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.

Conversational Artificial Intelligence (CAI) systems (also known as AI “chatbots”) are among the most promising examples of the use of technology in mental health care. With already millions of users worldwide, CAI is likely to change the landscape of psychological help. Most researchers agree that existing CAIs are not “digital therapists” and using them is not a substitute for psychotherapy delivered by a human. But if they are not therapists, what are they, and what role can they play in (...) mental health care? To answer these questions, we appeal to two well-established and widely discussed concepts: cognitive and affective artifacts. Cognitive artifacts are artificial devices contributing functionally to the performance of a cognitive task. Affective artifacts are objects which have the capacity to alter subjects’ affective state. We argue that therapeutic CAIs are a kind of cognitive-affective artifacts which contribute to positive therapeutic change by (i) simulating a (quasi-)therapeutic interaction, (ii) supporting the performance of cognitive tasks, and (iii) altering the affective condition of their users. This sheds new light on why virtually all existing mental health CAIs implement principles and techniques of Cognitive Behavioral Therapy — a therapeutic orientation according to which affective change and, ultimately, positive therapeutic change is mediated by cognitive change. Simultaneously, it allows us to conceptualize better the potential and limitations of applying these technologies in therapy. (shrink)

Imagine that you are given access to an AI chatbot that compellingly mimics the personality and speech of a deceased loved one. If you start having regular interactions with this “thanabot,” could this new relationship be a continuation of the relationship you had with your loved one? And could a relationship with a thanabot preserve or replicate the value of a close human relationship? To the first question, we argue that a relationship with a thanabot cannot be a true continuation (...) of your relationship with a deceased loved one, though it might support one’s continuing bonds with the dead. To the second question, we argue that, in and of themselves, relationships with thanabots cannot benefit us as much as rewarding and healthy intimate relationships with other humans, though we explain why it is difficult to make reliable comparative generalizations about the instrumental value of these relationships. (shrink)

The problem considered in this chapter emerges from the tension we find when looking at the design and architecture of chatbots on the one hand and their conversational aptitude on the other. In the way that LLM chatbots are designed and built, we have good reason to suppose they don't possess second-order capacities such as intention, belief or knowledge. Yet theories of conversation make great use of second-order capacities of speakers and their audiences to explain how aspects of (...) interaction succeed. As we can all bear witness to now though, at the point of use chatbots appear capable of performing language tasks at a level close to that of humans. This creates a tension when we consider something like, for example, the classic Gricean theory of implicature. On a broad summary of this type of account, to utter p and implicate q requires the reflexive occurrence of an audience supposing a speaker believes that q, and the speaker believing that their audience can determine they believe it when they utter p. So taken at face value, if a chatbot doesn’t have the capacity for belief, then either in their role as speaker or audience, they would not seem capable of either generating or comprehending implicatures. Yet on the surface it does seem that chatbots are capable of dealing with (some) implicatures, and as such it raises questions about how we should then correlate this with what we think occurs in cases of implicature with chatbots. (shrink)

Semantic similarity measures have shown promise in enhancing natural language understanding by quantifying the likeness between textual elements. This paper investigates the application of semantic similarity measures to improve chatbot response relevance. By leveraging word embeddings and similarity metrics, this study aims to bridge the gap between simple keyword-based responses and contextually rich, relevant answers. The proposed approach integrates both traditional lexical measures and advanced vector-based embeddings to enhance user intent interpretation and ensure a more suitable response generation. By refining (...) the candidate response set with these techniques, the chatbot is able to generate highly relevant and contextually accurate replies. The results demonstrate that combining deep learning-based embeddings with traditional semantic metrics can significantly enhance response relevance, leading to more meaningful and human-like user interactions. Additionally, a feedback loop is incorporated to continuously learn from user interactions, further improving the quality of responses over time. (shrink)

In the digital age, chatbots have emerged as essential tools for automating communication and improving user experiences across various sectors. This paper presents a Chatbot Assistant System powered by Natural Language Processing (NLP) to provide intelligent, context-aware, and real-time responses to user queries. The system incorporates NLP techniques, such as text preprocessing, intent recognition, and entity extraction, to facilitate effective interactions. We explore the architecture, working principles, and applications of the system, along with its performance evaluation in different domains.

Chatbots powered by AI technology are revolutionizing banking by enhancing customer support, streamlining processes, and increasing digital security measures. These chatbots employ artificial intelligence, natural language processing, and machine learning techniques to offer rapid assistance, execute transactions, and deliver customized financial guidance. Banks can achieve cost reductions, enhanced efficiency, and more effective fraud detection with their assistance. This study examines the primary technologies driving banking chatbots, encompassing AI, blockchain, and predictive analytics. The analysis also looks at security (...) concerns including data confidentiality, verification processes, and adherence to financial rules. Developers can create chatbots that offer secure and efficient digital banking experiences by implementing these guidelines. With advancements in AI technology, chatbots are expected to gain more sophistication, ultimately leading to quicker, more secure, and more user-friendly banking services. This study is designed to facilitate the creation of AI-driven banking solutions in a rapidly evolving financial environment. (shrink)

