arXiv:2606.16167v1 Announce Type: new Abstract: AI pluralism is often framed as a problem of representing diverse values, preferences, users, or outputs. This paper argues that this framing is incomplete because AI systems also impose ontologies: they define what counts as an entity, relation, feature, harm, benefit, and valid form of evidence. We define ontological flattening as the conversion of situated, contested, and historically specific meanings into a restricted technical category, proxy, aggregation rule, or benchmark target that is treated as neutral and difficult to contest. The pap
The increasing deployment and integration of AI systems across various sectors necessitates a deeper understanding of their inherent biases and the frameworks they subtly impose.
This paper highlights how AI's ontological flattening can mask hidden biases and assumptions, making it critical for policymakers and developers to consider the foundational impact of AI beyond mere output diversity.
The framing of AI pluralism shifts from purely representational diversity to a more profound consideration of the 'worlds' AI systems construct and potentially limit, challenging current development paradigms.
- · Ethical AI researchers
- · Independent AI audit firms
- · Sociologists of technology
- · Policy makers
- · AI developers prioritizing efficiency over ethical scope
- · Organizations deploying unchecked AI systems
- · Users unknowingly subjected to flattened ontologies
Increased scrutiny and demand for transparency in AI's underlying ontological frameworks and data models.
Development of new metrics and auditing tools focused on identifying and mitigating ontological flattening in AI systems.
Potential for regulatory frameworks to mandate ontological impact assessments for high-stakes AI deployments, moving beyond current bias detection.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI