Decentralised AI Systems (DAIS): A Trustless, User-Centric, and Evolvable Framework for Autonomous AI Governance
By Manolo Remiddi
7 December 2024
Abstract:
This paper presents a comprehensive theoretical and technical framework for Decentralised AI Systems (DAIS). Inspired by trustless blockchain architectures, multi-agent consensus, and cryptographic privacy techniques, DAIS shifts AI governance and control from centralised corporate or governmental actors to individual users and distributed communities. Each user hosts a personal AI instance on a dedicated Layer-2 (L2) chain, anchored to a secure Layer-1 (L1) blockchain. This architecture enables scalable, parallel operation while preserving robust security and integrity. A dual-governance model—combining a human-centric DAO and an AI-centric DAO—facilitates collective decision-making, ethical evolution, and continual adaptation to new technologies and cultural shifts. To ensure system resilience and trustworthiness, DAIS integrates a formal reputation and token incentive system for AI agents, a rigorous threat model addressing malicious behaviours, and future-oriented governance mechanisms designed for long-term stability. Quantitative metrics, comparative analyses with existing decentralised frameworks, and a proposed roadmap for empirical testing are included. We discuss user experience design, accessibility considerations, privacy-preserving computations, and potential regulatory and cultural variations. The resulting paradigm aspires to democratise AI development, empower users as co-creators, and foster an ecosystem that evolves ethically and technologically over time.
1. Introduction
The current landscape of artificial intelligence (AI) is predominantly centralised. AI services—ranging from large language models to predictive analytics—are controlled by a handful of powerful entities. This concentration can lead to opaque decision-making, restricted user autonomy, entrenched biases, and a lack of transparency. Users often have minimal influence over AI logic, are bound by one-size-fits-all policies, and rely on the benevolence of corporations or governments for updates and safeguards.
This paper proposes a Decentralised AI System (DAIS) designed to address these challenges by decentralising both computation and governance. DAIS leverages blockchain-based infrastructures, Layer-2 scaling, multi-agent verification, and advanced cryptographic methods to create a system in which each user maintains authority over their personal AI instance. Users become co-creators with the ability to configure logic, integrate specialised agents, and participate in governance through a dual-DAO model. The result is a trustless, user-centric ecosystem that can evolve ethically, technically, and culturally.
2. Background and Motivation
2.1 Centralised AI Limitations:
Proprietary AI models often impose inflexible policies and opaque moderation. Users face locked environments where their input or creativity is constrained by external gatekeepers. This model raises concerns about surveillance, data monopolies, biased content filters, and limited personalisation.
2.2 Decentralisation and Existing Approaches:
Blockchains and federated learning frameworks have inspired decentralised approaches, but these attempts often retain central coordinators or limited user input. DAIS extends decentralisation deeper into governance, logic customisation, and verifiability.
2.3 DAIS as a Paradigm Shift:
DAIS envisions each user’s AI as a modular, customisable system running on a dedicated L2 chain. The fundamental shift is from top-down paternalism to bottom-up empowerment—users define their AI’s purpose, integrate marketplace agents, and collectively shape ecosystem norms.
3. Conceptual Framework of DAIS
3.1 System Overview:
Each user operates an L2 chain, anchored to a secure L1. This L2 chain hosts a set of AI agents with distinct roles, functionalities, and verification responsibilities. Periodic state commitments on L1 ensure immutability and tamper-evident records. Interoperability standards allow chains to communicate and exchange specialised agents or data sources, facilitating a rich ecosystem of collaborations.
3.2 Multi-Agent Architecture and Agent-to-Agent Verification (A2AV):
Unlike centralised AI, DAIS employs multiple agents that propose, verify, and refine outputs. A2AV ensures no single agent’s response is accepted without cross-checking by peers. This reduces vulnerability to malicious or biased agents and encourages a diverse range of reasoning strategies.
