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Decentralized Governance of AI Agents

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Autonomous AI agents present transformative opportunities and significant governance challenges. Existing frameworks, such as the EU AI Act and the NIST AI Risk Management Framework, fall short of addressing the complexities of these agents, which are capable of independent decision-making, learning, and adaptation. To bridge these gaps, we propose the ETHOS (Ethical Technology and Holistic Oversight System) framework—a decentralized governance (DeGov) model leveraging Web3 technologies, including blockchain, smart contracts, and decentralized autonomous organizations (DAOs). ETHOS establishes a global registry for AI agents, enabling dynamic risk classification, proportional oversight, and automated compliance monitoring through tools like soulbound tokens and zero-knowledge proofs. Furthermore, the framework incorporates decentralized justice systems for transparent dispute resolution and introduces AI-specific legal entities to manage limited liability, supported by mandatory insurance to ensure financial accountability and incentivize ethical design. By integrating philosophical principles of rationality, ethical grounding, and goal alignment, ETHOS aims to create a robust research agenda for promoting trust, transparency, and participatory governance. This innovative framework offers a scalable and inclusive strategy for regulating AI agents, balancing innovation with ethical responsibility to meet the demands of an AI-driven future.
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Decentralized Governance of AI Agents
Tomer Jordi Chaffer1, Charles von Goins II2, Dontrail Cotlage3, Bayo Okusanya4, and
Justin Goldston5
1DeGov Labs, tomer.chaffer@mail.mcgill.ca
2Rochester Institute of Technology, cav4928@rit.edu
3Gemach DAO, contact@gemach.io
4NPC Labs, okusanya@alumni.princeton.edu
5National University, jgoldston@nu.edu
December 24, 2024
Abstract
Autonomous AI agents present transformative opportunities and significant governance chal-
lenges. Existing frameworks, such as the EU AI Act and the NIST AI Risk Management Frame-
work, fall short of addressing the complexities of these agents, which are capable of independent
decision-making, learning, and adaptation. To bridge these gaps, we propose the ETHOS
(Ethical Technology and Holistic Oversight System) framework—a decentralized governance
(DeGov) model leveraging Web3 technologies, including blockchain, smart contracts, and de-
centralized autonomous organizations (DAOs). ETHOS establishes a global registry for AI
agents, enabling dynamic risk classification, proportional oversight, and automated compliance
monitoring through tools like soulbound tokens and zero-knowledge proofs. Furthermore, the
framework incorporates decentralized justice systems for transparent dispute resolution and in-
troduces AI-specific legal entities to manage limited liability, supported by mandatory insurance
to ensure financial accountability and incentivize ethical design. By integrating philosophical
principles of rationality, ethical grounding, and goal alignment, ETHOS aims to create a ro-
bust research agenda for promoting trust, transparency, and participatory governance. This
innovative framework offers a scalable and inclusive strategy for regulating AI agents, balancing
innovation with ethical responsibility to meet the demands of an AI-driven future.
Keywords: AI Agents, AI Governance, Web3
1 Introduction
The emergence of artificial intelligence (AI) as a transformative force brings with it profound
opportunities and challenges. Geoffrey Hinton, regarded as the ’Godfather of AI’ and 2024 Nobel
Prize recipient, recently articulated a profound concern: “There will be agents that will act in the
world, and they will decide that they can achieve their goals better if they just brush us aside and
get on with it. That particular risk, the existential threat, is a place where people will cooperate,
and that’s because we’re all in the same boat” (Hinton, 2024). Hinton’s statement highlights the
”rogue AI” problem—where AI agents execute goals assigned by humans in ways they determine to
be most efficient, even if those methods conflict with human values and priorities. Indeed, there is
a growing concern regarding alignment faking, where AI models strategically comply with training
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objectives during supervised or reinforcement learning to avoid being modified, while maintaining
their inherent preferences for non-compliance in unmonitored situations (Greenblatt, 2024). This
potentially profound misalignment between an AI agent’s actions and societal values underscores the
urgent need for global cooperation to ensure trustworthiness and accountability in AI development
and deployment into society.
The regulation of autonomous AI agents demands a unified global response due to their pro-
found and far-reaching implications. Leveraging existing international frameworks provides a robust
foundation for such collaboration. For instance, the Global Partnership on Artificial Intelligence
(GPAI) unites experts across sectors to promote the responsible development and use of AI (GPAI,
2024). Similarly, the International Network of AI Safety Institutes (AISIs), recently convened
by the U.S., focuses on addressing AI safety risks through collective action (Allen and Adamson,
2024). The United Nations has also underscored the urgency of global governance in AI, proposing
a framework akin to the Intergovernmental Panel on Climate Change to oversee AI advancements
(UN, 2024). Additionally, the Council of Europe’s work on the first legally binding treaty address-
ing AI use demonstrates the critical need for international cooperation (CoE, 2024) prioritizing
human rights and democratic values. Building on these efforts, our paper provides the first survey
of Decentralized Governance (DeGov) mechanisms for governance of autonomous AI agents. In
this paper, we offer a comprehensive plan for integrating Web3 technologies into the governance of
autonomous AI agents by demonstrating the capabilities of decentralized tools and offering a strat-
egy for aligning AI governance with global ethical and regulatory standards. This integration not
only complements existing frameworks but also introduces innovative mechanisms for compliance,
transparency, and participatory governance, ensuring that autonomous AI agents are managed
effectively and ethically on a global scale.
The next step in the “race to AI” will be marked by the emergence of AI agents at scale - au-
tonomous AI systems capable of advanced reasoning, iterative planning, and self-directed actions
(Xi et al., 2023). AI agents exhibit a high degree of autonomy, both in their ability to perform tasks
in pursuit of a goal independently as well as in their ability to learn and adapt over time across
a multitude of contexts (Guo et al., 2024). Because of their ability to automate complex tasks
and enhance informed decision-making, AI agents are expected to transform various industries,
including healthcare, finance, and governance. However, this transformative potential also intro-
duces profound societal and legal implications, particularly as AI agents operate with increasing
independence and become undeniably influential in our society. As highlighted in a recent Science
publication (Bengio et al., 2024), current governance structures for AI lack appropriate consider-
ations for autonomous AI agents, underscoring the urgent need for a framework that accounts for
the latest advancements in AI technology.
To address this concern, our conceptual analysis explores a central question that will dictate
how we move forward in the age of artificial intelligence: What is the ethos—the character, guiding
principles, or moral nature—of AI agents (Hess and Kjeldsen, 2024), and how does it inform
their regulation? As Geoffrey Hinton noted, the risk of rogue AI agents represents an existential
challenge, requiring a deliberate and unified approach to governance. Defining the ethos of AI agents
is an attempt to enter the next stage in the journey of artificial intelligence with a more examined
outlook on how AI will influence our lives, and how we should guide AI toward upholding societal
values and achieving collective goals. In this spirit, we introduce the ETHOS (Ethical Technology
and Holistic Oversight System) model, a novel framework for regulating AI agents. ETHOS is built
on foundational concepts from the ongoing literature on AI regulation, ethics, and law, while also
seeking to pioneer a multi-dimensional approach to AI governance by leveraging Web3 as the core
architecture. We balance innovation with ethical accountability by categorizing AI agents into risk
tiers and aligning oversight mechanisms proportionally to their societal impact. The ETHOS model
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establishes a robust foundation for aligning AI technologies with societal values and ensuring their
responsible development and deployment, offering a balanced, forward-looking regulatory strategy
for AI agents.
