Whose Knowledge Counts? Co-Designing Community-Centered AI Auditing Tools with Educators in Hawai`i

TL;DR

This paper explores co-designed AI auditing tools with Hawaiian educators to address cultural bias and misrepresentation in AI systems used in indigenous and low-resource language education settings.

Contribution

It introduces a community-centered approach to AI auditing, integrating cultural values and practices specific to Hawaiian education contexts.

Findings

Educators emphasize cultural values in designing auditing tools.

Community-oriented AI auditing is more effective for indigenous contexts.

Designs include tracing knowledge genealogy in source materials.

Abstract

Although generative AI is being deployed into classrooms with promises of aiding teachers, educators caution that these tools can have unintended pedagogical repercussions, including cultural misrepresentation and bias. These concerns are heightened in low-resource language and Indigenous education settings, where AI systems frequently underperform. We investigate these challenges in Hawai`i, where public schools operate under a statewide mandate to integrate Hawaiian language and culture into education. Through four co-design workshops with 22 public school educators, we surfaced concerns about using generative AI in educational settings, particularly around cultural misrepresentation, and corresponding designs for auditing tools that address these issues. We find that educators envision tools grounded in specific Hawaiian cultural values and practices, such as tracing the genealogy of knowledge in source materials. Building on these insights, we conceptualize AI auditing as a community-oriented process rather than the work of isolated individuals, and discuss implications for designing auditing tools.