with advanced AI. The result of such success, according to Bostrom, would be a kind of deep utopia. We would not only live in a post-work, and post-scarcity (...) society (things already covered by shallower conceptions of utopia); we would also live in a “post-instrumental” society where almost anything we might consider doing, including pursuing our own personal enrichment, could be better accomplished by AI. Bostrom’s central concern is how we might find meaning and purpose in such a world. If everything you might consider doing can be done better by machine intelligence, what is there to live for? (shrink)

This article draws on Bernard Stiegler and Gilbert Simondon’s work to further interrogate the psychic, social, and political problems raised by the development of Artificial Intelligence. Stiegler’s political philosophy of time-consciousness reveals three concomitants urgencies: human memory is conditioned by industrial supplements that are increasingly disruptive, capitalism has produced an entropic condition where life on earth is threaten by toxic systems, the deployment of technologies of spirits has striped individuals of their psychic and collective individuation. I read media theory along (...) with Simondon’s philosophy to elucidate the question of mediation in today’s reticulated AI-driven society. (shrink)

Elon Musk famously predicted that an artificial intelligence superior to the smartest individual human would arrive by the year 2025. In response, Gary Marcus offered Musk a $1 million bet to the effect that he would be proved wrong. In specifying the conditions of this bet (which Musk did not take) Marcus lists the following ‘tasks that ordinary people can perform’ which, he claimed, AI will not be able to perform by the end of 2025. • Reliably drive a car (...) in a novel location that they haven’t previously encountered, even in the face of unusual circumstances like hand lettered signs, without the assistance of other humans. • Learn to ride a mountain bike off-road through forest trails. • Babysit children in an unfamiliar home and keep them safe. • Tend to the physical and psychological needs of an elderly or infirm person. Each of these tasks involves the application of practical or tacit knowledge, or what is also called ‘knowing how’, which is to say knowledge of a sort that is captured not by means of sentences or propositions or explicit rules, but rather through the expertise demonstrated in human actions. The talk addresses the puzzle that nowhere in the AI literature do we find a discussion of knowing how as a feature of human intelligence. (shrink)

Applications and devices of artificial intelligence are increasingly common in the healthcare field. Robots fulfilling some caregiving functions are not a distant future. In this scenario, we must ask ourselves if it is possible for machines to care to the extent of completely replacing human care and if such replacement, if possible, is desirable. In this paper, I argue that caregiving requires know-how permeated by affectivity that is far from being achieved by currently available machines. I also maintain that the (...) complete substitution of machine caregiving for human caregiving is not desirable because caregiving requires genuine human connection. (shrink)

The value of testimony as a source of knowledge has been a subject of epistemological debates. The "trust theory of testimony" suggests that human testimony is based on an affective relationship supported by social norms. However, the advent of generative artificial intelligence challenges our understanding of genuine testimony. The concept of "quasi-testimony" seeks to characterize utterances produced by non-human entities that mimic testimony but lack certain fundamental attributes. This article analyzes these issues in depth, exploring philosophical perspectives on testimony and (...) the implications of conversational AI technologies on our epistemic practices. (shrink)

There is an underappreciated respect in which the widespread availability of generative artificial intelligence (AI) models poses a threat to human connection. My central contention is that human creativity is especially capable of helping us connect to others in a valuable way, but the widespread availability of generative AI models reduces our incentives to engage in various sorts of creative work in the arts and sciences. I argue that creative endeavors must be motivated by curiosity, and so they must disclose (...) the creative agent’s inquisitive self. It is through self-disclosure, including the disclosure of the inquisitive self, that we put ourselves in a position to be seen by and connect with others through our creative pursuits. Because relying on AI for certain generative tasks is less self-disclosive than the creative work such technologies supplant, this reliance threatens to weaken our connections to one another. (shrink)

The increasing use of artificial intelligence (AI) systems in our daily life through various applications, services, and products explains the significance of trust/distrust in AI from a user perspective. AI-driven systems have significantly diffused into various fields of our lives, serving as beneficial tools used by human agents. These systems are also evolving to act as co-assistants or semi-agents in specific domains, potentially influencing human thought, decision-making, and agency. Trust/distrust in AI plays the role of a regulator and could significantly (...) control the level of this diffusion, as trust can increase, and distrust may reduce the rate of adoption of AI. Recently, varieties of studies have paid attention to the variant dimension of trust/distrust in AI, and its relevant considerations. In this systematic literature review, after conceptualization of trust in the current AI literature review, we will investigate trust in different types of human-Machine interaction, and its impact on technology acceptance in different domains. In addition to that, we propose a taxonomy of technical (i.e., safety, accuracy, robustness) and non-technical axiological (i.e., ethical, legal, and mixed) trustworthiness metrics, and some trustworthy measurements. Moreover, we examine some major trust-breakers in AI (e.g., autonomy and dignity threat), and trust makers; and propose some future directions and probable solutions for the transition to a trustworthy AI. (shrink)