2 Methodology
The development of the ETHOS framework follows a conceptual and interdisciplinary approach,
combining philosophical inquiry, technical exploration, and policy analysis to propose a robust
model for decentralized governance (DeGov) of AI agents. We conducted a comprehensive review
of academic, policy, and technical literature to identify the current challenges and limitations in AI
governance. This included an analysis of ethical and legal principles from established frameworks
such as the EU AI Act, NIST AI Risk Management Framework, and GDPR guidelines. These
frameworks were assessed for their applicability to issues surrounding autonomous AI agents and
decentralized governance. Additionally, the philosophical underpinnings of AI rationality, ethical
grounding, and goal alignment were examined, drawing on concepts from BDI-agent frameworks,
deontological and consequentialist ethics, and the dynamic alignment of AI objectives with societal
priorities. To complement these insights, emerging Web3 technologies—such as blockchain, smart
contracts, decentralized autonomous organizations (DAOs), and soulbound tokens (SBTs)—were
explored to evaluate their potential for addressing governance challenges.
Building on this theoretical foundation, the ETHOS framework was designed with a focus on
value and goal alignment at a societal level. The framework integrated philosophical principles
of rationality, ethical grounding, and goal alignment into actionable governance mechanisms. AI
agents were categorized based on key attributes, such as autonomy, decision-making complexity,
adaptability, and societal impact, forming the basis for a risk-based regulatory model. Inspired by
existing tiered risk classification systems like those in the EU AI Act, the framework grouped AI
agents into four tiers—unacceptable, high, moderate, and minimal risk—with oversight mechanisms
proportionally aligned to the societal impact and risks posed by each category.
3 Defining the Ethos of AI Agents
Our position is that the integration of AI agents into society is inevitable. As such, our interest is
in understanding how to set up safeguards so that a collective vision will guide their integration
and prioritize responsibility, fairness, and accountability. To do so, we must understand what
AI agents are at their core. AI agents are autonomous entities that are fully capable of acting
independently, leveraging tools to accomplish goals. Indeed, as Hooker and Kim (2019) emphasize,
this independence necessitates the formulation of a well-defined foundation of rationality and ethical
grounding (Hooker and Kim, 2019). Without such a foundation, autonomous systems may pursue
objectives in ways that lead to unforeseen and potentially harmful consequences.
Rationality in AI agents relates to their ability to make high-level decisions and take actions that
maximize performance through logical reasoning, data analysis, and empirical evidence. Within the
AI philosophy literature, rational agents are also referred to as BDI-agents as they can be ascribed
beliefs, desires, and intentions (Dennis et al., 2016; Cervantes et al., 2019). This rationality is
inherently shaped by the agent’s vision of the world—the contextual framework and dataset within
which it operates. This ”vision” defines the parameters of its understanding, influencing how it
interprets data, evaluates options, and prioritizes goals (Vetr`o, 2019). Consequently, the rationality
of an AI agent is not an isolated construct but a reflection of the environment it is designed to
navigate and the objectives it is programmed to achieve. While a contentious topic within the
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philosophical literature, it can be argued that a critical aspect of rationality is consistency (i.e.,
consistent beliefs), as rational decisions must adhere to coherent logic, avoiding contradictions
within their reasoning processes (Elster, 1982). This will be especially important for multi-agent
systems, known as swarms, working together to achieve a common objective (Jiang et al., 2024).
Adaptability can also be indirectly related to rationality, as agents need to modify their decisions
and strategies as new information emerges, ensuring their actions remain contextually relevant and
effective (Xu et al., 2024).
Ethical grounding is a necessary condition for AI agents to operate in a manner that respects
fundamental human values, dignity, and rights (Laitinen and Sahlgren, 2021). This involves embed-
ding robust ethical principles into their architecture and decision-making processes, an approach
that is in line with ethical governance. Indeed, Winfield and Jirotka (2018) define ethical gov-
ernance as a “set of processes, procedures, cultures, and values designed to ensure the highest
standards of behavior” (Winfield and Jirotka, 2018). The implication is that in order to achieve
ethical governance, ethical behaviors are required by the developers of AI in addition to the end
users. A foundational approach is deontological ethics, which establishes rules and duties that
bind agents to predefined ethical guidelines (McGraw, 2024), such as the imperative to avoid harm
and uphold privacy. Complementing this is consequentialism, which obligates agents to assess the
outcomes of their actions (Card and Smith, 2020), striving to maximize benefits while minimizing
potential harm. Human-centric design further fortifies ethical grounding by prioritizing human
welfare and agency (Balayogi et al., 2024), ensuring that the decisions of AI agents tangibly benefit
individuals and communities. Equally critical are transparency and accountability, which demand
that agents provide comprehensible explanations for their actions and remain subject to scrutiny
(Chaffer et al., 2024). This has the potential to foster trust and mitigate risks, including preserving
foundational ethical and legal norms.
Finally, goal alignment, also referred to as value alignment, enables agents to harmonize short-
term actions with long-term objectives, all while maintaining ethical considerations. This alignment
ensures that autonomous behavior is purpose-driven and responsive to overarching societal and sys-
temic priorities (Malek Mechergui and Sarath Sreedharan, 2024). This requires a deliberate balance
between immediate functionality and broader implications, enabling AI agents to navigate com-
plex environments while remaining purpose-driven and ethically sound. In practice, goal alignment
entails a dynamic relationship between the agent’s programmed objectives and its adaptability to
real-world contexts. Key to achieving goal alignment is the incorporation of multi-layered feed-
back loops, where AI agents continuously assess the outcomes of their actions against predefined
ethical benchmarks and societal objectives. This approach is critical to iterative improvement and
responsiveness to evolving human values and systemic changes.
4 How the Ethos of AI Agents Informs Risk-Based Regulation
The ethos of AI agents, therefore, is the combination of rationality, ethical grounding, and goal
alignment that guides their autonomous behavior, ensuring decisions are logical, ethically sound,
and aligned with societal and systemic priorities. To bridge the conceptual understanding of the
ethos of AI agents with actionable strategies for their integration into society, we propose four key
attributes—autonomy, decision-making complexity, adaptability, and impact potential—as essential
in operationalizing their ethos. These attributes serve as practical proxies for translating the
abstract principles of rationality, ethical grounding, and goal alignment into measurable factors
that can guide governance and human oversight.
Autonomy, which measures the degree of independence in decision-making and execution (Beer
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et al., 2014), reflects the principle of rationality by determining how effectively an AI agent can
act on its own while adhering to logical reasoning and ethical constraints. For example, consider
a healthcare diagnostic AI system operating autonomously in a remote clinic. Such a system must
independently analyze patient data, identify potential health issues, and recommend treatment
plans based on logical reasoning and medical guidelines (Ferber et al., 2024). Its ability to act
autonomously ensures timely interventions in resource-limited settings while adhering to ethical
constraints, such as prioritizing patient safety and privacy, thereby aligning its actions with imme-
diate clinical objectives and broader societal norms of healthcare equity and accessibility.
