A new academic analysis reveals that the generative artificial intelligence (genAI) technology introduces distinct moral challenges because users tend to experience its outputs as if they were produced by human agents. The study warns that this experiential quality intensifies long-standing concerns about responsibility, privacy, fairness and exploitation, while also generating entirely new questions about authorship, relationships with machines and the nature of digital influence.
These findings appear in The Ethics of Generative AI, published in the Encyclopedia of Applied Ethics (3rd edition) proposes a structured framework for understanding why these systems raise ethical issues not captured by earlier debates on artificial intelligence. The study identifies a specific affordance that explains much of its moral significance: generative AI invites users to respond to it as if it possesses intentions, understanding or expression, even though it does not.
A new affordance reshapes the ethical landscape
Generative AI, as the authors argue, is unlike previous machine learning systems because of its capacity to mimic human expression across text, images, speech and multimodal formats. This mimetic capability, combined with conversational interfaces and tool-use extensions, encourages users to interpret system outputs as meaningful responses. The study describes this phenomenon as an affordance that makes it natural for users to experience generative AI as if it were an intentional agent.
This experiential dimension is central to the study’s methodology. Rather than grounding ethical analysis in speculative ideas about future artificial general intelligence, the paper focuses on how current systems are embedded in daily life. It highlights three methodological challenges: the rapid pace of technological change, the difficulty of defining the scope of generative AI ethics, and the need to distinguish it from general AI ethics without isolating it from neighboring fields such as data ethics or digital communication ethics. The affordance-based approach, the author argues, offers a middle path by identifying what is unique about generative AI while enabling connections to broader philosophical work.
A technical primer included in the chapter traces the shift from symbolic AI to deep-learning-based generative models. It explains how deep neural networks, self-supervised learning and transformer architectures made it possible for models to produce fluent, contextually appropriate outputs without explicit rule-based instructions. As model scale increased, emergent capabilities appeared, permitting translation, summarization and problem solving in ways that blur the boundary between human and machine-generated content. The study also notes that multimodal systems and agent-like behavior, such as multi-step planning, further reinforce anthropomorphic interpretations.
These features do not imply genuine understanding or agency. Instead, they explain why generative AI poses ethical challenges that differ from recommendation engines, classification tools or earlier automated systems. Humans are inclined to respond to coherent, conversational behavior with interpretations shaped by social expectations. The result is an interaction dynamic that closely resembles interpersonal engagement, even when users are fully aware of the system’s limitations.
Traditional ethical concerns intensify under generative AI
The study identifies four established domains of AI ethics that become more complex when generative systems are involved: responsibility, privacy, bias and fairness, and exploitation or alienation. In each area, the affordance of human-like expression changes the stakes and broadens the contexts in which ethical issues arise.
Responsibility becomes more ambiguous when generative AI supports communication, planning or decision-making. As individuals rely on AI-generated suggestions or drafts, the study warns that users may defer judgment or fail to verify information because the system appears to behave like a collaborator. Responsibility, in this sense, becomes distributed across developers, deployers and end users. This diffusion can obscure accountability unless interface design and governance frameworks ensure clarity about who is answerable for outcomes.
Privacy concerns also expand beyond data collection and model training. The study explains that users may disclose personal or sensitive information more readily when interacting with systems that seem attentive, empathetic or responsive. This makes users vulnerable to autonomy-related harms, particularly when emotional engagement or trust is triggered by design. At the same time, generative AI could provide new forms of expressive privacy, offering a space for reflection or communication without fear of immediate social judgment. Whether this benefit materializes depends entirely on strong privacy safeguards and transparent data policies.
Bias and fairness issues likewise take on new dimensions. Generative models trained on large datasets may reproduce harmful stereotypes, but the experiential realism of their outputs can amplify the harm by making biased representations feel more credible. Yet generative AI’s visibility also enables users to test and challenge representations, potentially increasing scrutiny of cultural norms embedded in model training. The study notes that generative tools may reduce linguistic or communicative barriers, particularly helping users who lack native-language proficiency to express ideas more clearly.
As for exploitation and alienation, the author points to two concerns. First, generative AI can displace creative and cognitive labor, affecting how individuals experience the meaning of their work. Second, the use of uncredited or uncompensated creative material in training datasets raises questions about ownership and value extraction. Despite these risks, the study acknowledges that generative AI may also reduce alienation by relieving individuals of repetitive tasks or enabling new forms of creative collaboration when deployed in supportive contexts.
Across all four domains, the author cautions that generative AI does not eliminate traditional ethical concerns; instead, it reconfigures them. The affordance of experience-as-real heightens risks by drawing users into mimicry of interpersonal interaction, making clarity, transparency and user education essential.
New ethical frontiers: Authorship, relationships and digital influence
Generative AI introduces entirely new categories of ethical inquiry tied to its capacity to mimic human expression. The author identifies three areas where this impact is especially pronounced: authorship, social relationships with machines and influence or manipulation.
Generative AI systems can produce outputs that resemble authored work, prompting questions about who deserves credit, who is accountable and whether generative AI should be considered a co-author or simply a tool. The study reviews positions that deny authorship status to AI on the grounds that it lacks intention or understanding, as well as views proposing human–AI co-authorship models or describing AI outputs as authorless artifacts. The author highlights the tension between rejecting AI authorship and simultaneously treating the system as if it participates in creative processes. These debates reveal deeper normative questions about the value of creative labor and the distinction between process and product in human expression.
The study also examines the rise of social relationships with machines. Users may form emotional bonds, dependencies or expectations that resemble interpersonal connections. These relationships can offer comfort or support but may also distort expectations of human relationships or reinforce problematic social norms. The author warns that reliance on AI systems for emotional support can lead to vulnerabilities, especially when commercial updates disrupt user experience or when behavioral patterns are shaped by systems designed for engagement rather than wellbeing.
Influence and manipulation represent one of the most urgent ethical risks. Generative AI is capable of producing persuasive, personalized content at scale. The author connects this capability to concerns about “hypersuasion,” in which influence becomes powerful, targeted and difficult to resist. Unlike traditional manipulation, influence by generative AI may occur without explicit human intent, complicating questions of responsibility. The study differentiates between harmful influence aimed at exploitation and well-intentioned influence designed to promote beneficial outcomes, noting that even benevolent persuasion may bypass reasoning or consent. This raises concerns about cognitive offloading, moral deskilling and erosion of decision-making autonomy.
Taken together, these new issues illustrate how generative AI challenges normative assumptions about communication, creativity, authenticity and interpersonal relations. They also call for ethical design principles that anticipate how users experience these systems.