This paper offers the first careful analysis of the possibility that AI and humanity will go to war. The paper focuses on the case of artificial general intelligence, AI with broadly human capabilities. The paper uses a bargaining model of war to apply standard causes of war to the special case of AI/human conflict. The paper argues that information failures and commitment problems are especially likely in AI/human conflict. Information failures would be driven by the difficulty of measuring AI capabilities, (...) by the uninterpretability of AI systems, and by differences in how AIs and humans analyze information. Commitment problems would make it difficult for AIs and humans to strike credible bargains. Commitment problems could arise from power shifts, rapid and discontinuous increases in AI capabilities. Commitment problems could also arise from missing focal points, where AIs and humans fail to effectively coordinate on policies to limit war. In the face of this heightened chance of war, the paper proposes several interventions. War can be made less likely by improving the measurement of AI capabilities, capping improvements in AI capabilities, designing AI systems to be similar to humans, and by allowing AI systems to participate in democratic political institutions. (shrink)

The use of Artificial Intelligence (AI) in educational gamification marks a significant advancement, transforming traditional learning methods by offering interactive, adaptive, and personalized content. This approach makes historical content more relatable and promotes active learning and exploration. This research presents an innovative approach to heritage education, combining AI and gamification, explicitly targeting the Silk Roads. It represents a significant progression in a series of research, transitioning from basic 2D textual interactions to a 3D environment using photogrammetry, combining historical authenticity and (...) immersive gameplay. It features AI-driven characters developed in Unreal Engine, which provides heritage insights augmented by personalized interactions, highlighting the importance of collaborative tools like interactive quizzes for enriching education and promoting critical thinking and community among learners. In conclusion, this research underlines the transformative impact of AI and gamification in heritage education enhancement, integrating real-time voice conversation and interactive pedagogical methods into immersive 3D environments as a supplementary tool. (shrink)

Background: Microbiome biomarker discovery for patient diagnosis, prognosis, and risk evaluation is attracting broad interest. Selected groups of microbial features provide signatures that characterize host disease states such as cancer or cardio-metabolic diseases. Yet, the current predictive models stemming from machine learning still behave as black boxes and seldom generalize well. Their interpretation is challenging for physicians and biologists, which makes them difficult to trust and use routinely in the physician-patient decision-making process. Novel methods that provide interpretability and biological insight (...) are needed. Here, we introduce "predomics", an original machine learning approach inspired by microbial ecosystem interactions that is tailored for metagenomics data. It discovers accurate predictive signatures and provides unprecedented interpretability. The decision provided by the predictive model is based on a simple, yet powerful score computed by adding, subtracting, or dividing cumulative abundance of microbiome measurements. -/- Results: Tested on >100 datasets, we demonstrate that predomics models are simple and highly interpretable. Even with such simplicity, they are at least as accurate as state-of-the-art methods. The family of best models, discovered during the learning process, offers the ability to distil biological information and to decipher the predictability signatures of the studied condition. In a proof-of-concept experiment, we successfully predicted body corpulence and metabolic improvement after bariatric surgery using pre-surgery microbiome data. -/- Conclusions: Predomics is a new algorithm that helps in providing reliable and trustworthy diagnostic decisions in the microbiome field. Predomics is in accord with societal and legal requirements that plead for an explainable artificial intelligence approach in the medical field. (shrink)

In the rapidly evolving landscape of artificial intelligence (AI), disparities in access and benefits are becoming increasingly apparent, leading to the emergence of an AI divide. This divide not only amplifies existing socio-economic inequalities but also fosters the creation of AI caste systems, where marginalized groups—referred to as AI Dalits—are systematically excluded from AI advancements. This article explores the definitions and contributing factors of the AI divide and delves into the concept of AI caste systems, illustrating how they perpetuate inequality. (...) Furthermore, it proposes comprehensive strategies for bridging this divide through inclusive and equitable approaches. Key strategies include promoting equal access to AI education and resources, enhancing diversity and representation in AI development, implementing fair and transparent AI policies, promoting public awareness and engagement, and empowering AI Dalits through targeted initiatives. By championing an "All in AI" and "AI for All" paradigm, this paper envisions an inclusive future where the benefits of AI are universally shared, ensuring that no group is left behind in the AI-driven world. (shrink)