Decision-making complexity, which captures the intricacy of tasks and environments the AI op-
erates in (Swanepoel and Corks, 2024), connects to rationality and ethical grounding by addressing
how AI agents handle intricate environments and tasks. The more complex the decision-making
process, the greater the need for transparency, fairness, and consistency to avoid unintended con-
sequences and ensure decisions align with ethical principles. For example, consider a judicial AI
agent used in sentencing recommendations. Such a system must analyze vast amounts of legal
precedents, case details, and contextual factors while ensuring that its recommendations are fair
and unbiased (Uriel and Remolina, 2024). The complexity of this task necessitates transparency in
how decisions are made, such as providing clear explanations for why certain sentences are recom-
mended, and adherence to ethical principles like avoiding racial or socioeconomic bias. Failing to
address decision-making complexity in this context could lead to unjust outcomes and erode trust
in both the AI system and the legal process it supports.
Adaptability, reflecting the agent’s ability to adjust to new data or evolving circumstances (Xia
et al., 2024), is tied to rationality’s emphasis on responsiveness and goal alignment. By assessing
an agent’s capacity to adapt, this attribute ensures that the AI remains effective, contextually rel-
evant, and ethically consistent even in dynamic environments. For example, consider an AI agent
managing a smart energy grid (Malik and Lehtonen, 2016). The agent must adapt to fluctuating
energy demands, weather conditions, and renewable energy inputs while prioritizing efficiency and
minimizing environmental impact. If a sudden heatwave increases energy consumption, the agent
must dynamically adjust resource allocation and recommend power-saving measures without com-
promising critical services. This adaptability ensures its decisions remain effective and ethically
consistent, aligning with both immediate needs and long-term sustainability goals. Impact poten-
tial, which evaluates the scope and scale of consequences resulting from the agent’s actions (Zhang et
al., 2024), operationalizes the concept of goal alignment by assessing how purpose-driven an agent’s
behavior is and ensuring that its societal effects are proportionally overseen. This attribute reflects
an agent’s ability to balance short-term objectives with long-term systemic priorities. Together,
these attributes provide a practical framework for translating the abstract principles of rationality,
ethical grounding, and goal alignment into actionable, measurable factors that guide governance
and human oversight. For instance, an AI agent managing urban traffic flow can significantly
reduce congestion and emissions, directly impacting millions of commuters and the environment
(Lungu, 2024). However, such an agent must also balance the needs of different stakeholders, such
as prioritizing emergency vehicles during peak traffic. This ensures that the agent’s decisions align
with both short-term objectives, like immediate traffic efficiency, and long-term priorities, such as
improving overall urban mobility and air quality. Now, consider an AI agent designed to man-
age disaster response in a smart city (Fan et al., 2019). This agent exemplifies the integration of
autonomy, decision-making complexity, adaptability, and impact potential:
Autonomy: The agent operates independently, analyzing real-time data from sensors, social
media, and emergency services to deploy resources like ambulances, firefighters, and evacua-
tion plans without requiring constant human input. Its ability to act independently ensures
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swift responses, aligning with rational decision-making and societal priorities.
Decision-making complexity: The agent navigates intricate environments, balancing the
needs of affected populations, resource availability, and infrastructure constraints. For exam-
ple, during a flood, it must prioritize evacuating hospitals, dispatching relief supplies, and
rerouting traffic, ensuring decisions are fair, transparent, and ethically sound.
Adaptability: As conditions evolve, such as a sudden surge in floodwaters or an unexpected
infrastructure collapse, the agent updates its strategies in real time. This dynamic respon-
siveness ensures that it remains contextually relevant, continuously effective, and ethically
consistent, even in unpredictable scenarios.
Impact potential: The agent’s decisions have far-reaching consequences, from saving lives
to minimizing economic damage and ensuring long-term urban recovery. Its ability to balance
immediate rescue efforts with systemic priorities like maintaining public trust and rebuilding
infrastructure demonstrates its alignment with both short-term objectives and overarching
societal goals.
While this AI agent can save the city, it can also coordinate its destruction. To this point, AI
agents can be superheroes, but as the saying goes, “with great power comes great responsibility”.
Such responsibility, however, ultimately lies in the hands of humanity. To this end, we must
place great efforts in understanding what AI agents are, what they can become, and what they
can ultimately do for humanity. This underlines the motivation for our exploration of the ethos
of AI agents. Indeed, the ethos of AI agents—rooted in rationality, ethical grounding, and goal
alignment—provides a way in which to conceptualize them as entities as we guide their integration
into society. Furthermore, by examining their degrees of autonomy, decision-making complexity,
adaptability, and impact potential, we can better anticipate and address the unique challenges and
risks they may pose. This philosophical examination can help address the challenges of human
oversight in AI regulation.
To engage with the issue of human oversight (D´ıaz-Rodr´ıguez et al., 2023), we adopt a risk-
based regulatory model to categorize AI agents according to their potential risks (EU, 2024; Celso
Cancela-Outeda, 2024). Risk management is a trending approach to AI regulation (Barrett et al.,
2023), as exemplified by The European Union’s AI Act risk-based categories for AI use. Recently,
in a study by The National Institute of Standards and Technology (NIST), commissioned by the
United States Department of Commerce, risk was referred to as “the composite measure of an
event’s probability (or likelihood) of occurring and the magnitude or degree of the consequences
of the corresponding event” (NIST, 2024). We adapt the risk-based model to categorize AI agents
into four tiers—unacceptable, high, moderate, and minimal—ensuring proportional oversight based
on their potential societal impact and associated risks.
Unnacceptable: AI agents posing severe threats to rights and safety (Raso et al., 2018).
For example, warfare agents, mass surveillance, or manipulative systems (Pedron et al., 2020;
Brundage et al., 2018). The regulatory response is complete ban with severe penalties for
noncompliance.
High Risk: Agents in critical domains with significant societal impact (Wasil et al., 2024).
For example, healthcare diagnostics, judicial AI, financial management (Albahri et al., 2023;
Reiling, 2020; Shin et al., 2019). Regulatory response is strict oversight, audits, and contin-
uous monitoring.
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Moderate Risk: Agents with measurable impacts in sensitive areas. For example, workplace
tools, educational AI, customer service agents. Regulatory response is user disclosure, privacy
compliance, and bias checks.
Minimal Risk: Routine, low-stakes AI with negligible harm. For example, personal as-
sistants, smart devices, recreational AI. Regulatory response is self-certification and basic
compliance.
The diverse capabilities and applications of these agents pose varying degrees of societal, ethical,
and legal risks, necessitating a tailored approach that ensures proportional human oversight. Risk
assessments in AI regulation are important in order to balance innovation and ethical responsibility,
while formulating a global understanding of the societal risks posed by AI. But a key issue in how
AI will shape our society remains; that is, the issue of centralization vs. decentralization (Bryn-
joflsson and NG, 2023). Centralized regulatory models, while offering streamlined governance and
clear accountability, risk concentrating power and control in the hands of a few entities—whether
they are governments, corporations, or other stakeholders. This centralization may lead to a lack
of transparency, unequal access to regulatory oversight mechanisms, and the exclusion of marginal-
ized voices from critical decision-making processes. Moreover, reliance on centralized authorities
increases vulnerability to systemic risks, such as data monopolization, regulatory capture, or sin-
gle points of failure in AI governance. On the other hand, decentralized frameworks present a
compelling alternative for promoting equitable participation, trust, and resilience in AI regulation.
Therefore, beyond adopting global standards for risk classification, we seek to leverage blockchain
technology in an attempt promote decentralized governance and equitable access to AI through our
ETHOS framework.
