Respondents proposed testing and monitoring (61.9%) and human involvement (22.7%) as leading mitigations for algorithmic bias¶

Claim · All claims
Evidence · 1 study · qualitative r2 · q2
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Among respondents naming algorithmic bias as a risk, 61.9% described testing and monitoring strategies and 22.7% described human involvement such as human-in-the-loop workflows and co-design. → bias-mitigation

Evidence¶

bias-mitigation¶

Noakes, S., Shell, A., Van Nostrand, P., Murillo, A., Ruiz, P., and Liberman, B. (2024, September). AI-Powered Innovations in Mathematics Teaching & Learning: Initial Findings. Digital Promise. https://doi.org/10.51388/20.500.12265/229

q2 · i? · qualitative · r2

Qualitative thematic coding of mitigation strategies among respondents who flagged algorithmic bias. Strategies described include ongoing audits, diverse training data, human-in-the-loop testing, co-design, closed models, precise prompts, and knowledge graph approaches.

"A variety of strategies were described to mitigate the inevitability of bias, including testing and monitoring techniques (61.9%), human involvement (22.7%), technical strategies (8.2%), and supporting users through guidance and professional learning (7.2%)."

Discussion¶

Related Claims¶

• Respondents identified algorithmic bias (31.9%), data privacy (27.5%), and technical reliability (27.5%) as the primary risks of AI in education — related
• Data privacy mitigations centered on data governance and monitoring (68.9%), while technical reliability mitigations centered on human involvement (36.9%) — related
• A hybrid human-AI workflow using GPT-4o with retrieval-augmented generation supported efficient inductive thematic analysis while preserving researcher judgment — related
• Generative AI systems carry strong potential for introduced and inherited bias — related
• Ethical and governance dimensions of AI were less prominent in teachers' narratives than pedagogical opportunities — related
• Simulated Human-in-the-Loop workflow reduces required full human scoring of written productions by at least 50% — related
• Review reports ethical concerns of educational AI including algorithmic bias and exploitation of personal data — related