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Beyond "Fairness:" Structural (In)justice Lenses on AI for Education
Educational technologies, and the systems of schooling in which they are deployed, enact particular ideologies about what is important to know and how learners should learn.
Should You Fine-Tune BERT for Automated Essay Scoring?
Most natural language processing research now recommends large Transformer-based models with fine-tuning for supervised classification tasks; older strategies like bag-of-words features and linear models have fallen out of favor.
Why Attention is Not Explanation: Surgical Intervention and Causal Reasoning about Neural Models
Here we study the state-of-the-art in explanation for neural models for NLP tasks from the viewpoint of philosophy of science.
Machine-Aided Annotation for Fine-Grained Proposition Types in Argumentation
We introduce a corpus of the 2016 U. S. presidential debates and commentary, containing 4, 648 argumentative propositions annotated with fine-grained proposition types.
Equity Beyond Bias in Language Technologies for Education
There is a long record of research on equity in schools.
Principled Frameworks for Evaluating Ethics in NLP Systems
We critique recent work on ethics in natural language processing.
Stance Classification, Outcome Prediction, and Impact Assessment: NLP Tasks for Studying Group Decision-Making
In group decision-making, the nuanced process of conflict and resolution that leads to consensus formation is closely tied to the quality of decisions made.