This paper analyses the phenomenology and epistemology of chatbots such as ChatGPT and Bard. The computational architecture underpinning these chatbots are large language models (LLMs), which are generative artificial intelligence (AI) systems trained on a massive dataset of text extracted from the Web. We conceptualise these LLMs as multifunctional computational cognitive artifacts, used for various cognitive tasks such as translating, summarizing, answering questions, information-seeking, and much more. Phenomenologically, LLMs can be experienced as a “quasi-other”; when that happens, users anthropomorphise them. (...) For most users, current LLMs are black boxes, i.e., for the most part, they lack data transparency and algorithmic transparency. They can, however, be phenomenologically and informationally transparent, in which case there is an interactional flow. Anthropomorphising and interactional flow can, in some users, create an attitude of (unwarranted) trust towards the output LLMs generate. We conclude this paper by drawing on the epistemology of trust and testimony to examine the epistemic implications of these dimensions. Whilst LLMs generally generate accurate responses, we observe two epistemic pitfalls. Ideally, users should be able to match the level of trust that they place in LLMs to the degree that LLMs are trustworthy. However, both their data and algorithmic opacity and their phenomenological and informational transparency can make it difficult for users to calibrate their trust correctly. The effects of these limitations are twofold: users may adopt unwarranted attitudes of trust towards the outputs of LLMs (which is particularly problematic when LLMs hallucinate), and the trustworthiness of LLMs may be undermined. (shrink)

Mark Coeckelbergh is a Belgian philosopher who specializes in the philosophy of technology. His work primarily explores the intersection of technology and society, specifically the philosophical implications of emerging technologies such as AI and robotics. He has written on whether machines can be moral agents and how ethical frameworks should be applied to autonomous machines. He has a broad philosophical perspective drawing on classical sources, Eastern philosophy, Marxism, Foucault, phenomenology, and the postmodernists. In this short text, he brings his remarkable (...) insights and erudition to bear on our attempts at self-improvement in the age of AI. (shrink)

What can contemporary machine learning (ML) models do? Given the proliferation of ML models in society, answering this question matters to a variety of stakeholders, both public and private. The evaluation of models' capabilities is rapidly emerging as a key subfield of modern ML, buoyed by regulatory attention and government grants. Despite this, the notion of an ML model possessing a capability has not been interrogated: what are we saying when we say that a model is able to do something? (...) And what sorts of evidence bear upon this question? -/- In this paper, we aim to answer these questions, using the capabilities of large language models (LLMs) as a running example. Drawing on the large philosophical literature on abilities, we develop an account of ML models' capabilities which can be usefully applied to the nascent science of model evaluation. Our core proposal is a conditional analysis of model abilities (CAMA): crudely, a machine learning model has a capability to X just when it would reliably succeed at doing X if it 'tried'. The main contribution of the paper is making this proposal precise in the context of ML, resulting in an operationalisation of CAMA applicable to LLMs. We then put CAMA to work, showing that it can help make sense of various features of ML model evaluation practice, as well as suggest procedures for performing fair inter-model comparisons. (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)

The article argues that AI can enhance the measurement and implementation of democratic processes within political parties, known as Intra-Party Democracy (IPD). It identifies the limitations of traditional methods for measuring IPD, which often rely on formal parameters, self-reported data, and tools like surveys. Such limitations lead to partial data collection, rare updates, and significant resource demands. To address these issues, the article suggests that specific data management and Machine Learning techniques, such as natural language processing and sentiment analysis, can (...) improve the measurement and practice of IPD. (shrink)

Bài viết này giới thiệu năm yếu tố tiền đề đó với mục đích gia tăng nhận thức về quan hệ giữa người và máy trong bối cảnh công nghệ ngày càng thay đổi cuộc sống thường nhật. Năm tiền đề bao gồm: Tiền đề về cấu trúc xã hội, văn hóa, chính trị, và lịch sử; về tính tự chủ và sự tự do của con người; về nền tảng triết học và nhân văn của nhân loại; về hiện (...) tượng tiếp biến văn hóa sinh ra từ tương tác giữa người và máy; về tam giác tương tác giữa con người với máy tính, giữa con người với con người, và giữa con người với thiên nhiên. Trên cơ sở giải thích rõ về năm tiền đề này, bài viết đã đưa ra ba gợi ý cho Việt Nam về việc phát triển, ứng dụng, và quản lý trí tuệ nhân tạo. (shrink)