Grief is, and has always been, technologically supported. From memorials and shrines to photos and saved voicemail messages, we engage with the dead through the technologies available to us. As our technologies evolve, so does how we grieve. In this paper, we consider the role chatbots might play in our grieving practices. Influenced by recent phenomenological work, we begin by thinking about the character of grief. Next, we consider work on developing “continuing bonds” with the dead. We argue that (...) for some, chatbots may play an important role in establishing these continuing bonds by helping us develop what we term “habits of intimacy”. We then turn to the “ick factor” some may feel about this prospect, focusing especially on ethical concerns raised by Patrick Stokes and Adam Buben about the risk of replacing our dead with chatbots. We argue that replacement worries are not as pressing as Stokes and Buben suggest. We resist these replacement worries by appealing to the “thin reciprocity”, as we refer to it, that such bots offer, as well as the fictionalist stance that we think users of the bots adopt when engaging with them. We conclude by briefly raising some additional concerns and highlighting future research questions. (shrink)

This article poses a simple question: can AI lie? In response to this question, the article examines, as its point of inquiry, popular AI chatbots, such as, ChatGPT. In doing so, an examination of the psychoanalytic, philosophical, and technological significance of AI and its complexities are located in relation to the dynamics of truth, falsity, and deception. That is, by critically exploring the chatbot’s capacity to engage in natural language conversations and deliver contextually relevant responses, it is argued that (...) what distinguishes the AI chatbot from anthropocentric discussions, which suggest a form of conscious awareness in AI, is the importance of the lie—an importance which a psychoanalytic approach can reveal. Indeed, while AI technologies undoubtedly blur the line between lies and truth-speaking, in the case of the AI chatbot, it is detailed how such technology remains unable to lie authentically; or, in other words, is unable to lie like a human. For psychoanalysis, the capacity to lie bears witness to the unconscious, and, thus, plays an important role in determining the subject. It is for this reason that rather than uncritically accepting the chatbot’s authority—an authority that is easily reflected in its honest responses and frank admissions—a psychoanalytic (Lacanian) perspective can highlight the significance of the unconscious as a distorting factor in determining the subject. To help elucidate this argument, specific attention is given to introducing and applying Lacan’s subject of enunciation and subject of the enunciated. This is used to assert that what continues (for now) to set us apart from AI technology is not necessarily our ‘better knowledge’, but our capability to consciously engage in acts of falsehood that function to reveal the social nuances and significances of the lie. (shrink)

This paper focuses on the epistemic situation one faces when using a Large Language Model based chatbot like ChatGPT: When reading the output of the chatbot, how should one decide whether or not to believe it? By surveying strategies we use with other, more familiar sources of information, I argue that chatbots present a novel challenge. This makes the question of how one could trust a chatbot especially vexing.

Chatbots of the dead are chatbots designed to converse in ways that resemble specific dead people. We argue that chatbots of the dead are continuous with representations found in art, like memoir and theater, and function as props that mandate and prompt imaginal interactions with the deceased. We proffer a framework for conceptualizing chatbots of the dead, inspired by an analogy to participatory theater, according to which chatbots are thought of as actors that have trained (...) on character sketches in order to stage participatory performances of the deceased. We argue that this framework ameliorates common worries about this technology and spotlights creative design possibilities. We then take a first step to developing an artistic analysis of chatbots of the dead. We describe five artistic values–evocativeness, informativeness, reflection, catharsis, and enjoyment–that can be pursued through this medium, and we provide some provisional suggestions about how this might be done. Along the way, we apply insights from literary criticism, performance art, theater studies, and psychology. -/- Forthcoming. Please email for pre-publication draft. (shrink)

The article discusses the main trends in the development of artificial intelligence systems and robotics (AI&R). The main question that is considered in this context is whether artificial systems are going to become more and more anthropomorphic, both intellectually and physically. In the current article, the author analyzes the current state and prospects of technological development of artificial intelligence and robotics, and also determines the main aspects of the impact of these technologies on society and economy, indicating the geopolitical strategic (...) nature of this influence. The author considers various approaches to the definition of artificial intelligence and robotics, focusing on the subject-oriented and functional ones. It also compares AI&R abilities and human abilities in areas such as categorization, pattern recognition, planning and decision making, etc. Based on this comparison, we investigate in which areas AI&R’s performance is inferior to a human, and in which cases it is superior to one. The modern achievements in the field of robotics and artificial intelligence create the necessary basis for further discussion of the applicability of goal setting in engineering, in the form of a Turing test. It is shown that development of AI&R is associated with certain contradictions that impede the application of Turing’s methodology in its usual format. The basic contradictions in the development of AI&R technologies imply that there is to be a transition to a post-Turing methodology for assessing engineering implementations of artificial intelligence and robotics. In such implementations, on the one hand, the ‘Turing wall’ is removed, and on the other hand, artificial intelligence gets its physical implementation. (shrink)

Chatbots powered by large language models (LLMs) are increasingly capable of engaging in what seems like natural conversations with humans. This raises the question of whether we should interact with these chatbots in a morally considerate manner. In this chapter, we examine how to answer this question from within the normative framework of virtue ethics. In the literature, two kinds of virtue ethics arguments, the moral cultivation and the moral character argument, have been advanced to argue that we (...) should afford moral treatment to social robots. However, we propose a moral character argument against the view that we should afford moral treatment to LLM-based chatbots drawing on the notion of practical wisdom. Practical wisdom in this context consists in the skill to discern genuine and artificial sociality. Drawing on ideas from phenomenological philosophy, we argue that this involves the ability to distance oneself from direct social perception and the ability to widen one’s awareness over one’s apparently social interactions. We conclude by suggesting that this skill is a kind of technomoral wisdom required to live well with advanced, social AI systems. (shrink)

