AI & Society最新文献

This study examines the ethical, legal, and copyright challenges in training generative AI on a large-scale text dataset, using Books3 as a case study. This dataset, used for training foundation models such as GPT, BERT, Meta’s Llama, and StableLM includes pirated works by nearly 200,000 authors from various countries, raising concerns about intellectual property rights, dataset integrity, and transparency. Our analysis of the initial 99 ISBNs reveals significant biases, including linguistic imbalance, genre skew, and temporal limitations. AI similarity analysis shows that AI-generated text closely mirrors human-written content, suggesting that AI reconstructs patterns in words rather than copying verbatim. However, some parts of the analysis also indicate that AI outputs frequently paraphrase existing content rather than generating wholly independent text, complicating issues of copyright compliance and economic compensation for authors and publishers. These findings highlight the need for improved dataset transparency, ethical considerations, and legal safeguards in generative AI training. We propose a scalable hybrid governance framework integrating technical design-based solutions with regulatory and institutional strategies to ensure responsible AI development. This approach advances AI governance by addressing dataset integrity, source attribution, and evolving ethical, legal and economic challenges in an increasingly AI-driven society.

Toxic speech on online civic engagement platforms (CEPs) disproportionately affects marginalized groups and threatens the diversity of citizen voices. However, the deployment of AI-driven toxic speech detection (TSD) tools for CEPs faces complex challenges from legal, psychological, and technical perspectives that remain insufficiently explored. We present a first-of-its-kind interdisciplinary review of these challenges, focusing on the explainability of TSD systems, their compliance with European legal standards and offer a roadmap for ethical deployment. Our review reveals three main findings. First, although transparency in AI decision-making is necessary from both legal and psychological perspectives, assessing the explainability of AI-driven TSD tools, and their compliance with legal regulations within Europe, remains a significant challenge. Second, current explainability approaches, ranging from toxic span identification to advanced explainable AI methods, lack standardized metrics. This makes it difficult to assess their reliability and appropriateness for CEPs. Third, despite the importance of TSD, frameworks and best practices for CEPs are still lacking in existing literature. This paper aims to fill this gap by providing a holistic perspective on the challenges and solutions for TSD deployment. It provides the foundation for collaborative efforts to develop and standardize metrics, evaluation protocols, and best practices that can ensure AI decisions in CEPs are transparent, accountable, and aligned with users’ needs.

The cultural weaponization of shame surrounding the use of artificial intelligence (AI) tools like ChatGPT often redirects ethical scrutiny away from systemic concerns and toward individual users. Drawing on Sara Ahmed’s affect theory, this paper argues that cultural narratives of "AI shaming" function as moral displacement that redirects scrutiny away from the environmental costs, exploitative labor practices, and corporate monopolization defining contemporary AI development. The analysis examines how shame operates across academic and professional settings to create "effort anxiety" that demands both visible human labor and accelerated productivity. Current discourse treats AI use as a personal virtue problem and obscures the carbon-intensive data centers, underpaid content moderators, and proprietary knowledge systems that enable these technologies. Instead of eliminating shame, the paper proposes redirecting it toward collective accountability for AI’s systemic harms. Environmental degradation, algorithmic bias, and extractive infrastructures represent the true ethical frontier of artificial intelligence. Policy frameworks, educational interventions, and governance structures offer pathways for transforming shame from individual punishment into institutional reform. The stakes extend beyond AI itself: as emerging technologies reshape society, the patterns of moral responsibility established now will determine whether innovation serves collective flourishing or perpetuates existing inequalities. Shame can become a vehicle for institutional critique and systemic accountability if we redirect its focus from individual users to the powerful corporations, governance structures, and infrastructural systems that profit from AI’s rapid expansion.

The Belmont Report has a place of great importance in American biomedical research ethics. This paper argues that a similar kind of report, and the legal infrastructure that birthed it, is needed in the United States if we are to preempt a great many of the potential issues that are on the horizon with artificial intelligence (AI). What makes the Belmont Report so important is not just that it established a new basis for how medical professionals ought to treat their patients and experiment participants; it did so with the force of law. Establishing an equivalent legal framework for AI is going to take tremendous buy-in from a variety of private and public actors in the United States. The model afforded by the Belmont Report is well suited to generate such buy-in. While this may seem like a daunting task given various polarizing issues at play in society today, the context that produced the Belmont Report was quite fractious itself. It is the position of this paper that a similarly styled approach to AI regulation can succeed in proactively limiting the harms of AI’s use (and abuse).