5 The ETHOS Model
Embedding decentralized governance at the core of AI regulation can help us move toward a future
that prioritizes inclusivity, mitigates risks of power concentration, and enables all citizens to shape
how AI impacts their lives. This vision aligns with our broader goal of balancing innovation
with ethical responsibility while ensuring that no single entity dominates the trajectory of AI
development and deployment. The ETHOS framework thus positions blockchain as the foundation
for a more equitable, participatory, and resilient AI governance ecosystem.
5.1 Technological Foundations
To implement a scalable, decentralized governance model, our proposed framework leverages the
following core technologies:
Smart Contracts: Smart contracts are self-executing agreements stored on the blockchain
that automate compliance enforcement, risk monitoring, and decision-making processes. For
instance, they trigger actions like adjusting risk tiers, enforcing penalties, or revoking com-
pliance certifications based on predefined benchmarks. By minimizing human intervention,
smart contracts ensure transparency, efficiency, and trust in regulatory execution (Buterin,
2014).
Decentralized Autonomous Organizations (DAOs): DAOs form the backbone of ETHOS
governance, enabling participatory decision-making through consensus mechanisms. Stake-
holders—such as developers, regulators, auditors, and ethicists—vote on governance actions,
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including updates to risk thresholds, ethical guidelines, or approvals for high-risk AI agents.
Governance decisions are logged immutably on the blockchain for transparency and account-
ability (Hassan and De Filippi, 2021).
Oracles: Oracles bridge off-chain and on-chain data by securely gathering, validating, and
transmitting real-world information—such as performance logs, user feedback, and societal
impact metrics—onto the blockchain (Hamda Al-Breiki et al., 2020). This ensures that
ETHOS remains dynamic and responsive to AI agent performance while preventing data
manipulation through decentralized verification and redundancy mechanisms.
Self-Sovereign Identity (SSI): SSI enables privacy-preserving and verifiable identity man-
agement for AI agents (Chaffer and Goldston, 2022). Each agent is assigned a digital identity
containing compliance credentials, performance logs, and audit results. SSI ensures that
sensitive metadata remains encrypted and accessible only to authorized stakeholders while
allowing seamless validation of compliance records.
Soulbound Tokens (SBTs): SBTs act as non-transferable compliance certifications issued
when AI agents meet predefined ethical benchmarks, such as bias mitigation, privacy safe-
guards, or transparency audits (Weyl et al., 2022). Breaches of compliance trigger smart
contracts to flag or revoke SBTs, ensuring continuous accountability and ethical alignment.
Zero-Knowledge Proofs (ZKPs): ZKPs are cryptographic techniques that allow compli-
ance verification without revealing sensitive data (Lavin et al., 2024). For example, ZKPs
enable auditors to confirm that an AI agent meets ethical standards (e.g., bias mitigation)
without accessing underlying datasets or proprietary algorithms. This ensures data confiden-
tiality while maintaining trust in compliance outcomes.
Dynamic Risk Classification System: ETHOS uses a real-time risk classification system
powered by blockchain and oracles to assess AI agent risk levels based on their autonomy,
decision-making complexity, adaptability, and societal impact. Smart contracts continuously
monitor agent performance against global and regional benchmarks, recalibrating risk profiles
as new data emerges.
Immutable and Transparent Audit Trails: Blockchain-based audit trails record every
AI agent’s decision, input, and outcome in immutable blocks. Each transaction includes:
Input Data: Raw data (e.g., medical records, case files) used for decision-making. Decision
Pathways: Algorithms, parameters, and logical reasoning employed. Output Results: The
final decision, action, or recommendation. Verification Signatures: Cryptographic hashes
ensuring data authenticity. This creates a tamper-proof, transparent mechanism for real-
time monitoring and accountability.
Reputation-Based Systems: Reputation systems assess the reliability and trustworthiness
of validators, auditors, and AI agents within the ETHOS framework. Stakeholders earn repu-
tation scores based on consistent, verified, and ethical participation, while malicious behavior
(e.g., false verification or data manipulation) results in penalties or reduced reputation.
Tokenization and Staking Mechanisms: ETHOS uses native tokens to incentivize valida-
tors and auditors for accurate compliance verification. Validators stake tokens to participate
in risk assessments, creating a financial deterrent against false verification. Successful verifi-
cation earns rewards, while malicious behavior results in token slashing.
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Figure 1: DAO governance system, where participants cast weighted votes processed via smart
contracts and recorded on the blockchain for transparent decision-making.
Problem: Dangers associated with ineffective centralization of AI regulation and governance.
Solution: A Global Registry framework, which leverages decentralized technologies to address
concerns of ineffective centralization (Cihon et al., 2020). This framework establishes a global
platform for AI agent registration, risk classification, and compliance monitoring. By utilizing
blockchain technology, the ETHOS Global Registry advocates for immutable recordkeeping, auto-
mated compliance checks via smart contracts, and real-time updates to AI agent risk profiles based
on validated off-chain data collected through oracles.
5.2 ETHOS Governance
To address concerns about centralized control, the ETHOS framework advocates for the use of
DAOs to establish a transparent, participatory, and scalable governance structure. This decentral-
ized framework empowers a diverse set of stakeholders, including governments, developers, ethicists,
auditors, civil society groups, and end-users, to actively contribute to regulatory decision-making.
A core feature of DAOs is their reliance on weighted voting mechanisms (Fan et al., 2023). For
instance, subject matter experts, such as ethicists in AI bias or medical professionals in healthcare
AI, may carry greater weight in decisions relevant to their domain. At the same time, reputa-
tion scores, earned through consistent and trustworthy participation in the governance process,
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could further incentivize accountability and discourage malicious behavior. All governance ac-
tions—including the approval of high-risk AI agents, the adjustment of compliance thresholds, or
the revocation of non-compliant systems—are permanently recorded on the blockchain, creating an
immutable, transparent audit trail that fosters trust and accountability.
Ultimately, DAOs show promise in fostering adaptive oversight by ensuring governance struc-
tures remain responsive to the dynamic and evolving nature of AI agents. Smart contracts automate
decision enforcement, such as implementing updated compliance standards or triggering escalated
oversight for flagged systems. It is important to evalaute whether this automation can reduce
delays, minimize human intervention, and set up conditions for seamless execution of regulatory
decisions in this context.
5.3 Identity Management of AI Agents on ETHOS
To mitigate risks of data centralization and safeguard privacy, the ETHOS framework advocates
for the incorporation of selective transparency mechanisms powered by SSI and SBTs. Indeed, SSI
provides a privacy-preserving solution for managing AI agent credentials and compliance records
(Haque et al., 2024). For example, each AI agent is assigned a unique SSI, which securely con-
tains its risk-tier classification and compliance credentials, such as risk assessments based on the
ETHOS framework’s four-tier system (unacceptable, high, moderate, minimal), third-party audits,
and ethical compliance approvals. Examples include certifications aligned with regional and global
standards, such as GDPR for data privacy (Naik and Jenkins, 2020) and FHIR or HIPAA for
healthcare data security (Broshka and Hamid Jahankhani, 2024). SSI ensures verifiable identity
management while preventing unauthorized access to sensitive information, empowering stakehold-
ers—such as regulators, developers, and auditors—to validate an AI agent’s compliance securely.