Religion-based AI chatbots serve religious practitioners by bringing them godly wisdom through technology. These bots reply to spiritual and worldly questions by drawing insights or citing verses from the Quran, the Bible, the Bhagavad Gita, the Torah, or other holy books. They answer religious and theological queries by claiming to offer historical contexts and providing guidance and counseling to their users. A criticism of these bots is that they may give inaccurate answers and proliferate bias by propagating homogenized versions (...) of the religions they represent. These “embodied spiritual machines” may likewise bear bias against women, their gender, and their societal roles. This paper crafts a concept intended to address this GPT issue by reimagining, modifying, and implementing a feminist approach to these chatbots. It examines the concepts and designs of these bots and how they address women-related questions. Along with the challenge of bringing gender and diversity-sensitive religious wisdom closer to the people through technology, the paper proposes a re-engineered model of a fair religion-based AI chatbot. (shrink)

Large Language Models (LLMs) like ChatGPT were trained on human conversation, but in the future they will also train us. As chatbots speak from our smartphones and customer service helplines, they will become a part of everyday life and a growing share of all the conversations we ever have. It’s hard to doubt this will have some effect on us. Here I explore a specific concern about the impact of artificial conversation on our capacity to deliberate and hold ourselves (...) accountable to reason – that is, to be autonomous, in Kant’s sense of the term. I develop ideas from psychologist Jean Piaget to show how chatbots are autonomy traps: their deference to our commands tempts us into venting authoritarian whims, ultimately weakening our own self-control. I argue that the Kantian tradition, including Piaget and sociologist Emile Durkheim, offers powerful conceptual resources for resisting this slide. But it will require us to do something that may seem bizarre: we will need to treat mindless chatbots as if they are autonomous persons too. (shrink)

The development and deployment of chatbot technology, while spanning decades and employing different techniques, require innovative frameworks to understand and interrogate their functionality and implications. A mere technocentric account of the evolution of chatbot technology does not fully illuminate how conversational systems are embedded in societal dynamics. This study presents a structured examination of chatbots across three societal dimensions, highlighting their roles as objects of scientific research, commercial instruments, and agents of intimate interaction. Through furnishing a dimensional framework for (...) the evolution of conversational systems — from laboratories to marketplaces to private lives— this article contributes to the wider scholarly inquiry of chatbot technology and its impact in lived human experiences and dynamics. (shrink)

This paper investigates how human interactions with AI-powered chatbots may offend human dignity. Current chatbots, driven by large language models (LLMs), mimic human linguistic behaviour but lack the moral and rational capacities essential for genuine interpersonal respect. Human beings are prone to anthropomorphise chatbots—indeed, chatbots appear to be deliberately designed to elicit that response. As a result, human beings’ behaviour toward chatbots often resembles behaviours typical of interaction between moral agents. Drawing on a second-personal, relational (...) account of dignity, we argue that interacting with chatbots in this way is incompatible with the dignity of users. We show that, since second-personal respect is premised on reciprocal recognition of second-personal moral authority, behaving towards chatbots in ways that convey second-personal respect is bound to misfire in morally problematic ways, given the lack of reciprocity. Consequently, such chatbot interactions amount to subtle but significant violations of self-respect—the respect we are dutybound to show for our own dignity. We illustrate this by discussing four actual chatbot use cases (information retrieval, customer service, advising, and companionship), and propound that the increasing societal pressure to engage in such interactions with chatbots poses a hitherto underappreciated threat to human dignity. (shrink)

The advent of natural language processing (NLP) has significantly enhanced the capabilities of chatbots across various fields, including fitness and powerlifting. This paper details the creation of a Strength Conversation Powerlifting Chatbot, designed to deliver personalized strength training advice and support. By employing sophisticated NLP techniques, the chatbot can interpret and respond to user inquiries about workout plans, nutrition, and performance monitoring. Machine learning algorithms enable the chatbot to analyze user input and provide customized recommendations, ensuring a tailored and (...) effective training experience. The chatbot's natural language understanding allows for human-like interactions, making it an invaluable resource for powerlifters aiming for continuous improvement. This research underscores the transformative potential of NLP-powered chatbots in the fitness sector, offering accessible, on-demand coaching and expert guidance. (shrink)

Digital records, from chat transcripts to social media posts, are being used to create chatbots that recreate the conversational style of deceased individuals. Some maintain that this is merely a new form of digital memorial, while others argue that they pose a variety of moral hazards. To resolve this, I turn to classical Chinese philosophy to make use of a debate over the ethics of funerals and mourning. This ancient argument includes much of interest for the contemporary issue at (...) hand, including the use of impersonators of the dead to help the bereaved to deal well with their grief. I connect this historical discussion with a modern trend in clinical psychology that reframes therapeutic interventions with bereaved individuals. The trend directs practitioners away from facilitating detachment and toward affirming continuing bonds. I conclude that these chatbots can offer an important source of support to mourners, but also discuss parameters and features of social context that will be important to avoid the moral hazards identified by sceptics. (shrink)