With the rapid advances in science and technology, the introduction of Artificial Intelligence in the workplace enhanced information processing and identification of data patterns. At the same time, this is accompanied by an increasing trend of worker displacement with the promise of higher profitability and work efficiency. In the Philippines alone, 36% of jobs are predicted to replace human workers with A.I. machines. On the other hand, John Paul II’s social encyclical, Laborem exercens understands work as any activity by humans, both manual and intellectual, by which one earns his daily necessities and contributes to the unceasing elevation of the cultural and moral level of the society. Hence, it is always faced with fresh fears and threats that are connected with dimension of human existence. For this, the encyclical emphasizes on the priority of labor over capital in the face of technological advancements that are in conflict of the authentic meaning of work. This study aims to propose an ethical framework for A.I. adoption in workplace through the encyclical Laborem exercens.

The metaverse, enabled by technologies, such as virtual reality (VR), augmented reality (AR), and artificial intelligence (AI), challenges our traditional understanding of reality, identity, and corporeality. It offers immersive virtual experiences that blur the lines between the real and the synthetic, creating new opportunities for human interaction, expression, and self-exploration. In this paper, we explore (i) the technological advancements driving the development of the metaverse and its potential applications across various sectors; (ii) the avatar concept, a digital representation of oneself within the metaverse, and its implications for identity and presence; and (iii) the profound impact on our perception and understanding of reality and the complex philosophical and ethical questions it raises. The metaverse stands as a novel frontier in human experience, presenting both opportunities and challenges. It demands a critical reassessment of how we perceive embodiment, awareness, and identity in this digital era. While we embrace its potential to expand human capabilities, we must remain mindful of its risks and ensure its ethical and responsible social integration.

This paper investigates the impact of Large Language Model (LLM)-assisted writing on reflective thinking, building on existing adaptations of Albert Borgmann’s device paradigm to Don Ihde’s postphenomenology. Academic writing can facilitate engagement with our beliefs and pre-judgments, making it highly conducive to reflective thinking. However, generative AI tools, such as OpenAI’s ChatGPT and Microsoft Word Copilot, may undermine such meaningful engagement as they ‘disburden’ users of the effort inherent in reflective writing. Still, we fall short when we leave unexamined the kinds of uses each writing app inclines its users to pursue. Despite using the same LLM, a cross-comparison reveals that the user interface (UI) design of ChatGPT and Word Copilot affords distinct forms of interaction: ChatGPT’s UI design may, in principle, facilitate reflective engagement through conversational interactions, prompting users to formulate and engage with their beliefs on a given topic. In contrast, Word Copilot emphasizes automated document production, making a similar kind of engaging use unviable. As a conceptual basis for the argument, this paper extends Ihde’s history of writing ‘technics’ and brings it together with recent conceptual developments in postphenomenology by discussing the apps in terms of ‘quasi-materiality’ of application UIs and the affordances they offer as part of ‘multistabilities’. This paper concludes with a call for academic writers to critically assess how their tools mediate academic writing and thinking processes, arguing that choosing a writing tool for academic writing has ceased to be a matter of personal preference and has become one of academic ethos.

The use of Artificial Intelligence (AI) in art is increasingly widespread, and yet its effects are still far from being clear. The professional boundaries between the artist and the curator, particularly the “curator-as-artist” organizing thematic exhibitions are increasingly blurred, and AI complicates their division of labor even further. By performing theoretical analysis and case study, this paper examines how AI’s recent entry into the artistic realm is impacting the role of the curator-as-artist using the theoretical framework offered by Rossen Ventzislavov’s essay “Idle Arts: Reconsidering the Curator” (2014). Drawing upon the curatorial studies literature, it revisits the two central themes explored by Ventzislavov, i.e., the generation of artistic value, and the division labor between the artist and the curator. Using the theoretical instruments obtained from this examination, it comparatively analyzes two AI-based curatorial projects, namely the Helsinki Biennial titled “New Directions May Emerge” hosted by the Helsinki Art Museum and “Act as if you are a curator: an AI-generated exhibition” at Duke University’s Nasher Museum of Art. The study finds that the art worlds emerging around AI are collaborative and interdisciplinary, and despite the growing pressure on the figure of the individual curator in charge, AI assemblages still rely on human curatorial agency and authorship, especially for curatorial storytelling and the mediation of the negotiations among the human participants in the cooperative network.

This study investigates the multifaceted impacts of artificial intelligence (AI) on organizational management within international companies operating in Tunisia. Using a quantitative methodology, data were collected from 270 managers and employees across diverse enterprises. The findings reveal that AI significantly enhances managerial tasks by automating repetitive processes and fostering autonomy, confirming its role as an optimization tool. Interestingly, AI positively influences interpersonal relationships, exceeding initial expectations of potential negative impacts. However, its effects on organizational culture are negligible. Ethical and legal concerns, though significant, remain moderate, reflecting growing awareness of challenges related to data protection and decision-making transparency. These results highlight the nuanced role of AI in shaping managerial practices and emphasize the need for organizations to adopt balanced strategies that leverage its benefits while addressing its ethical and relational implications. Future research should focus on longitudinal studies to explore AI’s long-term effects and examine contextual factors influencing its integration into diverse organizational settings.