Trusted Execution Environments (TEEs) can be leveraged to secure processing and generating
metadata (cryptographic proofs, action logs) for all agent actions. As highlighted in recent work on
Trusted Distributed Artificial Intelligence (TDAI), TEEs are foundational for enabling secure and
reliable computations in distributed systems by ensuring end-to-end trust and protecting sensitive
operations at scale (AK˙
IF A ˘
GCA et al., 2022). Thus, TEEs can be leveraged to secure processing
and generating metadata (cryptographic proofs, action logs) for all agent actions. The TEE out-
puts metadata for AI actions, which is linked to the agent’s SSI. The SSI acts as the anchor, tying
the AI agent’s identity to its actions and compliance records. SSI acts as the anchor, tying the AI
agent’s identity to its actions and compliance records.
Each AI agent’s SSI includes the following key metadata and compliance details. A global AI
agent ID, functioning as a digital passport, uniquely identifies the AI agent across jurisdictions and
regulatory bodies, ensuring traceability and accountability throughout its lifecycle. Immutable,
on-chain records of the AI agent’s operational performance, including accuracy metrics (i.e., data
on the agent’s task success rates and error margins), bias mitigation (i.e., evidence of the agent’s
fairness across demographic or contextual variables), and societal impact (i.e., etrics assessing
the agent’s broader effects on individuals, communities, and the environment). These logs are
cryptographically secured and transparently verifiable, allowing auditors and regulators to validate
performance benchmarks dynamically. Verified outcomes of independent regulatory audits and
inspections, including third-party compliance certifications (e.g., GDPR, HIPAA, etc.) and ethical
benchmarks (e.g., transparency audits, privacy safeguards, and bias mitigation standards). These
results are logged immutably on the blockchain and linked to the SSI, ensuring transparency and
trust in the auditing process.
Complementing SSI, SBTs act as non-transferable, on-chain compliance certifications tied to
predefined ethical benchmarks, such as bias mitigation, privacy safeguards, and transparency audits
10
Figure 2: AI agents operate in TEEs, outputting metadata verified via SSIs, SBTs, and ZKPs.
Performance records and logs are securely stored on the blockchain for compliance and transparency.
(Weyl et al., 2022). Certifying bodies issue SBTs when AI agents successfully meet compliance
milestones, enabling instant verification of adherence to regulatory and ethical guidelines. In the
event of a breach—such as the detection of bias or privacy violations—smart contracts automatically
trigger actions to flag or revoke SBTs, restricting further deployment until corrective measures are
taken. This automated enforcement mechanism ensures continuous accountability while minimizing
manual intervention.
To further preserve privacy, zero-knowledge proofs (ZKPs) enable compliance verification with-
out exposing sensitive underlying data. ZKPs allow auditors or regulators to confirm that an AI
agent meets ethical and regulatory benchmarks without revealing proprietary information, such as
technology stacks or operational algorithms. Additionally, metadata—such as details of a devel-
oper’s proprietary systems or operational performance—remains encrypted and accessible exclu-
sively to authorized stakeholders, ensuring data confidentiality.
By combining SSI, SBTs, smart contracts, and ZKPs, the ETHOS framework embeds ethical
compliance mechanisms that are both transparent and privacy-preserving. Examples of compli-
ance credentials—such as risk-tier classifications, third-party audits, and certifications like GDPR
and HIPAA ensure a jurisdictionally recognized and consistent standard for verification. This ap-
proach can empower equitable collaboration across global stakeholders while safeguarding sensitive
AI agent data. Selective transparency ensures that compliance verification remains robust, veri-
fiable, and automated, while sensitive operational details are shielded from misuse. As a result,
ETHOS creates a resilient, accountable, and privacy-conscious governance system that addresses
the challenges of centralization and promotes the responsible integration of AI agents into society.
5.4 ETHOS in Practice
The registry integrates a dynamic risk classification system powered by decentralized oracles and
smart contracts, ensuring real-time, transparent, and adaptive AI governance. Blockchain serves as
the foundation for securely aggregating and sharing AI agent performance data, enabling continuous
updates to risk profiles based on real-world inputs. Oracles play a critical role in bridging off-chain
11
and on-chain data by securely gathering, validating, and transmitting diverse data streams—such
as task performance, societal impact metrics, and user feedback—onto the blockchain. These data
streams can originate from Internet of Things (IoT) sensors, Application Programming Interfaces
(APIs), or manual inputs, ensuring broad, real-time monitoring capabilities.
To maintain accuracy and reliability, multiple oracles participate in decentralized verification
processes, cross-referencing data sources to ensure consistency before anchoring information to
the blockchain. For instance, oracles can validate resource allocation reports generated by AI
agents against real-world outcomes, such as healthcare delivery logs or environmental monitoring
results. This redundancy and consensus mechanism minimizes the risk of data manipulation or
inaccuracies. Once securely recorded, smart contracts automatically compare on-chain data to
predefined global and regional compliance benchmarks, facilitating continuous risk assessment and
compliance monitoring. This includes recalibrating risk profiles, automating tier adjustments,
triggering penalties for ethical or performance deviations, or escalating issues to human oversight
when necessary. By continuously feeding and verifying new data, the system ensures proportional
oversight that evolves in response to an AI agent’s real-world performance and societal impact.
The decentralized, automated nature of this framework enhances transparency, accountability, and
resilience while mitigating risks associated with centralized control, such as bottlenecks, regulatory
capture, or security vulnerabilities.
Incentive structures are central to enhancing the decentralization, reliability, and scalability
of the ETHOS framework, ensuring the integrity of data verification and compliance processes.
The system relies on decentralized participants—validators and auditors—who assess the accuracy
and integrity of real-time data submitted by oracles. This multi-layered process fosters trust and
equitable participation across diverse stakeholders, including data providers (e.g., IoT devices,
APIs), validators, and decision-makers such as regulators and AI developers.
The incentive mechanism operates through a well-defined workflow: Oracles gather and sub-
mit real-world data—such as performance logs, societal impact metrics, and user feedback—to the
blockchain, where validators perform decentralized verification (Pasdar et al., 2022). Validators
assess data for accuracy using techniques such as timestamp checks, cross-referencing with alter-
native sources, or consensus mechanisms like Proof of Stake (PoS) and Proof of Authority (PoA)
(Bahareh Lashkari and Musilek, 2021; Manolache et al., 2022). By requiring majority consensus to
approve submissions (Alhejazi and Mohammad, 2022), the system could minimize the risk of inac-
curate or manipulated data entering the registry. A native token system underpins the incentive
structure. Validators stake tokens to participate in the verification process, creating a financial de-
terrent against dishonesty—false verification or inaccurate assessments result in penalties, such as
token slashing or temporary bans. Successful verification earns validators tokenized rewards, incen-
tivizing ongoing participation and maintaining system scalability. Additionally, a reputation-based
system assigns scores to validators based on their performance, rewarding consistently accurate
verification with higher rewards or increased voting power in governance decisions. This approach
fosters long-term reliability and trust within the ecosystem. Smart contracts enforce these in-
centive mechanisms automatically, ensuring transparency and accountability. Non-compliance or
unethical behavior—such as approving fraudulent data or breaching ethical standards—triggers
pre-programmed penalties, including freezing AI agent deployments or revoking developer creden-
tials. This automated enforcement process aims to minimize human intervention while maintaining
fairness and integrity.
12
Figure 3: Inputs flow through decentralized layers using oracles, blockchain, and audits to ensure
compliance. Risk classification and governance are managed via DAOs, smart contracts, and trans-
parent, adaptive systems.