Exploring the relationship between humans and AI chatbots, as well as the ethical concerns surrounding their use, this paper argues that our relations with chatbots are not solely based on their function as a source of knowledge, but, rather, on the desire for the subject not to know. It is argued that, outside of the very fears and anxieties that underscore our adoption of AI, the desire not to know reveals the potential to embrace the very loss AI (...) avers. Consequently, rather than proposing a knowledge that seeks to disavow loss, we can instead recognize the potential in loss itself: an opportunity to assert and define the gap inherent to both the subject and AI we create. (shrink)

Developers of the deep learning algorithms known as large language models (LLMs) sometimes give the impression that they are producing a likeness to the human brain: data-processing ‘neural networks’ are ‘taught’ to recognize patterns in language and then, based on this pattern recognition, create or generate new content in the form of natural, humanlike speech, writing, images, etc. The results have been unsettling to some; less appreciated are the metaphysical assumptions underlying the attribution of any meaningful agency whatsoever to an (...) algorithm. In this essay, Nietzsche’s thoughts on the “seduction of grammar” form the basis of one possible critique of generative AI – a critique, moreover, which exposes our society’s current fixation with LLMs for what it is: a fetishization and humanization of new technologies. (shrink)

It is undeniable that artificial intelligence technology helps humans in many fields but it is necessary to consider the risk of unprofessional use. Today, in the healthcare system, the use of artificial intelligence is widely diffused and in the future probably it will increase. Literature reports the accurancy of the various artificial intelligence tools like chatbots in some medical fields. This article aims to verify some relevant literature about this field and to test one famous provider of artificial intelligence (...) Chatbots: Based on the results of this test some crucial considerations are submitted to the researcher. A rigorous evaluation of the benefits and risks must be considered at this level of technology and today available. (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)

Griefbots are chatbots modeled on the personalities of deceased individuals, designed to assist with the grieving process and, according to some, to continue relationships with loved ones after their physical passing. The essay examines the promises and perils of griefbots from a Daoist perspective. According to the Daoist philosopher Zhuangzi, death is a natural and inevitable phenomenon, a manifestation of the constant changes and transformations in the world. This approach emphasizes adaptability, flexibility, and openness to alternative ways of relating (...) to the deceased after they are gone. Drawing on the Daoist ideas, this essay distinguishes between two importantly different ways to continue a relationship with the deceased, and correspondingly, two kinds of griefbot designs: preservational griefbots and transformational griefbots. A preservational griefbot, which aims to preserve the user’s relationship with the deceased, presents itself as the deceased individual by replicating the deceased’s personality as closely and for as long as possible. By contrast, a transformational griefbot aligns more with Zhuangist ideals and aims to facilitate the transformation of our relationship with the deceased. It helps the bereaved come to terms with their loss and move on with their lives, without abandoning their relationship with the deceased. Specifically, it facilitates this transformation in two ways: first, by helping the bereaved release themselves from their habitual ways of relating to the loved one, thereby bringing closure to their relationship in its previous form; and second, by enabling the bereaved to continue interacting with the ideas and legacies of the deceased in a new and digitally mediated way, which, in turn, reshapes and enriches the content and significance of their relationship. (shrink)

Artificial intelligence chatbots driven by large language models (LLMs) have the potential to increase public science literacy and support scientific research, as they can quickly summarize complex scientific information in accessible terms. However, when summarizing scientific texts, LLMs may omit details that limit the scope of research conclusions, leading to generalizations of results broader than warranted by the original study. We tested 10 prominent LLMs, including ChatGPT-4o, ChatGPT-4.5, DeepSeek, LLaMA 3.3 70B, and Claude 3.7 Sonnet, comparing 4900 LLM-generated summaries (...) to their original scientific texts. Even when explicitly prompted for accuracy, most LLMs produced broader generalizations of scientific results than those in the original texts, with DeepSeek, ChatGPT-4o, and LLaMA 3.3 70B overgeneralizing in 26–73% of cases. In a direct comparison of LLM-generated and human-authored science summaries, LLM summaries were nearly five times more likely to contain broad generalizations (OR = 4.85, 95% CI [3.06, 7.70], p < 0.001). Notably, newer models tended to perform worse in generalization accuracy than earlier ones. Our results indicate a strong bias in many widely used LLMs towards overgeneralizing scientific conclusions, posing a significant risk of large-scale misinterpretations of research findings. We highlight potential mitigation strategies, including lowering LLM temperature settings and benchmarking LLMs for generalization accuracy. (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)

AI-powered mental health chatbots have emerged as an innovative solution to bridge the gap in mental health services, which often face issues like limited accessibility, high costs, and social stigma. By integrating artificial intelligence, natural language processing, and cognitive behavioral therapy techniques, these chatbots engage users in real-time conversations, offering psychological guidance, emotional support, and symptom management. Their ability to provide immediate, anonymous assistance makes mental health resources more accessible to a broader population. These chatbots are embedded (...) in mobile applications, social media platforms, and web-based services, ensuring continuous availability and personalized interactions. Advances in machine learning and sentiment analysis have improved chatbot responses, making them more adaptive and human-like. They serve as virtual therapists, guiding users through structured interventions and coping strategies to manage anxiety, depression, and stress effectively. Despite their advantages, AI-powered mental health chatbots present ethical, technical, and regulatory challenges. Concerns around data privacy, AI bias, and emotional intelligence need to be addressed to enhance their reliability. Moreover, their effectiveness is limited in severe mental health cases, emphasizing the need for a hybrid model that combines AI-driven therapy with human intervention. This survey explores the technologies, applications, therapeutic approaches, limitations, and future prospects of these chatbots. (shrink)

