AnalyticScore applies new FGTI interpretability principles to text-based scoring and achieves accuracy within 0.06 QWK of uninterpretable state-of-the-art while matching human featurization on the ASAP-SAS dataset.
Fairness, accountability, transparency, and ethics (fate) in artificial intelligence (ai) and higher education: A systematic review
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Interpretability from the Ground Up: Stakeholder-Centric Design of Automated Scoring in Educational Assessments
AnalyticScore applies new FGTI interpretability principles to text-based scoring and achieves accuracy within 0.06 QWK of uninterpretable state-of-the-art while matching human featurization on the ASAP-SAS dataset.