The present narrative review seeks to unravel where we are now, and where we need to go to delineate the active ingredients in psychotherapy delivered by conversational agents (e.g., chatbots). While psychotherapy delivered by conversational agents has shown promising effectiveness for depression, anxiety, and psychological distress across several randomized controlled trials, little emphasis has been placed on the therapeutic processes in these interventions. The theoretical framework of this narrative review is grounded in prominent perspectives on the active ingredients in psychotherapy. (...) Based on search terms derived from various theoretical perspectives, we conducted a systematic literature search of four scientific databases and identified 17 studies. Across the selected studies, three themes emerged: relationship variables, emotional venting, and cognitive factors. While methodological issues compromise the epistemic value of this evidence base, core questions also remain to be answered. Such questions include, but are not limited to, whether humans can form affectionate bonds to inanimate beings and whether these kind of mental health treatments should be understood as psychotherapy or something else. Researchers should therefore be cautious when applying theories of psychotherapy in the realm of conversational agents. We conclude the paper by introducing recommendations for future research, which we hope will help instigate methodologically sound studies in this field. (shrink)

For synergistic interactions between humans and artificial intelligence (AI) systems, AI outputs often need to be explainable to people. Explainable AI (XAI) systems are commonly tested in human user studies. However, whether XAI researchers consider potential cultural differences in human explanatory needs remains unexplored. We highlight psychological research that found significant differences in human explanations between many people from Western, commonly individualist countries and people from non-Western, often collectivist countries. We argue that XAI research currently overlooks these variations and that (...) many popular XAI designs implicitly and problematically assume that Western explanatory needs are shared cross-culturally. Additionally, we systematically reviewed over 200 XAI user studies and found that most studies did not consider relevant cultural variations, sampled only Western populations, but drew conclusions about human-XAI interactions more generally. We also analyzed over 30 literature reviews of XAI studies. Most reviews did not mention cultural differences in explanatory needs or flag overly broad cross-cultural extrapolations of XAI user study results. Combined, our analyses provide evidence of a cultural bias toward Western populations in XAI research, highlighting an important knowledge gap regarding how culturally diverse users may respond to widely used XAI systems that future work can and should address. (shrink)

What does it mean to be human? Philosophers and theologians have been wrestling with this question for centuries. Recent advances in cognition, neuroscience, artificial intelligence and robotics have yielded insights that bring us even closer to an answer. There are now computer programs that can accurately recognize faces, engage in conversation, and even compose music. There are also robots that can walk up a flight of stairs, work cooperatively with each other and express emotion. If machines can do everything we (...) can, does that mean we are machines? -/- This book examines whether an artificial person can be constructed and if so, what that might tell us about our future and ourselves. Different human capacities such as perception, creativity, consciousness, social behavior, and free will are described in separate chapters. Technological advances in these areas are summarized and compared to our own abilities. The book adopts a multi-disciplinary approach, with a naturalistic perspective drawn from biology and psychology matched against a technological perspective based on computer science and robotics. (shrink)

This research explores how a person with whom information has been shared and, importantly, an artificial intelligence (AI) system used to deduce information from the shared data contribute to making the disclosure context private. The study posits that private contexts are constituted by the interactions of individuals in the social context of intersubjectivity based on trust. Hence, to make the context private, the person who is the trustee (i.e., with whom information has been shared) must fulfil trust norms. According to (...) the commitment account of trustworthiness, a person is trustworthy only if they satisfy the norm of competence. It is argued that a person using an AI system to answer a question is competent only if they are ex post justified in believing what has been delivered by the AI system. A person’s belief is justified in the doxastic sense only if the AI system is accurate. This feature of AI’s performance affects a person’s competence and, as a result, trustworthiness. The effect of AI on trust as an essential component of making the context private, and thus on privacy, means an AI system also impacts privacy. Therefore, a private context is constituted when the individual with whom the information is shared fulfils the competence norm and the AI system used for analysing the information is sufficiently accurate to adhere to this norm. The result of this research emphasises the significance of the relationship between individuals involved in information-sharing and how an AI system used for analysing that information impacts the relationship regarding making the context private, as well as how it impacts privacy. The findings of this research have significant implications for improving or ameliorating privacy regulations in light of trust. (shrink)