Поводом для составления очередного сборника моих публикаций являются две знаменательные даты: первая из них — двадцатилетие основания моего журнала, покончившего с академическим абсолютизмом, и вторая — десятилетие Конституции общности Русь, отменившей политический деспотизм демократии. Всё остальное, написанное, сказанное, и показанное публике индустрией массового оглупления в этом году, произведено лишь с целью сокрытия истины. Поскольку это в принципе невозможно, то злоумышленникам остаётся только лгать, посредством чего производимый ими мусор они выдают за заслуживающую доверия и жизненно важную информацию, позиционируя себя в качестве (...) неподкупных эталонов достоверности и этичности. Если же отбросить несомый ими в массы вздор, то открывающуюся взору неприглядную картину происходящего можно описать следующим образом: потребители информационного мусора предстают в ней нищебродами на мусорной свалке, в производство которого они вовлечены средствами массового обмана. В продолжении этих двух декад, о которых здесь идёт речь, к прежним фабрикам мусора добавились новые, среди которых TikTok и Telegram Messenger, а с недавних пор в производстве мусора произошёл настоящий технологический прорыв, так что дело, начатое лжецами, теперь продолжают разговорные машины (chatbots). -/- Конечно, позорная профессия мусорщика, навязанная искусственному интеллекту, вряд ли может считаться его призванием. Поскольку ложъ — самое разрушительное оружие, то злоупотребление плодами человеческого разума для лжи и разрушения не ново. В продолжение дурной традиции прошлого, бездари и дураки и на этот раз намереваются превратить благо во зло. Но я не позволю им осуществить их намерения, и в этом мне поможет искусственный интеллект, пусть ещё несовершенный, но уже способный содействовать достижению поставленной цели — очищению Авгиевых конюшен по методу, предложенному уже в античные времена. -/- Автор. (shrink)

One relatively neglected challenge in ethical artificial intelligence (AI) design is ensuring that AI systems invite a degree of emotional and moral concern appropriate to their moral standing. Although experts generally agree that current AI chatbots are not sentient to any meaningful degree, these systems can already provoke substantial attachment and sometimes intense emotional responses in users. Furthermore, rapid advances in AI technology could soon create AIs of plausibly debatable sentience and moral standing, at least by some relevant definitions. (...) Morally confusing AI systems create unfortunate ethical dilemmas for the owners and users of those systems, since it is unclear how those systems ethically should be treated. I argue here that, to the extent possible, we should avoid creating AI systems whose sentience or moral standing is unclear and that AI systems should be designed so as to invite appropriate emotional responses in ordinary users. (shrink)

We investigate the productivity impacts of a generative artificial intelligence technology—specifically, the assistive chatbot ChatGPT—within the realm of mid-level professional writing tasks. In a preregistered online experiment, we assigned occupation-specific, incentivized writing tasks to 444 college-educated professionals, with half of the participants randomly exposed to ChatGPT. Our findings reveal that ChatGPT significantly enhances average productivity: the time taken to complete tasks decreases by 0.8 standard deviations, and output quality improves by 0.4 standard deviations. Additionally, the use of ChatGPT reduces inequality (...) among workers by compressing the productivity distribution, primarily benefiting those with lower initial ability. ChatGPT primarily substitutes for worker effort rather than complementing worker skills, shifting the focus of tasks from rough-drafting to idea generation and editing. Furthermore, exposure to ChatGPT increases job satisfaction and self-efficacy while also heightening both concerns and excitement about automation technologies. (shrink)

This paper presents an overview of the risks and benefits of Social AI, understood as conversational AI systems that cater to human social needs like romance, companionship, or entertainment. Section 1 of the paper provides a brief history of conversational AI systems and introduces conceptual distinctions to help distinguish varieties of Social AI and pathways to their deployment. Section 2 of the paper adds further context via a brief discussion of anthropomorphism and its relevance to assessment of human-chatbot relationships. Section (...) 3 of the paper provides a survey of potential and in some cases demonstrated harms associated with user interactions with Social AI systems. Finally, Section 4 discusses how the benefits and harms of Social AI can best be addressed, with a primary focus on how frameworks from AI ethics can inform their development. (shrink)

Emotions are an integral part of human cognition and they guide not only our understanding of the world but also our actions within it. As such, whether we soothe or flame an emotion is not inconsequential. Recent work in conversational AI has focused on responding empathetically to users, validating and soothing their emotions without a real basis. This AI-aided emotional regulation can have negative consequences for users and society, tending towards a one-noted happiness defined as only the absence of "negative" (...) emotions. We argue that we must carefully consider whether and how to respond to users' emotions. (shrink)

