Siavosh Sahebi

Recent advances in Generative AI (GenAI) are transforming creative industries, raising urgent ethical questions. This paper explores the impacts of GenAI on the meaningfulness of work for specialist, embedded, and support creatives. By integrating Amabile’s componential model of creativity with a holistic framework of meaningful work, we explore five dimensions of meaningfulness: task integrity, skill cultivation, task significance, autonomy, and belongingness. We emphasize the dual impacts of GenAI, highlighting positive potential for democratization and task consolidation in creative work, alongside negative threats of deskilling, autonomy erosion, and worker isolation. We outline structural implications of our analysis, including the shift of creative practice from creation to curation, the emergence of a penalty for AI use, and rising professional precarity. We conclude by arguing that accurate ethical assessment requires recognizing the complex interdependencies between these dimensions, as the various impacts of AI can cascade, reinforce, and compensate for one another.

Much Large Language Model (LLM) use is happening undisclosed. This is troubling because the undisclosed use of LLMs to mediate interpersonal communication can be a morally objectionable kind of deception. It is morally objectionable when the intentional failure to disclose LLM use deprives others of valuable information they need for their epistemic appraisal of, inter alia, another’s authentic competencies. In this paper, we provide an analysis of deception in the context of undisclosed LLM use and argue that in some cases this amounts to an ethically salient epistemic wrongdoing.

As Artificial Intelligence is increasingly employed to mediate human interactions, there is uncertainty around how these technologies impact human behaviour and how such mediated interactions are perceived. One such case is the expanding use of Generative Artificial Intelligence (GenAI) to create and disseminate content across a range of media contexts, known as AI-Mediated Communication (AI-MC). Such cases raise important questions about how those on the receiving end of such outputs respond to these new forms of interpersonal communications. Based on evaluating a piece of written text, this study explores how recipients' perceptions of the content creator's trustworthiness and authenticity, alongside their willingness to use the content (via knowledge uptake) are impacted by author type (human, AI-assisted, or fully AI) and media context (workplace email vs. social media post). We conducted a pre-registered experimental survey study with a 3 (author type) x 2 (media context) between-subjects factorial design (N = 547) where participants evaluated a piece of written text. Our findings demonstrate an “AI penalty”, with communication involving AI being perceived as less trustworthy, less authentic, and less useful for knowledge uptake. We also identify a “disclosure paradox”, as while participants believed it is important to disclose AI use, they also penalise the communication when such disclosure is made, which risks creating perverse incentives for non-disclosure of GenAI use for written communications.

The use of large language models (LLMs) for companionship is rapidly increasing. As “friends”, LLMs act as scaffolds to the development and enactment of our ongoing comportment, not only with them but also in the broader environment in which we are embedded. From the perspective of scaffolding as it is understood in the philosophy of cognitive science literature, we will argue that LLMs qua “friends” are proving to be damaging to the overall interests of the users who engage with them. We will show that this generally manifests in terms of a growth in the addictive dependency those agents have on LLM-“friends”, as well as the social and moral deskilling they risk suffering, alongside the social demotivation they are likely to experience. Furthermore, we will contend that LLM-“friends” are not only harmful but hostile, whereby the overall interests of the agent utilising the LLM are undermined whilst those of another agent, who was actively involved in the establishment and refinement of the technology, are furthered. This “other agent” is the corporate body that is actively exploiting and profiting from the social difficulties many are currently facing, which are evinced most markedly by reports of rampant global loneliness.

