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On the Ethical Considerations of Generative Agents
N'yoma Diamond, Soumya Banerjee
TL;DR
Generative agents’ ability to emulate human behaviour has drawn interest, while raising ethical concerns about their development and use. This position paper reviews existing ethical work and identifies additional concerns, including risks from misinterpreting agent behaviour, misuse, and hijacking. It proposes mitigation guidelines and future research, while noting challenges in reliably preventing anthropomorphism and misinformation.
Problem
The ethical challenges posed by generative agents and their applications warrant consideration alongside their potential to emulate human behaviour.
Source: §1 · p. 1Method
The paper reviews literature on generative agents and related GAI tools, identifies additional ethical concerns, and discusses mitigation guidelines and future research.
Source: §1 · p. 1Results
The paper identifies risks involving anthropomorphised interpretations, parasocial relationships, misinformation, malicious use, hijacking, and labour displacement.
Source: Anthropomorphisation and… · p. 3, Excessive Trust and Insufficient… · p. 4, Usage by Malicious Actors · p. 4Takeaways & Limitations
Researchers and developers should avoid treating agent outputs as evidence of human behaviour, critically assess where agents are used, and favour collaboration that supplements human workers.
Source: Anthropomorphisation and… · p. 3, Vulnerability to Hijacking · p. 5, Displacement of Human Labour · p. 5Reliably avoiding anthropomorphic behaviour is difficult because its boundaries are unclear, and existing approaches may depend on users responding to warnings.
Source: Creation of Parasocial Relationships · p. 3
Abstract
from arXiv · showhide
The Generative Agents framework recently developed by Park et al. has enabled numerous new technical solutions and problem-solving approaches. Academic and industrial interest in generative agents has been explosive as a result of the effectiveness of generative agents toward emulating human behaviour. However, it is necessary to consider the ethical challenges and concerns posed by this technique and its usage. In this position paper, we discuss the extant literature that evaluate the ethical considerations regarding generative agents and similar generative tools, and identify additional concerns of significant importance. We also suggest guidelines and necessary future research on how to mitigate some of the ethical issues and systemic risks associated with generative agents.
1 Introduction
Generative agents emulate human behaviour by using individual memories and behavioural parameters to make decisions with limited user input. This position paper surveys ethical concerns around their development and use and discusses ways to mitigate them.
1 Introduction
- Generative agents use memories, reflection, planning, and short-term observations to emulate human behaviour and decision-making.Each agent maintains its own memories and behavioural parameters, which are supplied to a generative language model as needed.Source: §1 · p. 1
2 Existing Literature
Existing work addresses ethical issues in GAI and agentic systems, but often treats generative agents only as one part of a broader category. The reviewed literature also varies in its focus, from social risks and technical challenges to mitigation approaches.
2 Existing Literature
- Bail discusses GAI’s potential for social science alongside limits involving human biases, hallucinations, misinformation, and research reproducibility.Bail also identifies opportunities for open social science through open-source GAI tools and infrastructure.Source: §2 · p. 2
- Lazar describes possible benefits of generative-agent applications as well as risks including parasocial relationships, labour displacement, and privacy harms.The paper says Lazar’s analysis is primarily rhetorical and does not propose ways to address many of the concerns.Source: §2 · p. 2
- Chan et al. examine future harms across agentic algorithmic systems, but generative AI tools are not a specific focus.The paper notes that this broader scope leaves some risks specific to agentic GAI-based tools unaddressed.Source: §2 · p. 2
- Anwar et al. analyse technical risks in GAI systems, while Gabriel et al. [10] use AI assistants as a case study of agent-related ethical concerns.Anwar et al. discuss oversight, desired behaviour, safety with external services, and emergent multi-agent behaviour; Gabriel et al. identify personal, socioeconomic, and environmental concerns.Source: §2 · p. 2
3 Ethical Concerns of Generative Agents
Anthropomorphising generative agents can blur the distinction between an emulation and human behaviour. The paper argues that outputs describe the implemented framework and its model, not human behaviour, and calls for care in interpreting them.