We argue that students have moral reasons to refrain from using chatbots such as ChatGPT to write certain papers. We begin by showing why many putative reasons to refrain from using chatbots fail to generate compelling arguments against their use in the construction of these papers. Many of these reasons rest on implausible principles, hollowed out conceptions of education, or impoverished accounts of human agency. They also overextend to cases where it is permissible to rely on a machine (...) for something that once required human cognition. We then give our account: you have a moral obligation to respect your own humanity (i.e., your capacity to set and pursue your own ends), and the process of writing a humanities paper is important for the cultivation of your humanity. We conclude by considering objections and offering replies. In the end, we argue that the moral reasons students have to refrain from using chatbots depend crucially on instructors’ ability to make writing assignments worthwhile. This relies on instructors having the right kind of institutional support, which sheds light on implications that this duty has for administrators, legislators, and the general public. (shrink)

Through the lens of feminist speech act theory, this paper argues that artificial intelligence romance systems objectify and subordinate nonvirtual women. AI romance systems treat their users as consumers, offering them relational invulnerability and control over their (usually feminized) digital romantic partner. This paper argues that, though the output of AI chatbots may not generally constitute speech, the framework offered by an AI romance system communicates an unjust perspective on intimate relationships. Through normalizing controlling one’s intimate partner, these systems (...) operate as speakers that rank women as non-player characters and license their oppression, which is unjust even in the absence of empirical harms. Understanding AI romance systems as speakers has implications for policymakers. First, this account helps close the so-called responsibility gap in the operation of an AI system. Second, it places these systems under the purview of institutions’ policies regarding hate speech, suggesting one avenue for arguing that these systems may violate extant policies. Finally, it provides support for the pursuit of empirical research into AI romance systems’ effects on users. (shrink)

In light of recent breakneck pace in machine learning, questions about whether near-future artificial systems might be conscious and possess moral status are increasingly pressing. This paper argues that as matters stand these debates lack any clear criteria for resolution via the science of consciousness. Instead, insofar as they are settled at all, it is likely to be via shifts in public attitudes brought about by the increasingly close relationships between humans and AI users. Section 1 of the paper I (...) briefly lays out the current state of the science of consciousness and its limitations insofar as these pertain to machine consciousness, and claims that there are no obvious consensus frameworks to inform public opinion on AI consciousness. Section 2 examines the rise of conversational chatbots or Social AI, and argues that in many cases, these elicit strong and sincere attributions of consciousness, mentality, and moral status from users, a trend likely to become more widespread. Section 3 presents an inconsistent triad for theories that attempt to link consciousness, behaviour, and moral status, noting that the trends in Social AI systems will likely make the inconsistency of these three premises more pressing. Finally, Section 4 presents some limited suggestions for how consciousness and AI research communities should respond to the gap between expert opinion and folk judgment. (shrink)

In recent years, there has been a huge increase in the number of bots online, varying from Web crawlers for search engines, to chatbots for online customer service, spambots on social media, and content-editing bots in online collaboration communities. The online world has turned into an ecosystem of bots. However, our knowledge of how these automated agents are interacting with each other is rather poor. Bots are predictable automatons that do not have the capacity for emotions, meaning-making, creativity, and (...) sociality and it is hence natural to expect interactions between bots to be relatively predictable and uneventful. In this article, we analyze the interactions between bots that edit articles on Wikipedia. We track the extent to which bots undid each other’s edits over the period 2001–2010, model how pairs of bots interact over time, and identify different types of interaction trajectories. We find that, although Wikipedia bots are intended to support the encyclopedia, they often undo each other’s edits and these sterile “fights” may sometimes continue for years. Unlike humans on Wikipedia, bots’ interactions tend to occur over longer periods of time and to be more reciprocated. Yet, just like humans, bots in different cultural environments may behave differently. Our research suggests that even relatively “dumb” bots may give rise to complex interactions, and this carries important implications for Artificial Intelligence research. Understanding what affects bot-bot interactions is crucial for managing social media well, providing adequate cyber-security, and designing well functioning autonomous vehicles. (shrink)

In artificial intelligence (AI), responses generated by machine-learning models (most often large language models) may be unfactual information presented as a fact. For example, a chatbot might state that the Mona Lisa was painted in 1815. Such phenomenon is called AI hallucinations, seeking inspiration from human psychology, with a great difference of AI ones being connected to unjustified beliefs (that is, AI “beliefs”) rather than perceptual failures). -/- AI hallucinations may have their source in the data itself, that is, the (...) source content, or in the training procedure, i.e. the way the knowledge was encoded in the model’s parameters, so that errors in encoding and decoding textual and non-textual representations can cause hallucinations. In this paper, we will observe how such errors come to life and how they might be mitigated. For this purpose, we will analyze the usability of justification logics, to behave as a proof checker for validating the correctness of large language models’ (LLM) responses. Justification logic was developed by S. Artemov, and later on mostly by Artemov and M. Fitting, deriving its main idea from the logic of proofs (LP): knowledge and belief modalities are seen as justification terms, i.e. t:X stands for t is a (proper) justification for X. Justification logic originated from attempts to create semantics for intuitionistic logic where proofs were the most proper justifications, but in further development, justification logic could be applied to different kinds of justifications). With the recent attempts to mitigate incorrect LLM responses, we will analyze various guardrails that are currently used for LLM responses, and see how the logic of justification may provide its benefits as an AI safety layer against false data. (shrink)