Deepfakes pose a multi-faceted threat to the acquisition of knowledge. It is widely hoped that technological solutions—in the form of artificially intelligent systems for detecting deepfakes—will help to address this threat. I argue that the prospects for purely technological solutions to the problem of deepfakes are dim. Especially given the evolving nature of the threat, technological solutions cannot be expected to prevent deception at the hands of deepfakes, or to preserve the authority of video footage. Moreover, the success of such (...) technologies depends on institutional trust that is in short supply. Finally, outsourcing the discrimination between the real and the fake to automated, largely opaque systems runs the risk of undermining epistemic autonomy. (shrink)

“Throw away anything that sounds too complicated. Only keep what is simple to grasp...If the information appears fuzzy and causes the brain to implode after two sentences, toss it away and stop listening. Doing so will make the news as orderly and simple to understand as the truth.” - In “GHG emissions,” The Kingfisher Story Collection, (Vuong 2022a).

The simulation hypothesis can reinforce a cynical dismissal of human potential. This attitude can allow online platform designers to rationalize employing manipulative neuromarketing techniques to control user decisions. We point to cognitive boosting techniques at both user and designer levels to build critical reflection and mindfulness.

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

Psychoanalysis, particularly as articulated by figures like Freud and Lacan, highlights the inherent division within the human subject—a schism between the conscious and unconscious mind. It could be said that this suggests that such an internal division becomes amplified in the context of generative art, where technology and algorithms are used to generate artistic expressions that are meant to emerge from the depths of the unconscious. Here, we encounter the tension between the conscious artist and the generative process itself, which (...) may yield unexpected, even uncontrollable results. -/- This paper, therefore, seeks to address this division within the modern subject and its relationship to technology, wherein the division within the living body is revealed through the presence of prosthetic elements, which mirrors the division brought about by the incorporation of language as a signifier. I argue that the amplification of this internal schism does not necessarily lead to a more fractured subject. Instead, generative art, bolstered by advancements in AI and machine learning, offers a unique opportunity for individuals to externalize and explore their minds in novel ways. -/- By examining contemporary works such as Hal Foster’s Prosthetic Gods, which stands as a pivotal exploration of the convergence between modernist art and psychoanalytic theory and Isabel Millar’s Psychoanalysis of Artificial Intelligence, this paper elucidates the profound implications of Freud’s vision of modern -/- subjectivity as Prothesengott (Prosthetic God) and address the questions concerning this technological imbrication of the human mind and body through the Lacanian framework. Although for Freud, Man does not become a real God, rather, the potential to transcend one’s limitations ascribes us to God-like qualities by seeking to generate new forms of life that go beyond merely reproducing nature — a transcention of the natural. Millar emphasizes that Freud observes that this is evidenced by the fact that these additional organs remain distinct from the organism and can never assimilate into it. One continually falls short of realizing the fantasy he envisions, opting instead to use his supplementary artificial organs to endlessly revolve around the objects of the drive. -/- This evolving relationship that the drive has with its technological objects, resounds in Lacan’s conception of “lathouse” which allows extimate objects to convert interiority (unconscious) into exteriority (conscious) and exteriority into interiority. The thesis of this paper seeks to employ this underutilized concept to understand the nature of human subjectivity and its bodily and structural relationship to generative art. Therefore, this paper emphasizes what really happens when we enter into this relationship with the lathouse, whereby this artificial object has effects in the "real of jouissance", where these Lathouses create a network, namely the Alethosphere. My goal is to argue that generative art as a technological development, can be seen as an extension to the development of the drive. Conclusively, I make the claim for generative art's potential to externalize the human creative drive by emphasizing the interplay between randomness and structure, and how it offers a means to surpass our inherent limitations by presenting an avenue for self-expression that transcends traditional modes of art. (shrink)

Artificial Intelligence (AI) is increasingly adopted in society, creating numerous opportunities but at the same time posing ethical challenges. Many of these are familiar, such as issues of fairness, responsibility and privacy, but are presented in a new and challenging guise due to our limited ability to steer and predict the outputs of AI systems. This chapter first introduces these ethical challenges, stressing that overviews of values are a good starting point but frequently fail to suffice due to the context (...) sensitivity of ethical challenges. Frequently, additional (ethical) values emerge for specific applications, as e.g. challenges with fraud detection are very different from those around language technologies. Second, methods to tackle these challenges are discussed. Main ethical theories (virtue ethics, consequentialism, and deontology) are shown to provide a starting point, but often lack the details needed for actionable AI Ethics. Instead, mid-level philosophical theories coupled to design-approaches such as Design for Values, together with interdisciplinary working methods, offer the best way forwards. The chapter aims to show how these approaches can lead to an ethics of AI that is actionable and that can be proactively integrated in the design of AI systems. (shrink)

