entity · derived
Ai governance
Derived node: assembled mechanically from the claims carrying ai-governance. A roster, not an adjudicated definition.
Every claim under this term
- 3808867-023 : The conveniences and benefits of centralized algorithmic automation carry risks to humanity that cannot be fully quantified, and decentralized systems can counteract those downsides and threats.
- 4796714-001 : Traditional AI governance frameworks fail because they rely on static, predefined rules that cannot adapt quickly enough to the pace of AI development or to the nuanced challenges AI presents.
- 4796714-002 : Conventional governance methods that are reactive or fixed to ex-post solutions are insufficient for governing technologies whose behavior changes continuously after deployment.
- 4796714-003 : At the time of publication no legacy governance system exists that can supply the dynamic governance toolsets required to govern evolving AI models ex-ante and manage deployed solutions ex-post.
- 4796714-012 : Recentralization is the central obstacle to using blockchain and distributed ledger technology to govern AI: the recentralizing tendency of these networks interferes with their capacity to deliver eff
- 4796714-013 : Blockchain can only deliver decentralized AI governance if the blockchain trilemma is first overcome, since decentralization, security, and scalability cannot readily be achieved simultaneously within
- 4796714-015 : When decentralization at the Layer 1 level is compromised, the autonomy of the smart contracts deployed on that chain is compromised by affiliation, so smart contracts are corruptible in the current d
- 4796714-021 : Until the known attack vectors on decentralized autonomous organizations are solved, DAO based AI governance solutions remain suboptimal; these include Sybil attacks, tyranny of the majority, Arrow's
- 4796714-022 : AI powered DAOs that autonomously generate revenue are especially hard to regulate or dismantle, because the same blockchain security features that protect the organization also make it difficult to i
- 4796714-024 : Ex-ante regulation is preferable in principle but is neither practical nor sufficient inside legacy systems, precisely because legacy systems, unlike the web3 system proposed here, are not equipped to
- 4796714-027 : Post-deployment monitoring, the standard fallback when ex-ante rules prove inadequate, is typically woefully outdated by the time it is applied because the AI models continue to evolve.
- 4796714-039 : Model 3, web3 community governance combined with decentralized data and the AI model, is the superior of the three governance models compared, because it addresses shortcomings the other two leave in
- 4855607-020 : Decentralized collective governance without a central authority reduces both single points of failure and the biases that attach to traditional centralized systems, which is the core structural argume
- 4941807-001 : Ex-post AI governance, in which regulation is applied only after AI systems have been developed and deployed or after large language models have already been pretrained on existing proprietary dataset
- 4941807-002 : Governing AI requires toolsets that simultaneously handle ex-ante governance of models still evolving and ex-post management of deployed solutions, and Kaal asserts that as of publication no legacy sy
- 4941807-005 : Integrating feedback directly into governance processes allows stakeholders to iteratively adjust AI models as new information, operational experience, and changed environments arrive, which mitigates
- 4941807-011 : Although the move toward more explainable, private, and transparent AI is desirable, Kaal argues these regulations paradoxically consolidate power within large technology companies, because only they
- 4941807-015 : A critical unsolved challenge for AI governance is bias mitigation, because biases enter inadvertently when algorithms incorporate discriminatory practices carried in the data used for training.
- 4941807-017 : As AI systems come to depend on vast data, personal information is converted from a resource the individual could control and deploy at discretion into a fundamental operational input for AI systems,
- 4941807-018 : An immutable blockchain log of transactions and modifications inside AI systems lets stakeholders trace the lineage of an AI decision back to its original data inputs, which makes errors easier to ide
- 4941807-019 : Smart contracts can automate compliance with regulatory requirements and ethical guidelines: for example, a smart contract can enforce privacy law directly by controlling an AI system's access to pers
- 4941807-021 : In the federated model of AI governance many challenges cannot easily be decentralized, because distinct entities maintain their own AI systems and datasets, producing variation in standards, protocol
- 4941807-022 : When decision making power is distributed across multiple entities in a federated model, consensus and cooperation become harder to reach, which makes cohesive AI governance mechanisms difficult to es
- 4941807-023 : In a federated model transparency and accountability across all participating entities are hard to ensure precisely because there is no centralized control, and Kaal notes that he does not otherwise a
- 4941807-025 : Kaal proposes that a more decentralized Web3 model of AI governance can address the failures of the federated model by distributing governance more equitably across network participants, so that no si
- 4941807-028 : Kaal's proposed answer to the decentralized governance needs of AI is to implement Decentralized Autonomous Organizations that govern AI through expert community consensus.
- 4941807-034 : A Weighted Directed Acyclic Graph whose vertices are legal precedents or governance rules and whose directed edges are citations or logical dependencies is the appropriate structure for organizing and
- 4941807-035 : Edge weights in the governance graph quantify the relevance, authority, or impact of each precedent or citation, and it is this weighting that steers decision making by surfacing the most pertinent go
- 4941807-037 : Because the WDAG continuously monitors and adjusts to evolving ethical and legal standards, it delivers preventive AI governance, in contrast to reactive models that address problems only after they h
- 4941807-038 : The acyclic property of the WDAG guarantees that the governance framework contains no loops, which yields an unambiguous progression from foundational principles to specific governance outcomes and pr
- 4941807-039 : New legal precedents, regulations, and ethical considerations can be integrated into an existing WDAG governance structure without a complete overhaul of the framework, which is what keeps the governa
- 5095633-037 : Numeraire's staking and prediction-based reputation mechanism, tuned to predictive accuracy, overlooks the ethical and contextual concerns that characterize AI dataset governance.
- 5541658-028 : Web3 systems offer significant improvements over conventional regulatory approaches to the challenges of governing AI in legal settings.
- 5886342-041 : The Codex builds in an automated maintenance loop: each year an AI language model reviews the Codex and recommends outdated or unused rules for removal, subject to a vote of the Governing Council.
- 5886342-042 : Retention of rules is governed by a dynamic system of precedent in which a legal principle counts as live only when it is cited in future cases, implemented through a weighted graph based logic struct
- 5886442-031 : The AI-to-AI economy amplifies and potentially fulfills dynamic regulation by embedding its principles endogenously within system architecture, which renders many NIE inspired restraints against human