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Rethinking Streaming Machine Learning Evaluation
While most work on evaluating machine learning (ML) models focuses on computing accuracy on batches of data, tracking accuracy alone in a streaming setting (i. e., unbounded, timestamp-ordered datasets) fails to appropriately identify when models are performing unexpectedly.
An Algorithmic Equity Toolkit for Technology Audits by Community Advocates and Activists
A wave of recent scholarship documenting the discriminatory harms of algorithmic systems has spurred widespread interest in algorithmic accountability and regulation.
The Promise and Peril of Human Evaluation for Model Interpretability
Finally, we propose two potential research directions to disambiguate cognitive function and explanation models, retaining control over the tradeoff between accuracy and interpretability.
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