Despite its centrality in the philosophy of cognitive science, there has been little prior philosophical work engaging with the notion of representation in contemporary NLP practice. This paper attempts to fill that lacuna: drawing on ideas from cognitive science, I introduce a framework for evaluating the representational claims made about components of neural NLP models, proposing three criteria with which to evaluate whether a component of a model represents a property and operationalising these criteria using probing classifiers, a popular analysis (...) technique in NLP (and deep learning more broadly). The project of operationalising a philosophically-informed notion of representation should be of interest to both philosophers of science and NLP practitioners. It affords philosophers a novel testing-ground for claims about the nature of representation, and helps NLPers organise the large literature on probing experiments, suggesting novel avenues for empirical research. (shrink)

This paper seeks to answer the question: Can contemporary forms of artificial intelligence be creative? To answer this question, I consider three conditions that are commonly taken to be necessary for creativity. These are novelty, value, and agency. I argue that while contemporary AI models may have a claim to novelty and value, they cannot satisfy the kind of agency condition required for creativity. From this discussion, a new condition for creativity emerges. Creativity requires curiosity, a motivation to pursue epistemic (...) goods. I argue that contemporary AI models do not satisfy this new condition. Because they lack both agency and curiosity, it is a mistake to attribute the same sort of creativity to AI that we prize in humans. Finally, I consider the question of whether these AI models stand to make human creativity in the arts and sciences obsolete, despite not being creative themselves. I argue, optimistically, that this is unlikely. (shrink)

The recent wave of generative AI (GAI) systems like Stable Diffusion that can produce images from human prompts raises controversial issues about creatorship, originality, creativity and copyright. This paper focuses on creatorship: who creates and should be credited with the outputs made with the help of GAI? Existing views on creatorship are mixed: some insist that GAI systems are mere tools, and human prompters are creators proper; others are more open to acknowledging more significant roles for GAI, but most conceive (...) of creatorship in an all-or-nothing fashion. We develop a novel view, called CCC (collective-centered creation), that improves on these existing positions. On CCC, GAI outputs are created by collectives in the first instance. Claims to creatorship come in degrees and depend on the nature and significance of individual contributions made by the various agents and entities involved, including users, GAI systems, developers, producers of training data and others. Importantly, CCC maintains that GAI systems can sometimes be part of a co-creating collective. We detail how CCC can advance existing debates and resolve controversies around creatorship involving GAI. (shrink)

We propose a quantum curiosity algorithm as a means to implement quantum thinking into AI, and we illustrate 5 new quantum curiosity types. We then introduce 6 new hybrid quantum curiosity types combining animal and plant curiosity elements with biomimicry beyond human sensing. We then introduce 4 specialized quantum curiosity types, which incorporate quantum thinking into coding frameworks to radically transform problem-solving and discovery in science, medicine, and systems analysis. We conclude with a forecasting of the future of quantum thinking (...) in AI and illustrate an example of how to apply the new curiosity types: General Collaborative Networks. (shrink)

Consulting early and medieval Christian thinkers, I theologically analyze the question of how we are to construe and live well with the sociable robot under the ancient theological concept of “glory”—the manifestation of God’s nature and life outside of himself. First, the oft-noted Western wariness toward robots may in part be rooted in protecting a certain idea of the “person” as a relational subject capable of self-gift. Historically, this understanding of the person derived from Christian belief in God the Trinity, (...) an eternally relational and self-giving God who has created all other things. According to this trinitarian anthropology, the “glory” of God is the manifestation of his life outside of himself, especially in human relationships of self-possessed empathic self-giving. Second, the material world can be drawn into this glorification of God by the invention of technologies, including robots. For Christianity, the personal transcends the material, and matter cannot simply be recombined to make a person. Nonetheless, the material world is a lesser glory that echoes fragmentarily the primal self-gift by which God exists. Human persons can marshal these material powers to serve the personal by the invention of technology, which extends the possibilities of human self-giving and, therefore, of God’s “glory.” Third, this Christian account of creation and technology shapes medieval Christian writings on humanoid robots. These “automata” uniquely draw together nature’s deep powers, but they lack true personal interiority and so cannot give themselves. Instead, they are instruments by which humans’ own relational personhood can be developed or degraded. At best, a robotic image of personhood can serve as an “icon,” directing us back to the relationality by which humans echo God. At worst, robots serve as “idols” when they become substitutes for human companions, drawing their users into a utilitarian frame that excludes self-gift by simply mirroring back to the user his or her own aims. I illustrate these outcomes by two medieval legends. In one, robots function iconically as social facilitators; in the other, as ambiguous romantic partners. And fourth, looking to an actual renaissance-era “praying” robot, I will propose that the non-subjective robot might yet “glorify God” within the religious community by representing the prayers of particular humans—thus iconically standing for rather than idolatrously standing in for the relational subject. (shrink)