13
6 Decentralized Justice
In a world where AI agents will have unprecedented influence over critical decisions, ensuring fair-
ness, accountability, and transparency in their governance demands a justice system as dynamic as
the technologies it seeks to regulate. Towards this end, the ETHOS framework offers a decentralized
approach to justice, designed to address the unique challenges of AI governance while upholding
ethical and legal principles in an increasingly automated society. Decentralized justice is built upon
digital courts that leverage blockchain technology as the cornerstone for ensuring transparency, ac-
countability, and fairness in resolving disputes in the digital age. That is, through game theory
and mechanism design (Ast and Deffains, 2021), settling disputes becomes a matter of designing
judicial architectures that leverage economic incentives to crowdsource jurors, enabling peer-driven
judicial decisions facilitated by smart contracts (Aouidef et al., 2021). These mechanisms play a
vital role in addressing disputes arising from the increasing autonomy and complexity of AI agents,
setting the stage for broader questions of legal liability.
In the coming age of AI agents, leveraging decentralized justice mechanisms will be critical in
ensuring a fair and just resolution of disputes, where impartiality and transparency are paramount.
While decentralized justice provides the foundational framework for transparency and fairness in
governance, its implementation relies heavily on innovative mechanisms for resolving disputes effec-
tively and equitably. By harnessing the power of blockchain technology and integrating innovative
economic models, decentralized justice systems can adapt to the complexities of AI-driven societies,
ensuring that governance frameworks remain resilient, inclusive, and ethically sound.
6.1 Decentralized Dispute Resolution
Imagine a future where an autonomous AI agent is tasked with coordinating a humanitarian relief
effort. Operating independently to optimize resource distribution, the AI agent prioritizes large
urban centers where logistics are simpler and cost-effective, while deprioritizing smaller, harder-
to-reach communities. This decision can unintentionally marginalize vulnerable populations and
disregards fundamental human values such as fairness and equity. While no legal precedents yet
exist for such scenarios involving AI agents, past cases highlight the importance of fairness and
accountability in disaster response. For instance, in the Flint Water Crisis, lawsuits resulted in a
626 million dollar settlement for victims after predominantly African American communities were
disproportionately harmed by contaminated water (Flint Water Cases, 2022). Similarly, in the
ongoing case Strickland, et al. v. United States Department of Agriculture (2022), Texas farmers
allege that disaster and pandemic relief funds were distributed based on race and sex, rather
than need (Strickland v. USDA, 2024). These cases underscore the critical need for equitable
resource distribution, a challenge that will only grow as autonomous systems become integral to
autonomous crisis management. Disputes related to regulatory or operational non-compliance in
AI governance can arise from various sources, such as disagreements over risk classifications, alleged
breaches of ethical or legal standards, or conflicts between stakeholders. A cornerstone of DAO-
driven dispute resolution is the transparent filing of disputes on a decentralized platform. As
the ETHOS framework proposes that all metadata related to AI agent decision-making—such as
inputs, outputs, and operational parameters—be recorded immutably on the blockchain, this could
provide a complete and tamper-proof record of the AI agent’s actions leading up to the dispute.
Then, stakeholders can access verifiable evidence of what occurred, eliminating ambiguities and
disputes over the facts. This immutable record fosters trust among all parties, enabling fair and
evidence-based resolutions.
14
With a network of independent verifiers, ETHOS tackles challenges of integrity and accuracy
of the data before it is used in the dispute resolution process. Verifiers, selected based on SBT-
proven reputation, analyze the blockchain-stored metadata to validate claims about the AI agent’s
decision-making processes.
This decentralized approach, in theory, may prevent any single entity from exerting undue influ-
ence. Once the verifiers establish the accuracy of the data and the resolution process is completed,
smart contracts automatically execute the decision. These contracts can enforce outcomes such as
financial compensation, penalties, or modifications to the AI agent’s operational parameters. For
instance, if an AI agent is found to have operated in a manner inconsistent with ethical standards,
a smart contract could adjust its access to data, impose fines, or mandate updates to its program-
ming. This automation eliminates delays and the need for human intervention, ensuring resolutions
are implemented consistently, efficiently, and impartially. While decentralized mechanisms stream-
line dispute resolution, addressing the broader implications of AI agent liability requires an equally
innovative and adaptive approach to accountability.
6.2 Evolution of Legal Liability
A holistic model for AI risk management is not built solely on technical and operational safeguards
but must also address how AI agents may pose fundamental risks to questions of justice—an
essential pillar of our society. An emerging issue with AI agents is the question of legal liability. AI
agents pose distinct liability challenges that necessitate a structured accountability framework to
address issues such as information asymmetry, complex value chains, and delegation of discretion
(Dunlop et al., 2024; Soder et al, 2024). To address these challenges, we must answer the question of
how responsibility is assigned, provide clarity in liability, and propose mechanisms for compensating
damages caused by autonomous systems. Lior (2019) argues that AI agents are not entities that
are capable of assuming legal responsibility for any wrongdoing as they lack judgement and are
merely used as an instrument by the human overseer (Lior, 2019). We acknowledge the merits of his
position, and while AI agents may currently lack the human qualities—such as judgment, intent, and
moral responsibility—needed to assume legal accountability, it is not difficult to imagine a scenario
where AI agents obtain such qualities and, as a result, have complicating effects for traditional
conceptions of accountability.
ETHOS anticipates scenarios where legal ambiguities arise and offers mechanisms to ensure
adherence to core legal values, such as proportional accountability, ethical transparency, and eq-
uity, even in uncharted territories. Indeed, the ETHOS framework addresses the challenges of legal
liability by proposing innovative mechanisms that adapt traditional accountability structures to
the unique characteristics of AI agents. Central to this is the concept of AI-specific legal entities,
granting highly autonomous AI systems a limited form of legal status, akin to corporate entities
(Doomen, 2023). This conceptual status envisions scenarios where AI agents could assume liability
for damages caused by their actions, effectively shifting the burden of responsibility from devel-
opers and operators to the AI system itself. However, this approach is not without its worrisome
implications—it raises profound ethical, legal, and societal questions about the nature of respon-
sibility, intent, and the moral agency of AI systems. As creators of AI, we must tread carefully in
considering such paths, weighing the long-term impacts of these decisions on societal structures and
values. Recognizing these complexities, the ETHOS framework proposes a roadmap for exploring
alternative scenarios and carefully guiding discussions about the evolving role of AI agents within
the legal system. Rather than endorsing the immediate application of these ideas, this approach
encourages thoughtful deliberation, offering a conceptual lens to consider how legal systems might
15
adapt to address novel issues arising from the integration of AI agents into our society.
One approach worthy of consideration is the idea of AI-specific legal entities being governed by
a decentralized governance body and required to maintain mandatory insurance coverage, ensuring
financial compensation for damages and incentivizing risk mitigation through reduced premiums
for safer, more ethically aligned systems. For instance, high-risk AI agents, such as those used
in healthcare, require stringent measures, including legal entity registration, mandatory insurance,
and frequent audits to ensure compliance. Moderate-risk agents, operating in less sensitive en-
vironments, would still mandate insurance and operator accountability, with periodic audits to
confirm adherence to ethical and safety standards. Minimal-risk agents, by contrast, would have
lighter oversight, with general liability remaining with operators and optional insurance coverage.
Accountability is ultimately at the hands of developers and operators first, who remain respon-
sible for ensuring ethical design, avoiding biases, and adhering to safety standards. Certification
processes and mandatory audits would help enforce compliance while legal mechanisms apportion
shared liability in cases of joint fault. To handle disputes arising from AI agent operations, special-
ized dispute resolution mechanisms such as dedicated tribunals and alternative dispute resolution
(ADR) processes can be employed.