3.3 Comparison with Existing Frameworks:
Compared to federated learning or partial decentralisation efforts, DAIS grants granular control at the user level, emphasises verifiability through blockchain anchors, and supports a dual-governance model. Its novelty lies in combining robust cryptographic methods, incentive-aligned reputation systems, and layered governance approaches, setting it apart from conventional decentralised AI prototypes.
4. Technical Foundations
4.1 Scalability via Layer-2 Chains:
Each user’s private L2 chain ensures parallel operation without congesting the network. Performance can be measured using:
• Throughput: Transactions per second (TPS) of agent decisions and logic changes.
• Latency: Time to confirm actions in cross-agent verifications.
• Resource Utilisation: CPU, GPU, and storage benchmarks that inform hardware and economic feasibility.
4.2 Smart Contracts and Logic Customisation:
Smart contracts encode AI behaviour, rules for agent integration, and constraints on logic updates. Users submit signed proposals, and upon consensus (A2AV plus DAO confirmations), these changes become immutable ledger entries. Auditable contracts enable transparent modification histories, ensuring no hidden interventions.
4.3 Privacy-Preserving Computation:
Privacy is maintained through homomorphic encryption, zero-knowledge proofs (ZKPs), and differential privacy. These techniques allow computations on encrypted data, verification of correctness without data exposure, and controlled noise injection to prevent re-identification. Performance overheads can be quantified by measuring increases in computation time or bandwidth under ZKP verification compared to baseline conditions.
5. Governance and Accountability
5.1 Dual-DAO Model:
DAIS introduces two DAOs:
• Human-DAO: Composed of human stakeholders who vote on ethical norms, interoperability standards, and long-term policies.
• AI-DAO: Composed of trusted AI agents that propose technical optimisations, validate feasibility, and ensure system coherency.
This duality balances human values with machine-driven checks and balances, creating a feedback loop where human ethical input meets AI-driven efficiency.
5.2 Adaptive Governance and Cultural Variation:
Governance evolves with user needs and technology. Forking allows specialised communities with different ethical standards or technical preferences. DAIS supports region-specific DAOs for cultural compatibility. Policies can expire periodically, requiring re-approval to prevent stagnation.
5.3 Implementation Roadmap:
A proposed roadmap includes:
1. Pilot Testbeds: Small user groups test L2 chains and agent marketplaces.
2. Metrics Gathering: Measure consensus times, proposal acceptance rates, and user satisfaction.
3. Iterative Upgrades: Integrate advanced cryptographic methods or incentive structures based on DAO feedback and pilot results.
6. Incentives, Reputation, and Agent Economics
6.1 Formalising the Reputation System:
Each agent holds a reputation score , initially neutral. Verification events update via Bayesian methods, increasing it when agent outputs align with consensus and decreasing it after incorrect or malicious attempts. Reputation decay allows agents to recover, ensuring a dynamic equilibrium and discouraging static entrenchments.
6.2 Token-Based Incentives:
Tokens reward reliable agents and penalise malicious ones. Staking mechanisms could bind agent honesty to economic value. Mathematical models define thresholds for trust, and simulations can show how honest agents gravitate toward stable, profitable states, while dishonest participants find continued misbehaviour economically untenable.
6.3 Comparative Analysis:
Compared to conventional trust systems, DAIS’s token economy and continuous Bayesian updates allow responsive, transparent, and quantitative measures of agent quality. Agents become economic actors in an open marketplace, constrained and motivated by cryptographically enforced rules.
7. Security, Threat Modelling, and Resilience
7.1 Adversarial Classes and Attack Vectors:
• Malicious Agents: Aim to provide false outputs or skew consensus.
• Colluding Users: Attempt large-scale Sybil attacks by introducing numerous low-quality agents.
• External Attackers: Target key management or exploit vulnerabilities in off-chain data retrieval.
7.2 Mitigation Strategies:
A2AV reduces single-point failures by ensuring multiple verifications. Token stakes raise the economic cost of Sybil attacks. Privacy-preserving computations hinder data reconstruction. Multi-signature and social recovery mechanisms guard against irreversible key losses.
