# Community governance `kaal:entity:community-governance` **Status.** derived This node is assembled mechanically from the 10 claims that carry the concept tag `community-governance`. It is a roster of what the corpus says under this term. It is **not** an adjudicated definition: no single statement here has been ruled canonical, and no first-appearance call has been made. Read the claims and judge for yourself. ## Every claim under this term 10 claims across 4 works, 2021 to 2024. **2021** - [3981021-002](https://wulfkaal.github.io/claims/3981021-002) [mechanism/asserted] -- Decentralized autonomous organizations combine feedback loops and transparency features with community governance, and that combination addresses the shortcomings of charitable organizations in centralized structures. > DAOs combine unique feedback loops and transparency features with community governance that address the existing shortcomings of charitable organizations in centralized structures. Wulf A. Kaal, How Decentralized Autonomous Organizations Optimize Charitable Giving (2021). SSRN: https://ssrn.com/abstract=3981021 - [3981021-016](https://wulfkaal.github.io/claims/3981021-016) [mechanism/argued] -- The public commenting function on the forum lets the CHARITYxDAO internalize information from the edges of the charity ecosystem that would otherwise have no agency in any charitable organization, and that new information further increases the accountability of the DAO. > The public commenting function on the forum helps the CHARITYxDAO internalize information form the edges of the charity ecosystem that otherwise would have no agency in any charitable organization. This new information from the edges of the charity ecosystem further increases the Wulf A. Kaal, How Decentralized Autonomous Organizations Optimize Charitable Giving (2021). SSRN: https://ssrn.com/abstract=3981021 - [3981021-019](https://wulfkaal.github.io/claims/3981021-019) [mechanism/argued] -- By giving the power of the endowment to the DAO the donor relinquishes control over the assets in exchange for community governance, and that relinquishment builds trust and community buy in, which in turn secures growth and legacy for the donor's purpose. > The donor can help mold the community to some extnt but at the same time the donor releases the control to the community. The relinquishment of control builds trust and community buy in. This in turn, ensures growth and legacy for the respective purpose of the donor and the associated DAO. Wulf A. Kaal, How Decentralized Autonomous Organizations Optimize Charitable Giving (2021). SSRN: https://ssrn.com/abstract=3981021 **2024** - [4796714-034](https://wulfkaal.github.io/claims/4796714-034) [mechanism/argued] -- Routing proposals through the Forum and then through Validation Pool review is what allows the input parameters and learning data of AI systems to be governed by expert community consensus, because only vetted and consensus backed data and parameters reach AI development. > This process involves submitting proposals to the Forum and undergoing Validation Pool review, ensuring that only vetted and consensus-backed data and parameters are utilized in AI development. Wulf A. Kaal, AI Governance (2024). SSRN: https://ssrn.com/abstract=4796714 - [4796714-038](https://wulfkaal.github.io/claims/4796714-038) [mechanism/argued] *(failure mode)* -- Broad community governance of AI training identifies and mitigates bias more effectively than data validation alone, because validation focused approaches can overlook systemic biases already embedded in the pretrained model. > Moreover, involving a broad community in the governance of AI training can help identify and mitigate biases more effectively than a focus on data validation alone, which might overlook systemic biases embedded in the pretrained models. Wulf A. Kaal, AI Governance (2024). SSRN: https://ssrn.com/abstract=4796714 - [4796714-039](https://wulfkaal.github.io/claims/4796714-039) [normative/argued] -- 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 place. > Yet, Model 3: "Web 3 Community Governance - Decentralized Data - AI Model" stands out for several reasons, potentially offering a superior approach when compared to the other two models. Wulf A. Kaal, AI Governance (2024). SSRN: https://ssrn.com/abstract=4796714 - [4796714-040](https://wulfkaal.github.io/claims/4796714-040) [failure/argued] *(failure mode)* -- The hybrid model falls short of full community co-governance because it relies on a select group of experts for data validation and therefore may not capture the diverse perspectives and expertise of the broader community. > This is a significant step beyond the hybrid approach, which, while incorporating community input, primarily relies on a select group of experts for data validation and may not fully capture the diverse perspectives and expertise of a broader community. Wulf A. Kaal, AI Governance (2024). SSRN: https://ssrn.com/abstract=4796714 - [4855607-030](https://wulfkaal.github.io/claims/4855607-030) [condition/asserted] -- The feedback effects that make community governance of federated learning work will not materialize unless expert community members are selected coherently, making coherent expert selection a precondition of the mechanism rather than an optional refinement. > The key is to select the expert community members coherently to allow for the feedback effects for FL to materialize. Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607 - [4941807-003](https://wulfkaal.github.io/claims/4941807-003) [design/argued] -- Kaal advocates an ex-ante governance approach within Web3 frameworks in which community coordinated regulatory measures and oversight mechanisms are set during the development phase of AI technologies rather than imposed after deployment. > In contrast, an ex-ante governance approach, advocated within Web3 frameworks as presented in this paper, involves setting community coordinated regulatory measures and oversight mechanisms during the development phase of AI technologies. Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024). SSRN: https://ssrn.com/abstract=4941807 - [4941807-028](https://wulfkaal.github.io/claims/4941807-028) [design/argued] -- Kaal's proposed answer to the decentralized governance needs of AI is to implement Decentralized Autonomous Organizations that govern AI through expert community consensus. > One promising way to address the decentralized governance needs of AI pertains to the implementation of Decentralized Autonomous Organizations (DAOs) for the governance of AI through expert community consensus. Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024). SSRN: https://ssrn.com/abstract=4941807 ## Verify Every claim above resolves to a record carrying a verbatim source quote, the sha256 of the source PDF, and a preformatted citation. Nothing here asks to be taken on trust. curl -s https://wulfkaal.github.io/entities/community-governance.md | sha256sum **Canonical form.** This markdown file is the canonical hashed representation of this entity node. Its sha256 is the content hash.