Anthropomorphisation and Misunderstanding of Experimental Results
- The paper identifies a research gap: no extant research it found directly analyses risks from anthropomorphised interpretations of generative-agent results.Prior literature discusses anthropomorphisation of agents and similar tools toward users in social applications [10].Source: Anthropomorphisation and… · p. 3
- Excessive anthropomorphisation risks misinterpreting experiments and creating misinformation because agent outputs describe the implemented framework, not human behaviour.The paper states that generative-agent and language-model behaviours are not causally linked to human behaviour patterns.Source: Anthropomorphisation and… · p. 3
- Generative agents may emulate human-like actions where people cannot be used, including in studies of complex human-interaction systems.The paper presents this as a potential benefit, while warning that outputs must not be treated as direct models of how humans behave.Source: Anthropomorphisation and… · pp. 2–3
Creation of Parasocial Relationships
Personalised or embodied generative agents may invite stronger human-like perceptions than existing virtual assistants, creating a risk of harmful attachments. The paper notes that current mitigation approaches have practical limits and that reliably identifying anthropomorphic behaviour is difficult.
Creation of Parasocial Relationships
- Generative agents’ persona, memory, and reflection features may make them seem more human-like than virtual assistants, increasing the risk of harmful attachments.The passage notes that this perception may arise despite agents not being fundamentally more intelligent.Source: Creation of Parasocial Relationships · p. 3
- Existing proposals to state that agents are generative models and avoid anthropomorphic behaviour have uncertain effectiveness and may burden users with managing relationships.Warnings may be ignored, misinterpreted, or circumvented, and the paper says anthropomorphic characteristics lack objective boundaries.Source: Creation of Parasocial Relationships · p. 3
- Park et al.’s example relies on an untested assumption that the underlying model can identify harmful anthropomorphisation accurately.The check’s accuracy may vary across models, and the system offers no guardrails against users ignoring or circumventing it.Source: Creation of Parasocial Relationships · p. 3
Excessive Trust and Insufficient Scepticism
Generative agents may invite undue trust despite remaining extensions of language models, which can hallucinate, lose information, reinforce biases, and behave unreliably. Existing trust-mitigation proposals have limits, while retrieval-augmented generation (RAG) can improve access to information but does not guarantee accurate or impartial outputs.
Excessive Trust and Insufficient Scepticism
- Despite memory and reflection, generative agents remain extensions of language models and can hallucinate, fail to recall information, reinforce biases, or behave unreliably.Their apparent ability to recall information and develop insights may encourage greater trust.Source: Excessive Trust and Insufficient… · p. 4
- Undue trust can leave users vulnerable to misinformation, manipulation, and ideological entrenchment, while proposed safeguards may restrict functionality or be circumvented [10].The paper argues that further research is needed on techniques that inherently mitigate these risks.Source: Excessive Trust and Insufficient… · p. 4
- RAG may improve information access but cannot prevent misinformation; incomplete or complex knowledge and retrieval errors can still produce harmful conclusions.The authors report finding no literature applying RAG to memory- and reflection-enabled agents as proposed by Park et al..Source: Excessive Trust and Insufficient… · p. 4
Usage by Malicious Actors
Generative agents may be misused to conduct scams, spread disinformation, or launch cyberattacks, potentially with greater realism and effectiveness than existing technologies. Detection is difficult, so the paper calls for broader detection approaches and legislation governing development and use.
Usage by Malicious Actors
- Generative agents may enable malicious actors to spread disinformation, conduct scams, or launch cyberattacks with greater realism and effectiveness than existing technologies.Their memory and reflection capabilities may make them better at deceiving people and avoiding automated detection.Source: Usage by Malicious Actors · p. 4
- Malicious agents may impersonate friends, relatives, or officials and conduct social-engineering attacks or cyberattacks previously possible only for humans.Source: Usage by Malicious Actors · p. 5
- Accurately detecting and mitigating malicious behaviour is exceptionally difficult, so the paper calls for broader attack detection and legislation on acceptable development and use [10].The authors suggest researching agent detection but note that targeting GAI specifically may be less effective than addressing misinformation, scams, or attacks broadly.Source: Usage by Malicious Actors · p. 5
Vulnerability to Hijacking
Generative agents may be hijacked or jailbroken, and their memory and reflection capabilities raise concerns about sensitive access and propagation to other systems. The paper recommends considering whether to deploy them and, where use cannot be eliminated, limiting what hijacked agents can do.