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

Growing demand for broadly accessible mental health care, together with the rapid development of new technologies, trigger discussions about the feasibility of psychotherapeutic interventions based on interactions with Conversational Artificial Intelligence (CAI). Many authors argue that while currently available CAI can be a useful supplement for human-delivered psychotherapy, it is not yet capable of delivering fully fledged psychotherapy on its own. The goal of this paper is to investigate what are the most important obstacles on our way to developing CAI (...) systems capable of delivering psychotherapy in the future. To this end, we formulate and discuss three challenges central to this quest. Firstly, we might not be able to develop effective AI-based psychotherapy unless we deepen our understanding of what makes human-delivered psychotherapy effective. Secondly, assuming that it requires building a therapeutic relationship, it is not clear whether psychotherapy can be delivered by non-human agents. Thirdly, conducting psychotherapy might be a problem too complicated for narrow AI, i.e., AI proficient in dealing with only relatively simple and well-delineated tasks. If this is the case, we should not expect CAI to be capable of delivering fully-fledged psychotherapy until the so-called “general” or “human-like” AI is developed. While we believe that all these challenges can ultimately be overcome, we think that being mindful of them is crucial to ensure well-balanced and steady progress on our path to AI-based psychotherapy. (shrink)

The appearance of openly accessible Artificial Intelligence Applications such as Large Language Models, nowadays capable of almost human-level performances in complex reasoning tasks had a tremendous impact on public opinion. Are we going to be "replaced" by the machines? Or - even worse - "ruled" by them? The behavior of these systems is so advanced they might almost appear "alive" to end users, and there have been claims about these programs being "sentient". Since many of our relationships of power and (...) social dominance are apparently based on intelligence, where the "smarter" seems to prevail on the "dumber" (as we see for example with livestock farming, or with the distribution of salaries according to formal and intellectual qualifications etc.) the emergence of programs and AI applications of this level of sophistication brings us to the question "what will AI do to us?". Our error is to frame Artificial Intelligence in the same way we frame Natural Intelligence and mistakenly address it as we would do with biological life, even applying to it the same biopolitical categories. We here try to reverse the common arguments in relationship between life and intelligence and show that our progresses in Artificial Intelligence might have no immediate consequence for the topic of life as such, as life and intelligence are well separated issues, and - even when we would like to somehow fuse them together, we are very far away from having an "Artificial Life" to the degree of complexity and effectiveness we have with "Artificial Intelligence". (shrink)

In their thought-provoking article, Sedlakova and Trachsel (2023) defend the view that the status—both epistemic and ethical—of Conversational Artificial Intelligence (CAI) used in psychotherapy is complicated. While therapeutic CAI seems to be more than a mere tool implementing particular therapeutic techniques, it falls short of being a “digital therapist.” One of the main arguments supporting the latter claim is that even though “the interaction with CAI happens in the course of conversation… the conversation is profoundly different from a conversation with (...) a human therapist” (Sedlakova and Trachsel 2023, 8). In particular, unlike a human therapist, CAI cannot help its users gain new insight and self-understanding (Sedlakova and Trachsel 2023). We agree that currently available therapeutic CAI cannot be considered a “digital therapist,” however, we think that the issue surrounding the acquisition of new self-understanding in the interaction with therapeutic CAI is more complicated than Sedlakova and Trachsel suggest. (shrink)

