Laura Gorrieri

The central claim of this paper is that chatbots are, in an important sense, deceptive technologies; this deception, however, is not always morally problematic. It becomes so when it is about the relationship the user has with the bot. Since our relationship with chatbots is built solely on dialogue, the ethics of dialogue can offer a privileged lens for discerning harmless and harmful deception. It emerges that the ethical issue with chatbot interactions does not lie in users being deceived about the system’s nature, i.e., thinking it is human, but rather in being misled about the quality of the dialogue they are engaging in. In particular, it is morally bad when chatbots deceive the user about the mutual openness of their interaction, as such systems are inherently incapable of reciprocating. Not all chatbots engage in harmful deception: some remain within the boundaries of what is known as banal deception. Broadly speaking, it is possible to distinguish between two categories of bots: instrumental chatbots, which are task-driven and engage only in banal deception, and simulated-behaviour chatbots, which may engage in harmful deception. In this latter category, AI companions are always morally problematic as they intentionally deceive users about the mutuality of the relationship. Based on this diagnosis, some normative prescriptions are advanced to regulate AI companions and ensure they foster only banal deception. The paper contributes both conceptual clarity to the ethics of AI deception and practical guidance for the responsible design and governance of chatbot technologies.

The second edition of the BEWARE workshop, co-located with the AIxIA 2023 conference, was held in Rome on November 6, 2023. The workshop focused on the emerging ethical aspects of AI, particularly addressing Bias, Risk, Explainability, and the role of Logic and Logic Programming. The event brought together a diverse group of researchers and practitioners to discuss and explore solutions for ethical decision-making in AI. This year, the workshop saw significant participation, with 9 accepted high-quality submis- sions, 5 as long papers and 4 as short papers (out of a total of 11 submitted papers), rigorously peer-reviewed by our Program Committee.

Distributional Semantic Models (DSM) that represent words as vectors of weights over a high dimensional feature space have proved very effective in representing semantic or syntactic word similarity. For certain tasks however it is important to represent contrasting aspects such as polarity, opposite senses or idiomatic use of words. We present a method for computing discriminative word embeddings can be used in sentiment classification or any other task where one needs to discriminate between contrasting semantic aspects. We present an experiment in the identification of reports on natural disasters in tweets by means of these embeddings.

[ENG BELOW] La prima parte ripercorre la storia dell’intelligenza artificiale come disciplina e cerca di fornirne una definizione. La seconda esamina alcuni problemi e difficoltà che si presentano nel settore. Nell’ultima parte si considerano alcune questioni etiche specifiche come la tutela della privacy, i bias, l’explainability, la fairness, i crowdworker, l’umanizzazione degli assistenti virtuali e alcuni problemi posti dalle applicazioni in ambito medico. The first part is about the history of artificial intelligence as a discipline and attempts to provide a definition. The second one examines some problems and difficulties of the field. In the last part some specific ethical issues are considered such as privacy, bias, explainability, fairness, crowdworker, humanization of virtual assistants, and some problems concerning applications in the medical field.

It is undeniable that conversational agents took the world by storm. Chatbots such as ChatGPT (Generative Pre Trained) are used for translations, financial advice, and even as therapists, by millions of users every month. When interacting with technology it’s important to be careful, especially if we do so by using natural language, since our relationship with artificial agents is shaped by the technology’s features and the manufacturer's goal. The paper, organized into three sections, explores the question of whether ChatGPT’s production can be described as ‘bullshit’. In the first section, the focus is on ChatGPT’s architecture and development; in the second a new formulation of the concept of Frankfurt’s ‘bullshit’ is presented, in which its central features of indifference, deception and manipulation are highlighted; in the last section, the title question is tackled, proposing an affirmative answer to it, arguing that ChatGPT can be considered a ‘bullshit’ generator.

Aylsworth and Castro’s recent paper, Should I Use ChatGPT to Write My Papers?, argues that students in the humanities have a moral obligation to refrain from using AI tools such as ChatGPT for writing assignments. Their claim is that writing is an autonomy-fostering activity, essential for intellectual growth and critical reflection, and that every agent has the moral duty to respect their own autonomy. While the authors raise significant ethical concerns, the paper lacks the identification of which specific features of writing promote autonomy. Writing is a multifaceted process which involves distinct activities, not all of which equally contribute to fostering autonomy. Furthermore, writing can be a shared endeavour, for example when co-authoring a paper with a colleague. This prompts the question of which features or activities exactly are the ones to support autonomy. The usage in AI in education is a double-edged sword: while some scholars emphasize the limitations of generated texts, others have reported positive outcomes from using AI tools in educational settings. This invites a deeper examination of how technology can be designed or used to support, rather than mine, students’ autonomy.