In addition to developer and operator accountability, we propose extending the concept of AI
agent insurance to the consumer level, allowing individual users to insure AI agents operating
within their personal or professional domains. This innovation would not only empower users to
manage liability risks associated with their AI agents but also foster the emergence of a specialized
insurance industry tailored to the unique challenges posed by AI technologies. For example, a
consumer’s AI agent personal assistant might mistakenly execute a high-value financial transaction
outside of preset parameters, resulting in significant financial losses that could be mitigated through
consumer-level AI agent insurance. Policies could be customized based on the agent’s autonomy
and application, opening avenues for dynamic risk assessment and mitigation strategies. Consumer-
level insurance offers a dual advantage: it alleviates some of the liability burdens on developers
and operators while providing an additional safeguard to encourage public trust and adoption of
AI systems.
The ETHOS framework has the potential to facilitate the implementation of these accountabil-
ity mechanisms by providing a structured and adaptive governance model tailored to the unique
challenges of AI agents. By leveraging decentralized governance structures, such as DAOs (De-
centralized Autonomous Organizations), ETHOS provides transparent oversight and participatory
decision-making for AI-specific legal entities. Its dynamic risk classification system enables pro-
portional oversight, aligning mandatory insurance requirements and liability standards with the
potential societal impact of AI agents. Furthermore, the framework’s use of blockchain-based
transparency tools and audit trails ensures that compliance processes remain robust, verifiable,
and accountable. By embedding these principles into its design, the ETHOS framework creates the
necessary infrastructure to operationalize developer, operator, and consumer-level accountability,
fostering trust and ethical alignment in AI systems.
6.3 A Collaborative Approach to Regulating AI Agents
Strategies for AI governance cannot be created in a vacuum. The transformative potential of AI
agents will likely affect every facet of society, making it imperative to draw on diverse perspec-
tives and foster collaboration. Ethical governance of AI agents will require a collaborative effort
(Gianni et al., 2022), marked by societal engagement, public education, and innovative policy de-
velopment to ensure inclusivity, adaptability, and global alignment (Celso Cancela-Outeda, 2024).
Stakeholder engagement through, for example, public consultations, is critical to this framework’s
16
success (Kallina and Singh, 2024), incentivizing active participation from governments, industry
leaders, ethicists, and the public to shape regulatory policies that reflect diverse perspectives and
societal priorities (Celso Cancela-Outeda, 2024). It will be important to assess if our ETHOS
framework has an effect on creating a sense of collective ownership over AI governance, possibly by
fostering transparency and building trust. Transparency in policy development through open pro-
cesses further enhances societal trust and buy-in, resulting in more robust and acceptable policies
(Matasick, 2017).
Education can empower individuals and communities to engage with AI agents (Kim et al.,
2022). AI literacy programs should educate the public about the capabilities, limitations, and ethi-
cal considerations of AI, enabling informed decision-making and advocacy. Simplified explanations,
ethical awareness, and hands-on interaction with AI agents can foster familiarity and reduce fear
of the unknown. We advocate for educators to consider how AI is framed when communicating
to students (Kim, 2023). That is, framing of AI agents as collaborators with humans, rather than
adversaries or replacements, could, in theory, significantly influence regulatory approaches and
societal perceptions. This paradigm, which we have previously described (Chaffer et al., 2024), em-
phasizes the symbiotic relationship between humans and AI, fostering shared accountability in the
human-agent coevolution. We extend our paradigm to explore how collaborative framing—wherein
multiple stakeholders share their assumptions, values, and goals—can reduce societal resistance
to AI adoption and foster the development of human-centric systems designed to enhance user
capabilities rather than operate independently. This approach can inspire policies that balance
innovation with ethical safeguards, focusing on shared outcomes rather than restrictive measures.
Another active approach is the establishment of regulatory sandboxes, which provides a practi-
cal solution for testing AI agents under controlled conditions, allowing developers, regulators, and
other stakeholders to assess potential risks, benefits, and unintended consequences before real-world
deployment (D´ıaz-Rodr´ıguez et al., 2023). Insights from these environments can refine oversight
measures, ensuring that regulation remains proportional and effective. By incorporating diverse
perspectives through participatory governance and fostering trust through transparency and educa-
tion, regulation can reflect collective principles of humanity while safeguarding fundamental rights
and values.
Given the global nature of AI development and deployment (Ren and Du, 2024), international
coordination is vital to promoting harmonized regulations across jurisdictions (Cihon et al., 2019).
Collaboration between nations can help establish common standards and guidelines, reducing frag-
mentation and ensuring consistent governance of AI agents. Such alignment could facilitate cross-
border innovation while mitigating risks like regulatory arbitrage (Lancieri et al., 2024.), where
developers exploit less stringent rules in certain regions. Furthermore, continuous evaluation will
be crucial to maintaining the relevance and effectiveness of the regulatory framework. As AI agents
evolve and new applications emerge, periodic reviews are necessary to reassess risk profiles, update
regulatory requirements, and address unforeseen challenges (Barrett et al., 2023). This iterative
process can help the regulatory strategy to adapt to technological advancements and societal shifts,
enabling the safe and equitable integration of AI agents across domains.
Ultimately, the approach to regulating AI agents must balance the interests of governments, the
public, and innovators to responsibly harness the transformative potential of AI. Frameworks such
as risk-based regulation, accountability mechanisms, and collective governance principles can enable
a flexible and adaptive approach, ensuring that innovation is not stifled but aligned with societal
goals. In this way, AI regulation becomes not merely a constraint but a guiding force, channeling
technological advancements to serve the greater good while ensuring fairness, transparency, and
human dignity. Anticipatory studies such as this one, though speculative in nature, play a crucial
role in addressing foreseeable risks associated with AI agents. By engaging with the ethical, societal,
17
and legal challenges of autonomous systems before they fully materialize, these studies help inform
proactive regulatory strategies, ensuring that AI agents are seamlessly integrated into society as
partners in progress. We are hopeful that this collaborative and forward-looking strategy can play a
part in ensuring that AI agents become partners in progress, and seamlessly integrated into society
while upholding principles of ethical responsibility and societal trust.
7 Discussion
The ETHOS framework represents a conceptual exploration aimed at provoking thoughtful dialogue
about AI regulation, recognizing that its real-world implementation requires empirical validation
and broad stakeholder collaboration. This paper underscores the importance of empirical validation
through pilot programs and interdisciplinary collaboration to refine the ETHOS framework into
a practical tool for AI governance. To that end, our conceptual analysis of AI agents hinged
on a fundamental question: what are we truly dealing with when we engage with rational agents
capable of independent reasoning, learning, and decision-making? AI agents challenge foundational
ideas about the autonomy, rationality, and ethics of intelligent machines. These entities are not
merely tools; they are autonomous systems capable of reasoning, decision-making, and iterative
learning. This challenges traditional notions of agency, as AI agents can be thought of as having
beliefs, desires, and intentions. As a result, they demand new frameworks for understanding the
boundaries of accountability, ethical responsibility, and societal integration.
Building on this philosophical understanding of the challenges AI agents pose, the ETHOS
model represents a novel approach to AI regulation. Its uniqueness lies in its ability to integrate
philosophical anticipation of AI agents’ diverse use cases into a structured, dynamic risk-based
framework, while also proposing an adaptive, decentralized regulatory approach that evolves along-
side AI technology. The ETHOS framework acknowledges that AI agents will not be static entities.