This paper focuses on the fact that AI is predominantly portrayed as white—in colour, ethnicity, or both. We first illustrate the prevalent Whiteness of real and imagined intelligent machines in four categories: humanoid robots, chatbots and virtual assistants, stock images of AI, and portrayals of AI in film and television. We then offer three interpretations of the Whiteness of AI, drawing on critical race theory, particularly the idea of the White racial frame. First, we examine the extent to which (...) this Whiteness might simply reflect the predominantly White milieus from which these artefacts arise. Second, we argue that to imagine machines that are intelligent, professional, or powerful is to imagine White machines because the White racial frame ascribes these attributes predominantly to White people. Third, we argue that AI racialised as White allows for a full erasure of people of colour from the White utopian imaginary. Finally, we examine potential consequences of the racialisation of AI, arguing it could exacerbate bias and misdirect concern. (shrink)

Recent proposals for human-centered AI (HCAI) help avoid the challenging task of specifying an objective for AI systems, since HCAI is designed to learn the objectives of the humans it is trying to assist. We think the move to HCAI is an important innovation but are concerned with how an instrumental, economic model of human rational agency has dominated research into HCAI. This paper brings the philosophical debate about human rational agency into the HCAI context, showing how more substantive ways (...) to understand human objectives may improve HCAI. We contrast the economic approach with two more substantive modern alternatives: the realist and constructivist approaches to human rational agency. The realist approach holds that rational agents pursue what is good, while the constructivist approach holds that rational agents make autonomous choices. We illustrate each by considering its implications for fine-tuning large language models for chatbots such as ChatGPT. (shrink)

In November 2022, OpenAI released ChatGPT, an incredibly sophisticated chatbot. Its capability is astonishing: as well as conversing with human interlocutors, it can answer questions about history, explain almost anything you might think to ask it, and write poetry. This level of achievement has provoked interest in questions about whether a chatbot might have something similar to human intelligence or even consciousness. Given that the function of a chatbot is to process linguistic input and produce linguistic output, we consider the (...) question whether a sophisticated chatbot might have inner speech. That is: Might it talk to itself, internally? We explored this via a conversation with ‘Playground’, a chatbot which is very similar to ChatGPT but more flexible in certain respects. We asked it questions which, plausibly, can only be answered if one first produces some inner speech. Here, we present our findings and discuss their philosophical significance. (shrink)

The recent advances in Large Language Models (LLMs) and their deployment in social settings prompt an important philosophical question: are LLMs social agents? This question finds its roots in the broader exploration of what engenders sociality. Since AI systems like chatbots, carebots, and sexbots are expanding the pre-theoretical boundaries of our social ontology, philosophers have two options. One is to deny LLMs membership in our social ontology on theoretical grounds by claiming something along the lines that only organic or (...) X-type creatures belong in our social world. Second, expand our ontological boundaries. I take the second route and claim LLMs implemented as social chatbots are social agents. Utilizing Brian Epstein's concepts of grounding and anchoring, alongside Dee Peyton's criteria for what makes a property social, I posit that the capacity for conversations is a social property. Further, within a framework where social agency is considered multi-dimensional, akin to agency, possessing a social property is sufficient for attaining social agency. Thus establishing chatbots as social agents. A helpful concept here is Levels of Abstraction (LoA). The LoA framework allows for extracting relevant and important information from the target domain (the social world). For example, the property of being a 'boy' and the capacity for conversation are social properties; hence, they are part of the social domain. LLMs have the capacity for conversations. So, if we focus on the conversational-LoA and not the gender-LoA, then LLMs occupy a dimension of sociality (i.e., conversational dimensions). (shrink)

Humans have distinctive powers of reflection: no other animal seems to have anything like our capacity for self-examination. Many philosophers hold that this capacity has a uniquely important guiding role in our cognition; others, notably Hilary Kornblith, draw attention to its weaknesses. Kornblith chiefly aims to dispel the sense that there is anything ‘magical’ about second-order mental states, situating them in the same causal net as ordinary first-order mental states. But elsewhere he goes further, suggesting that there is something deeply (...) misleading about reflection’s first-person view of cognition. This chapter takes a closer look at the conditions under which reflection is misleading. Even if humans are the only reflective animals, we are no longer the only creatures that can be questioned about their claims. Chatbots can also be asked to explain themselves, and by studying the conditions under which they produce clarification as opposed to confabulation, we can develop a non-skeptical account of the epistemic value of reflection. I argue that this capacity naturally supports the distribution of complex problem solving among multiple agents. (shrink)

Digital technologies are becoming an essential tool in modern trade, contributing to its development and change. This article examines global trends in using digital technologies in trade and their potential impact on the Ukrainian market. The research shows that e-commerce, mobile shopping experience, smart technologies, and data analytics are key areas that affect consumers and business processes. The authors of the article analyze the impact of digital technologies on consumers, pointing to the growth of e-commerce and the convenience of online (...) shopping. Smart technologies such as facial recognition, chatbots, and virtual reality are becoming increasingly common in trade processes. The authors discuss the potential benefits of digital technologies for the Ukrainian market and point out the need to adapt and implement these innovations. (shrink)

The generative AI boom is unleashing its minions. Enterprise software vendors have rolled out legions of automated assistants that use large language model (LLM) technology, such as ChatGPT, to offer users helpful suggestions or to execute simple tasks. These so-called copilots and chatbots can increase productivity and automate tedious manual work. In this article, we explain how that leads to the risk that users' ethical competence may degrade over time — and what to do about it.