The rapid adoption of commercial Generative Artificial Intelligence (Gen AI) products raises important questions around the impact this technology will have on our communicative interactions. This paper provides an analysis of some of the potential implications that Artificial Intelligence-Mediated Communication (AI-MC) may have on epistemic trust in online communications, specifically on social media. We argue that AI-MC poses a risk to epistemic trust being diminished in online communications on both normative and descriptive grounds. Descriptively, AI-MC seems to (roughly) lower levels of epistemic trust. Normatively, we argue that this brings about the following dilemma. On the one hand, there are at least some instances where we should epistemically trust AI-MC less, and therefore the reduction in epistemic trust is justified in these instances. On the other hand, there are also instances where we epistemically trust AI-MC less, but this reduction in epistemic trust is not justified, resulting in discrimination and epistemic injustice in these instances. The difficulty in knowing which of these two groups any instance of AI-MC belongs to brings about the AI-MC dilemma: We must choose between maintaining normal levels of epistemic trust and risking epistemic gullibility when reduced trust is justified, or adopting generally reduced epistemic trust and risking epistemic injustice when such reduced trust is unjustified. Navigating this choice between problematic alternatives creates a significant challenge for social media as an epistemic environment.

This paper provides a philosophically informed and robust account of the global justice implications of Artificial Intelligence (AI). We first discuss some of the key theories of global justice, before justifying our focus on the Capabilities Approach as a useful framework for understanding the context-specific impacts of AI on low- to middle-income countries. We then highlight some of the harms and burdens facing low- to middle-income countries within the context of both AI use and the AI supply chain, by analyzing the extraction of _materials_, which includes mineral extraction and the environmental harms associated with it, and the extraction of _labor_, which includes unethical labor practices, low wages, and the trauma experienced by some AI workers. We then outline some of the potential harms and benefits that AI poses, how these are distributed, and what global justice implications this has for low- to middle-income countries. Finally, we articulate the global justice significance of AI by utilizing the Capabilities Approach. We argue that AI must be considered from a global justice perspective given that, globally, AI puts significant downward pressure on several elements of well-being thereby making it harder for people to achieve threshold levels of the central human capabilities needed for a life of dignity.

This paper provides a philosophically informed and robust account of the global justice implications of Artificial Intelligence (AI). We first discuss some of the key theories of global justice, before justifying our focus on the Capabilities Approach as a useful framework for understanding the context-specific impacts of AI on lowto middle-income countries. We then highlight some of the harms and burdens facing low- to middle-income countries within the context of both AI use and the AI supply chain, by analyzing the extraction of materials, which includes mineral extraction and the environmental harms associated with it, and the extraction of labor, which includes unethical labor practices, low wages, and the trauma experienced by some AI workers. We then outline some of the potential harms and benefits that AI poses, how these are distributed, and what global justice implications this has for low- to middle-income countries. Finally, we articulate the global justice significance of AI by utilizing the Capabilities Approach. We argue that AI must be considered from a global justice perspective given that, globally, AI puts significant downward pressure on several elements of well-being thereby making it harder for people to achieve threshold levels of the central human capabilities needed for a life of dignity.

How social media impacts the autonomy of its users is a topic of increasing focus. However, much of the literature that explores these impacts fails to engage in depth with the philosophical literature on autonomy. This has resulted in a failure to consider the full range of impacts that social media might have on autonomy. A deeper consideration of these impacts is thus needed, given the importance of both autonomy as a moral concept and social media as a feature of contemporary life. By drawing on this philosophical literature, we argue that autonomy is broadly a matter of developing autonomy competencies, having authentic ends and control over key aspects of your own life, and not being manipulated, coerced, and controlled by others. We show how the autonomy of users of social media can be disrespected and harmed through the control that social media can have over its users’ data, attention, and behaviour. We conclude by discussing various recommendations to better regulate social media.

Generative AI technologies have the potential to be socially and politically transformative. In this paper, we focus on exploring the potential impacts that Generative AI could have on the functioning of our democracies and the nature of citizenship. We do so by drawing on accounts of deliberative democracy and the deliberative virtues associated with it, as well as the reciprocal impacts that social media and Generative AI will have on each other and the broader information landscape. Drawing on this background theory, we outline some of the key positive and negative impacts that Generative AI is likely to have on democratic citizenship. The political significance of these impacts suggests the need for further regulation.