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The Disagreement Problem in Faithfulness Metrics
The field of explainable artificial intelligence (XAI) aims to explain how black-box machine learning models work.
Making Intelligence: Ethical Values in IQ and ML Benchmarks
In recent years, ML researchers have wrestled with defining and improving machine learning (ML) benchmarks and datasets.
Should attention be all we need? The epistemic and ethical implications of unification in machine learning
"Attention is all you need" has become a fundamental precept in machine learning research.
Epistemic values in feature importance methods: Lessons from feminist epistemology
As the public seeks greater accountability and transparency from machine learning algorithms, the research literature on methods to explain algorithms and their outputs has rapidly expanded.
Feature Importance
Computers and Society