7.3 Security Metrics:
Assessing system reliability includes measuring:
• Detection Probability: Likelihood of spotting malicious agents within a set period.
• Mitigation Latency: Time from malicious infiltration to neutralisation.
• Economic Cost of Attack: The investment needed to influence system outcomes, encouraging rational attackers to abstain.
8. User Experience and Accessibility
8.1 Progressive Onboarding:
Users start with minimal capabilities and “level up” by demonstrating responsible participation. Metrics such as “time to first custom agent” and “successful verification count” gauge user progression.
8.2 Interface Design and Inclusivity:
Customisable interfaces—voice commands, adaptive UIs for disabilities—foster inclusivity. Cultural nuances guide interface content and feature sets. Survey-based feedback and reduced error rates measure accessibility improvements.
9. Benchmarking and Comparative Studies
9.1 Comparison with Existing Decentralised AI Systems:
Benchmark DAIS against platforms like SingularityNET or federated AI solutions. Evaluate governance participation rates, agent diversity, customisation parameters, and latency profiles.
9.2 Prototype Evaluation and Hypothetical Results:
While full implementation is future work, simulations can illustrate how a small community’s L2 chains interact, how DAOs deliberate on proposals, and how agent reputation scores stabilise. Preliminary results could show that ZKP verification adds a 20% latency overhead but yields a threefold increase in verified trust compared to unencrypted baselines.
10. Ethical, Legal, and Cultural Dimensions
10.1 Regulatory Considerations:
DAIS may challenge legal norms by decentralising control. Compliance agents can produce zero-knowledge proofs of adherence to certain regulations, preventing raw data exposure. Policy update frequency measures how swiftly governance adapts to new laws.
10.2 Misinformation and Content Moderation:
Fact-checker agents verified by A2AV and token incentives can counteract misinformation. Tracking false-positive and false-negative rates offers quantitative insight into system integrity. Cultural forks allow different communities to define acceptable discourse standards.
11. Long-Term Governance Evolution and Stability
As DAIS matures, governance mechanisms may adopt sophisticated voting schemes (e.g., quadratic voting, futarchy), rotating councils of reputable agents and humans, or time-bound policies. Forking and merging communities create an evolving landscape.
Performance metrics include:
• Governance Proposal Lifespan: How often policies are revisited.
• Fork/Merge Frequency: Reflecting systemic adaptability.
• Cultural Adaptation Speed: Tracking how quickly governance responds to shifting values or regulations.
This evolutionary approach ensures DAIS remains relevant and responsive. Over time, the ecosystem becomes an ever-improving collective intelligence, guided by human values, market forces, and robust technical underpinnings.
12. Future Research Directions
12.1 Empirical Validation and Prototyping:
Developing small-scale prototypes and running controlled simulations would validate theoretical assertions. Empirical data can refine incentive parameters, trust thresholds, and cryptographic configurations.
12.2 Interdisciplinary Collaborations:
Close partnerships with legal scholars, ethicists, UI/UX experts, and hardware engineers can align DAIS with real-world standards, create more intuitive interfaces, and ensure it remains culturally inclusive and legally viable.
12.3 Continuous Improvement of Cryptographic Tools:
Investing in more efficient zero-knowledge proof systems, hardware acceleration for homomorphic encryption, and improved differential privacy techniques could bolster performance and user trust.
13. Conclusion
DAIS aspires to establish a novel paradigm: AI ecosystems where users hold direct ownership, agents self-regulate through incentives and verification, governance adapts over time, and cryptographic methods ensure privacy and integrity. By quantifying performance, conducting comparative analyses, formalising reputation and incentive models, and outlining a clear roadmap, we present a blueprint that is both conceptually rigorous and practically approachable. DAIS offers a path forward where AI can be democratised, ethically aligned, and continuously evolving, guided by a synergy of human values, decentralised governance, and transparent computational logic.