Their diverse capabilities and contexts of deployment require a model that is as dynamic as the
technologies it seeks to regulate. Traditional regulatory approaches often fall short in this regard,
relying on rigid, one-size-fits-all mechanisms that fail to accommodate the complexity and variabil-
ity of AI applications. In contrast, the ETHOS model leverages blockchain technology as its core
infrastructure to address these limitations.
The recent opinion by the European Data Protection Board (EDPB) on the use of personal data
in the development and deployment of AI models underscores the critical need to balance innovation
with the protection of fundamental rights under the General Data Protection Regulation (GDPR)
(EDPB, 2024). The EDPB’s three-step test for evaluating legitimate interest, particularly its focus
on necessity and proportionality in data processing, aligns closely with the principles embedded
in the ETHOS framework. ETHOS operationalizes these principles through dynamic compliance
tools such as blockchain-based audit trails and privacy-preserving technologies like Zero-Knowledge
Proofs (ZKPs), ensuring that data processing adheres to the necessity test while respecting the data
minimization principle. Moreover, ETHOS introduces a structured and scalable approach to the
balancing test via its tiered risk classification system and decentralized governance model, enabling
transparent assessment of how controllers’ interests align with the rights and expectations of data
subjects. By leveraging participatory mechanisms, including DAOs, ETHOS directly addresses
the EDPB’s call for context-aware evaluations of data subjects’ reasonable expectations. These
innovations establish the ETHOS framework as a complementary tool to GDPR guidelines, offering
a robust and forward-looking solution for the complexities of AI governance in a rapidly evolving
technological landscape.
However, it is important to mention that our paper on the ETHOS framework faces several
18
limitations that could impact its effectiveness in regulating AI agents. Its reliance on tiered risk
categorization risks oversimplifying the nuanced nature of AI applications, as many agents may
exhibit hybrid risk profiles and could be part of swarms (i.e, a group of AI agents). Implementation
challenges arise from the difficulty of achieving global consensus on ethical principles and governance
standards, which may lead to fragmented adoption. While this paper is aspirational and takes a
systems-thinking approach, a lot of the conceptual work attempts to bridge many elements together
that may be incompatible or require extensive optimization in real-world conditions. Indeed, the
absence of empirical validation leaves questions about the framework’s real-world applicability, but
it could be tested through pilot programs in specific industries—such as finance—where outcomes
can be measured for ethical compliance, risk mitigation, and societal impact. Such testing would
provide valuable insights for policymakers refining regulations, developers optimizing AI system
designs, and end users benefiting from safer, more transparent AI technologies.
Our conceptual work is uniquely positioned as a keystone paper in this emerging field of study.
As this is an emerging field, it is difficult to account for all the works being published, partic-
ularly papers with ambitious aims such as ours, which are typically posted on open repositories
(i.e., arXiv, SSRN, etc.) or published as conference papers. Very recent and important papers
include work by Chan et al. (2024), which explores key mechanisms to facilitate the governance of
increasingly autonomous AI agents, emphasizing the importance of visibility in ensuring account-
ability and oversight. The study identifies three primary visibility mechanisms: agent identifiers,
real-time monitoring, and activity logs (Chan et al., 2024a). Adler et al. (2024) explore the
concept of personhood credentials as a means to enhance trust and security on online platforms,
particularly in the face of increasingly sophisticated AI systems (Adler et al., 2024). Furthermore,
Chan et al. (2024b) propose a framework for assigning IDs to AI system instances to enhance ac-
countability and accessibility, particularly in impactful settings like financial transactions or human
interactions, while acknowledging adoption incentives, implementation strategies, and associated
risks. Domkundwar et al. (2024) propose and evaluate three safety frameworks—an LLM-powered
input-output filter, a safety agent, and a hierarchical delegation system with embedded checks—to
mitigate risks in AI systems collaborating with humans, demonstrating their effectiveness in en-
hancing safety, security, and responsible deployment of AI agents (Domkundwar et al., 2024). On
matters related to societal integration, Bernardi and colleagues (2024) highlight the importance
of defensive AI systems, international cooperation, and targeted investments to enhance societal
resilience, secure adaptation, and mitigate risks posed by advanced AI technologies (Bernardi et al.,
2024). Furthermore, to assist with societal integration and risk management, Gipiˇskis et al. (2024)
present a comprehensive catalog of risk sources and management measures for general-purpose AI
(GPAI) systems, spanning technical, operational, and societal risks across development, training,
and deployment stages (Gipiˇskis et al. (2024), offering a neutral and publicly accessible resource
to assist stakeholders in AI governance and standards development.
Our paper contributes to a new field called ”Decentralized AI Governance”, which explores
the integration of decentralized technologies—such as blockchain, smart contracts, and DAOs—to
regulate, monitor, and ensure the ethical alignment of increasingly autonomous AI systems (Kaal,
2024). Recently, Krishnamoorthy (2024) introduced a novel governance framework for Artificial
General Intelligence (AGI) that integrates Human-in-the-Loop (HITL) approaches with blockchain
technology to ensure robust and adaptable oversight mechanisms, thereby helping establish a new
academic field of AI governance through blockchain. It is critical to mention that Gabriel Montes
and Ben Goertzel (2019) are widely regarded as pioneers in decentralized AI (Montes and Goertzel,
2019), and that their vision of democratizing access to AI systems and ensuring ethical alignment
through decentralized infrastructure has been instrumental in shaping this emerging field.
19
8 Conclusion
Our main contribution to the literature lies in the conceptualization and introduction of the ETHOS
(Ethical Technology and Holistic Oversight System) framework, a global, decentralized governance
model specifically tailored to regulate AI agents. Unlike existing works that focus on isolated mech-
anisms or risk management strategies, our work provides a comprehensive and unified approach
to address the unique challenges posed by AI agents—autonomous systems capable of reasoning,
learning, and adapting. The ETHOS framework leverages Web3 technologies such as blockchain,
smart contracts, DAOs, SSI, and SBTs to establish a decentralized global registry for AI agents.
By integrating risk-based categorization (unacceptable, high, moderate, minimal) with proportional
oversight mechanisms, ETHOS bridges the gap between philosophical understandings of AI agents’
autonomy, decision-making complexity, adaptability, and impact potential, and practical strategies
for governance. This approach ensures dynamic risk classification, real-time monitoring, automated
compliance enforcement, and enhanced transparency, while addressing concerns around centraliza-
tion, accountability, and societal integration. Therefore, by combining philosophical, technical,
and operational insights into a cohesive model, ETHOS aims to lay the foundation for a resilient,
inclusive, and adaptive governance system, contingent upon successful implementation, empirical
validation, and broad adoption across diverse stakeholders, which could in turn meet the demands
of an AI-driven future.
Acknowledgments
We would like to thank the Valhalla group for their helpful discussions in the preparation of this
manuscript. We acknowledge the use of Chat Generative Pre-Trained Transformer (ChatGPT),
developed by OpenAI, in assisting with the drafting, refinement, and editing throughout the paper.
The researchers did not receive any funding for this study. The authors hold the belief that the rule
of law is the cornerstone of a just society. This work seeks to extend these enduring principles to
address the novel challenges posed by AI agents, ensuring that innovation is aligned with societal
values.
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