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AI governance must treat training data as a collective resource

By PulseAugur Editorial · [3 sources] ·

Rohini argues that AI training data should be treated as a collectively stewarded resource, emphasizing community consent, attribution, and benefit-sharing. She highlights that public knowledge repositories like Wikipedia are exploited by LLMs without sustaining the commons, and that copyright reform alone is insufficient. For the Global Majority, underrepresentation in training data is a critical issue, and simply scraping data without community control constitutes 'extractive inclusion' and epistemic violence. AI

IMPACT Calls for new AI governance models that prioritize community consent and benefit-sharing for training data.

RANK_REASON The cluster consists of opinion pieces discussing AI governance and data stewardship.

Read on Mastodon — sigmoid.social →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

COVERAGE [3]

  1. Mastodon — sigmoid.social TIER_1 English(EN) · [email protected] ·

    Better # governance would treat training data as a collectively stewarded resource. That means community # consent or refusal, attribution standards, benefit-sh

    Better # governance would treat training data as a collectively stewarded resource. That means community # consent or refusal, attribution standards, benefit-sharing, and lessons from indigenous data sovereignty. Publicly funded datasets, multilingual corpora, and open models can…

  2. Mastodon — sigmoid.social TIER_1 English(EN) · [email protected] ·

    Public knowledge repositories such as # Wikipedia are commons built by volunteer labor, donations, and public-interest governance. LLMs extract value from them,

    Public knowledge repositories such as # Wikipedia are commons built by volunteer labor, donations, and public-interest governance. LLMs extract value from them, but that value rarely flows back to sustain the commons. # Copyright reform alone cannot solve this. AI firms can invok…

  3. Mastodon — sigmoid.social TIER_1 English(EN) · [email protected] ·

    For the Global Majority world, the dilemma is sharp. Many languages and knowledge traditions are underrepresented in training data. But “fixing” that via ungove

    For the Global Majority world, the dilemma is sharp. Many languages and knowledge traditions are underrepresented in training data. But “fixing” that via ungoverned scraping turns communities into data sources, not decision-makers. I argue that is 'extractive inclusion': being in…

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