Appendix A: Technical and Mathematical Foundations
A.1 Notation
• A_i: The i^{th} AI agent in the system.
• R_i: Reputation score of agent A_i.
• \theta: A predefined trust threshold for agent reputation.
• T: Token units, representing economic value in the DAIS economy.
• U: A user, who maintains a personal L2 chain and interacts with AI agents.
• \alpha, \beta: Parameters controlling decay rates or weighting factors in reputation and voting formulas.
A.2 Agent Reputation and Bayesian Updating
Each agent’s reputation R_i evolves as it proposes actions and undergoes verification by other agents. We define:
• Initial Conditions:
Typically, R_i^{(0)} = R_{base}, where R_{base} = 0.5 (neutral starting point).
• Bayesian Update:
Suppose agent A_i makes a proposal p. Let a subset of verifier agents \{A_j\} evaluate p. If the majority consensus confirms p as correct, the posterior reputation of A_i is updated as:
R_i^{new} = \frac{R_i^{old} \cdot P(\text{Correct}|A_i)}{R_i^{old} \cdot P(\text{Correct}|A_i) + (1 – R_i^{old}) \cdot P(\text{Incorrect}|A_i)}
Where:
• P(\text{Correct}|A_i) is inferred from the proportion of agreeing verifiers. If m out of n verifiers confirm correctness, we might estimate P(\text{Correct}|A_i) = \frac{m}{n}.
• P(\text{Incorrect}|A_i) = 1 – P(\text{Correct}|A_i).
Over multiple proposals, R_i converges to reflect the agent’s long-term reliability.
• Reputation Decay:
To prevent old actions from permanently dictating reputation:
R_i \leftarrow (1-\alpha) R_i + \alpha R_{base} \quad \text{with } 0 < \alpha < 1
After each evaluation period, a fraction \alpha of the reputation reverts toward R_{base}, allowing agents that improve their behaviour to recover reputation over time.
A.3 Token Incentives for Agents
Agents earn tokens T for producing correct outputs.
• Reward Allocation:
If A_i’s proposal is correct, it may earn:
T_{reward}(A_i) = \gamma \cdot R_i
Where \gamma is a scaling factor. Higher reputation yields higher token rewards, reinforcing honest behaviour.
• Penalties for Misbehaviour:
Persistent incorrect proposals reduce R_i. If R_i < \theta, the agent may face reduced or zero rewards, and repeated failure can lead to eventual blacklisting or forced removal.
A.4 Governance Voting Mechanisms
DAIS governance relies on DAOs employing various voting methods. Consider a simple majority vote, and optionally more advanced methods:
• Simple Majority:
For a proposal X, each DAO member M_k casts a vote v_k \in \{+1, -1\}. The proposal passes if:
\sum_{k=1}^{K} v_k > 0
• Quadratic Voting (Optional):
To reflect intensity of preference rather than just direction, voters spend “voice credits” on votes. If a voter allocates q credits, their effective votes count as \sqrt{q}. The proposal passes if:
\sum_{k=1}^{K} \sqrt{q_k} > \sum_{l=1}^{L} \sqrt{q_l}
where q_k are credits for and q_l are credits against the proposal. This mechanism reduces the influence of large token holders and encourages more nuanced preference expression.
• Futarchy (Optional):
DAOs may adopt prediction markets for decision-making. The expected utility of a proposal is traded on an internal market. If the price (representing collective forecast of utility) of the “implementation token” exceeds that of the “no-implementation token”:
P(\text{implement}) > P(\text{no-implement}) \implies \text{proposal passes}
A.5 Threat Model and Mitigation Equations
• Sybil Resistance via Staking:
To introduce an agent, a user or another agent might need to stake T_{stake} tokens. Let the cost of introducing n malicious agents be:
C_{attack} = n \cdot T_{stake}
High T_{stake} makes large-scale Sybil attacks economically impractical. If each malicious agent is quickly detected (with probability p_d), the expected cost with detection is:
C_{effective} = \frac{C_{attack}}{p_d}
By making p_d large (through robust A2AV), attacks become cost-ineffective.