Vulnerability to Hijacking
- Hijacking and jailbreaking threaten users and can let users circumvent safety features; memory and reflection may increase agents’ utility and exposure as adoption grows [10].A hijacked agent may be difficult to recover, access sensitive information or actions, or spread unwanted behaviour to other agents or systems.Source: Vulnerability to Hijacking · p. 5
- Generative-agent hijacking is difficult to detect and prevent, and remains insufficiently understood.The paper therefore urges developers to assess where, when, how, and whether to deploy agents.Source: Vulnerability to Hijacking · p. 5
- Where vulnerable agents remain in use, safeguards should prevent action when hijacked, such as requiring human authentication for sensitive access or sandboxing agents.Source: Vulnerability to Hijacking · p. 5
Displacement of Human Labour
Generative agents could make automation attractive to organisations, raising concerns about human-labour displacement. The paper recommends using them to supplement workers rather than replace them, drawing on literature that reports productivity gains from human–AI collaboration without harming the workforce.
Displacement of Human Labour
- Generative agents may attract organisations seeking to automate human tasks, with potential effects on the quantity and quality of labour and other socioeconomic factors [10].Generative language models have already begun replacing workers in customer service, translation, and knowledge-expert roles.Source: Displacement of Human Labour · p. 5
- Overzealous use of generative agents may rapidly displace workers across occupations in a disruptive and unprecedented manner.Source: Displacement of Human Labour · p. 5
- The paper recommends using agents to supplement workers rather than replace them, citing literature that finds human–AI collaboration can improve productivity without harming the workforce.Source: Displacement of Human Labour · p. 5
Exploitation of Developing Nations and Modern Slavery
Generative-agent hardware demand raises concerns about exploitation and modern slavery in developing nations, while the paper finds little meaningful discussion of these risks in GAI literature. It recommends minimizing hardware needs and auditing supply chains.
Exploitation of Developing Nations and Modern Slavery
- Mining silica, lithium, and other device-manufacturing materials can pose environmental and health risks for miners and communities in developing nations, where exploitation and modern-slavery risks are significant.The paper notes that these materials are used in computer chips and batteries.Source: Exploitation of Developing Nations and… · p. 6
- The authors found no literature meaningfully discussing these resource-related concerns in relation to GAI, despite substantial documentation of their impacts.Source: Exploitation of Developing Nations and… · p. 6
- Developers and users should evaluate and minimize hardware requirements, improve efficiency, audit supply chains, and avoid unnecessary agent use where simpler techniques suffice.The paper also recommends independent audits of manufacturers and other supply-chain segments.Source: Exploitation of Developing Nations and… · p. 6
Environmental Impact
Generative AI systems carry environmental risks through energy use, hardware production, and the industries they support, while embodied emissions remain incompletely understood. The paper argues that agent use should be justified against its environmental impact.
Environmental Impact
- High training and inference energy use, emissions embodied in hardware, and support for environmentally irresponsible industries and applications pose environmental risks for AI systems.Source: Environmental Impact · p. 6
- The magnitude of emissions embodied in model training and required hardware is only beginning to be investigated and understood.Source: Environmental Impact · p. 6
- Because emissions-reduction options depend on accessible, feasible resources and techniques, generative-agent use should be critically assessed for necessity against its environmental impact.The paper recommends avoiding superfluous or redundant use.Source: Environmental Impact · p. 6
4 Conclusion
The conclusion frames generative agents as presenting ethical risks from developers, malicious actors, and ordinary users, and argues that ethical evaluation must be addressed directly. It calls for applying existing mitigations and developing new ones where needed.
4 Conclusion
- Frivolous implementation, unsafe or inefficient design, unreliable or untrustworthy behavior, and user error pose significant ethical threats across generative-agent development and use.Source: §4 · p. 6
- Ethical evaluations and impact assessments of new GAI and generative-agent techniques are often treated as an afterthought or omitted.Source: §4 · p. 6
- Researchers, developers, and legislators should apply proposed mitigation approaches where possible and create new techniques for concerns that remain unresolved.Source: §4 · p. 6