تُخبرنا النظرية الحاسوبية للعقل (أو مذهب الحوسبة)، أن عقولنا تُشبه الحواسيب في عملها؛ أي أنها تتلقى مدخلات من العالم الخارجي، ثم تُنتج بالخوارزميات مخرجات في شكل حالات ذهنية أو أفعال. وبعبارة أخرى، تذهب النظرية إلى أن الدماغ لا يعدو أن يكون معالج معلومات؛ حيث يكون العقل بمثابة «برمجيات» (سوفت وير) تعمل على «جهاز» هو الدماغ (هارد وير). وما دام العقل مجرد برمجيات تخضع للحوسبة الفيزيائية بواسطة الأدمغة، أليس من الممكن إذن منطقيًا نقلها إلى أي حاسوب مثلما نقوم بنقل أية برمجيات (...) أخرى؟. (shrink)

This paper investigates the responses of GPT-4, a state-of-the-art AI language model, to ten prominent philosophical paradoxes, and evaluates its capacity to reason and make decisions in complex and uncertain situations. In addition to analyzing GPT-4's solutions to the paradoxes, this paper assesses the model's Theory of Mind (ToM) capabilities by testing its understanding of mental states, intentions, and beliefs in scenarios ranging from classic ToM tests to complex, real-world simulations. Through these tests, we gain insight into AI's potential for (...) social reasoning and its capacity for more sophisticated forms of human-AI interaction. The paper also explores the limitations and biases of AI-generated reasoning and its implications for our comprehension of complex philosophical problems. (shrink)

An interview by Alex Thomson of UKColumn on Landgrebe and Smith's book: Why Machines Will Never Rule the World. The subtitle of the book is Artificial Intelligence Without Fear, and the interview begins with the question of the supposedly imminent takeover of one profession or the other by artificial intelligence. Is there truly reason to be afraid that you will lose your job? The interview itself is titled 'Where this is no will there is no way', drawing on one thesis (...) of the book to the effect that there will never be anything like the human will on the part of a machine. This is for mathematical reasons. AI is always and in every case a matter of algorithms; thus of applied mathematics. And algorithms are not the sorts of things that can want or desire or intend to do something. It is the human will of those who work at OpenAI or google or microsoft which have given rise to the new shiny objects of AI (ChatGPT and other Large Language Models). And it is the human will of the users of these shiny objects which gives them the resources and power to work their apparent miracles. (shrink)

It is indubitable that machines with artificial intelligence (AI) will be an essential component in humans’ quest to become a spacefaring civilization. Most would agree that long-distance space travel and the colonization of Mars will not be possible without adequately developed AI. Machines with AI have a normative function, but some argue that it can also be evaluated from the perspective of ethical norms. This essay is based on the assumption that machine ethics is an essential philosophical perspective in realizing (...) the aim of humanity becoming a spacefaring civilization. In this essay, I explore two questions in the field of machine ethics, that I believe to be relevant to the role AI will play in long-distance space travel. The first is, should moral theory be extended to include machines with AI, and second, can machines be fully ethical agents? In this essay, I define AI and then discuss the difference between implicit, explicit and full ethical agents in relation to machines with AI. I then present the argument that the inclusion of moral theory is essential in the development of machines with AI. Without an adequate inclusion of moral theory in the design of AI it may pose an existential threat to humanity, especially in the development of super-intelligent machines. I also highlight that conceptual clarity is essential in the field of machine ethics and the choice of the conceptual foundation that informs AI research and development has ethical implications, especially in the case of super-intelligent machines. This essay is an exploratory and speculative philosophical analysis of certain aspects of machine ethics relevant to long-distance space travel and does not attempt to provide definitive answers to the questions posed in the essay, but instead aims to bring attention to what I deem important considerations. (shrink)

Limits need to be set on AI’s ability to simulate human feelings. Ensuring that chatbots don’t use emotive language, including emojis, would be a good start. Emojis are particularly manipulative. Humans instinctively respond to shapes that look like faces — even cartoonish or schematic ones — and emojis can induce these reactions.

While feminist critiques of AI are increasingly common in the scholarly literature, they are by no means new. Alison Adam’s Artificial Knowing (1998) brought a feminist social and epistemological stance to the analysis of AI, critiquing the symbolic AI systems of her day and proposing constructive alternatives. In this paper, we seek to revisit and renew Adam’s arguments and methodology, exploring their resonances with current feminist concerns and their relevance to contemporary machine learning. Like Adam, we ask how new AI (...) methods could be adapted for feminist purposes and what role new technologies might play in addressing concerns raised by feminist epistemologists and theorists about algorithmic systems. In particular, we highlight distributed and federated learning as providing partial solutions to the power-oriented concerns that have stymied efforts to make machine learning systems more representative and pluralist. (shrink)