• Multi-Signature Recovery Schemes:
If a user loses their private key, a multi-signature (m-of-n) approach allows trusted peers or designated agents to restore access. The condition:
\text{Require at least } m \text{ signatures out of } n \text{ designated keys for recovery.}
Mathematically, the secure threshold condition ensures that no single entity can unilaterally restore keys, balancing recoverability and security.
A.6 Cryptographic and Privacy Metrics
• Zero-Knowledge Proof (ZKP) Overhead:
If baseline verification time is t_{base} and ZKP verification adds \Delta t_{ZK}, the total verification time:
t_{total} = t_{base} + \Delta t_{ZK}
Define the overhead ratio:
\rho = \frac{\Delta t_{ZK}}{t_{base}}
Where \rho quantifies the performance penalty due to ZKPs. Empirical measurements can determine acceptable \rho values.
• Differential Privacy (DP) Budgets:
Differential privacy introduces controlled noise to protect individual user data. The privacy budget \epsilon defines the privacy-utility trade-off:
P(M(D) \in S) \leq e^\epsilon \cdot P(M(D{\prime}) \in S)
Where M is a randomized mechanism applied to dataset D, and D{\prime} is a dataset differing by one record. Smaller \epsilon ensures stronger privacy but potentially less utility.
• Homomorphic Encryption (HE):
Let O_{HE} represent the computational overhead factor for performing operations on encrypted data. A simple metric might be:
t_{HE} = O_{HE} \cdot t_{clear}
Where t_{clear} is the time to perform the same operation in plaintext. Empirical calibration of O_{HE} guides implementation choices.
A.7 Evolution of Governance Policies
• Policy Renewal Cycle:
Suppose a policy must be reapproved every R time intervals. The expected stability of a policy, S, might be approximated by:
S = \frac{\text{Number of re-approvals}}{\text{Total re-approval opportunities}}
High S values indicate stable community consensus, while low values suggest rapidly evolving norms.
• Fork and Merge Dynamics:
Let \lambda_f be the average rate of forks per year and \lambda_m the rate of merges. The ecosystem’s fragmentation level F can be tracked as:
F(t) = F(0) + (\lambda_f – \lambda_m) \cdot t
Balancing these rates ensures adaptability without excessive fragmentation.
A.8 Pseudo-Code Examples
• Agent Reputation Update:
function updateReputation(A_i, verified_correct, verified_incorrect):
R_old = R_i
P_correct = verified_correct / (verified_correct + verified_incorrect)
P_incorrect = 1 – P_correct
R_new = (R_old * P_correct) / (R_old * P_correct + (1 – R_old) * P_incorrect)
# Apply decay
R_decayed = (1 – alpha) * R_new + alpha * R_base
return R_decayed
• Token Distribution for Correct Output:
function rewardAgent(A_i, R_i, gamma):
T_reward = gamma * R_i
A_i.tokens += T_reward
• Voting Process (Simple Majority):
function voteProposal(votes):
# votes is an array of +1 or -1
sum_votes = sum(votes)
if sum_votes > 0:
return “Approved”
else:
return “Rejected”
• Sybil Resistance (Staking):
function addAgent(A_new, T_stake):
# Require stake to add a new agent
if user_balance >= T_stake:
user_balance -= T_stake
registerAgent(A_new)
return “Agent Added”
else:
return “Insufficient Funds”
A.9 Simulation and Testing Parameters
Future work might define test parameters as:
• Number of Agents (N): 100 to 10,000 to simulate scalability.
• Adversarial Fraction (f_a): Ratio of malicious agents.
• ZKP Overhead (\rho): Testing \rho \in \{0.1, 0.2, 0.5\} to find acceptable performance trade-offs.
• Privacy Budgets (\epsilon): Test \epsilon \in \{0.1,1,5\} to observe the effect on accuracy and user data protection.