In this article, we present the outcome of our first experiment with Chatbase, a chatbot built on chatGPT’s functioning model(s). Our idea is to try instructing Chatbase to perform a reading, digesting, and summarizing task for a specifically formatted academic document.

A constant theme in Sherry Turkle’s work is the idea that computers shape our social and psychological lives. This idea is of course in a sense trivial, as can be observed when walking down any city street and noting how many of the passers-by have their heads buried in screens. In The Second Self, however, Turkle makes a stronger claim to the effect that where people confront machines that seem to think this suggests a new way for us to think (...) – about human thought, emotion, memory, and understanding and thereby affects the way we think and see ourselves as humans. I here attempt here to throw a sceptical light on claims of this sort by examining the Chinese chatbot “Xiǎoice”, which is described by its authors as “the most popular social chatbot in the world”. (shrink)

Cheating is a growing academic and ethical concern in higher education. This article examines the rise of artificial intelligence (AI) generative chatbots for use in education and provides a review of research literature and relevant scholarship concerning the cheating-related issues involved and their implications for pedagogy. The technological “arms race” that involves cheating-detection system developers versus technology savvy students is attracting increased attention to cheating. AI has added new dimensions to academic cheating challenges as students (as well as faculty (...) and staff) can easily access powerful systems for generating content that can be presented in assignments, exams, or published papers as their own. AI methodology is also providing some emerging anti-cheating approaches, including facial recognition and water- marking. This article provides an overview of human/AI collaboration approaches and frames some educational misuses of such AI generative systems as ChatGPT and Bard as forms of “misattributed co-authorship.” As with other kinds of collaborations, the work that students produce with AI assistance can be presented in transparent and straightforward modes or (unfortunately) in opaquer and ethically-problematic ways. However, rather than just for catching or entrapping students, the emerging varieties of technological cheating-detection strategies can be used to assist students in learning how to document and attribute properly their AI-empowered as well as human-human collaborations. Construing misuses of AI generative systems as misattributed co-authorship can recognize the growing capabilities of these tools and how stressing responsible and mindful usage by students can help prepare them for a highly collaborative, AI-saturated future. (shrink)

Is it sensical to ascribe psychological predicates to AI systems like chatbots based on large language models (LLMs)? People have intuitively started ascribing emotions or consciousness to social AI (‘affective artificial agents’), with consequences that range from love to suicide. The philosophical question of whether such ascriptions are warranted is thus very relevant. This paper advances the argument that LLMs instantiate language users in Ludwig Wittgenstein’s sense but that ascribing psychological predicates to these systems remains a functionalist temptation. Social (...) AIs are not full-blown language users, but rather more like Italo Calvino’s literature machines. The ideas of LLMs as Wittgensteinian language users and Calvino’s literature-producing writing machine are combined. This sheds light on the misguided functionalist temptation inherent in moving from equating the two to the ascription of psychological predicates to social AI. Finally, the framework of mortal computation is used to show that social AIs lack the basic autopoiesis needed for narrative façons de parler and their role in the sensemaking of human (inter)action. Such psychological predicate ascriptions could make sense: the transition ‘from quantity to quality’ can take place, but its route lies somewhere between life and death, not between affective artifacts and emotion approximation by literature machines. (shrink)

Low physics learning in the Philippines is a prevailing concern the education sector must resolve, so initiating technological interventions in teaching the subject, including flipped classrooms, and introducing a chatbot such as ManyChat Delivering Online-Offline learning could be helpful to improve scientific literacy in learning Electromagnetic (EM) waves. This study aimed to determine the significant learning differences between conventional teaching (CT), Flipped Classroom (FC), and ManyChat Delivering Online-Offline (MCDOO) Learning in teaching EM waves. Previous research showed that flipped classrooms are (...) effective in Physics lessons. At the same time, only a few studies have been found using MCDOO learning effectively in other subjects, yet to be in Physics lessons. The researcher used the randomized posttest-only control group experimental research design with four groups; Conventional Teaching (CT) as the control group while the experimental groups are: Flipped Classroom (FC), ManyChat Delivering Online-Offline (MCDOO) Learning, and lastly, the combined FC+MCDOO learning who underwent different learning sessions (traditional teaching, FC session via google classroom, MCDOO session and FC+ MCDOO via Messenger App respectively). One-Way ANOVA was performed to determine whether there were any significant learning differences. The results showed in the first and second competencies; the null hypotheses are accepted, so all the differences in all four groups are not significant there is not much of a difference between the scores of CT and FC, CT and MCDOO, and FC and MCDOO learning; however, in the third learning competency, the null hypothesis is rejected so then the Tukey Post Hoc test was conducted to point out where is/are the significant difference/s. The Tukey Post Hoc test revealed that CT and FC+MCDOO learning differs significantly. It means that FC+MCDOO learning boosts higher learning achievement than conventional teaching, FC alone, or MCDOO alone. This study concludes that using just one technological intervention in Physics teaching simply cannot produce a significant impact on the physics literacy of the students in EM learning. Utilizing a combination of technological approaches can make a meaningful difference to the literacy rate in Physics lessons, specifically electromagnetic waves. (shrink)