29 citations · 44 across the 5 of their papers we have counts for
10 papers
Hidden Author Bias in Book Recommendation
Savvina Daniil, Mirjam Cuper, Cynthia C. S. Liem +2
Collaborative filtering algorithms have the advantage of not requiring sensitive user or item information to provide recommendations. However, they still suffer from fairness relat…
Social Inclusion in Curated Contexts: Insights from Museum Practices
Han-Yin Huang, Cynthia C. S. Liem
Artificial intelligence literature suggests that minority and fragile communities in society can be negatively impacted by machine learning algorithms due to inherent biases in the…
What Are We Really Testing in Mutation Testing for Machine Learning? A Critical Reflection
Annibale Panichella, Cynthia C. S. Liem
Mutation testing is a well-established technique for assessing a test suite's quality by injecting artificial faults into production code. In recent years, mutation testing has bee…
Run, Forest, Run? On Randomization and Reproducibility in Predictive Software Engineering
Cynthia C. S. Liem, Annibale Panichella
Machine learning (ML) has been widely used in the literature to automate software engineering tasks. However, ML outcomes may be sensitive to randomization in data sampling mechani…
ReproducedPapers.org: Openly teaching and structuring machine learning reproducibility
Burak Yildiz, Hayley Hung, Jesse H. Krijthe +9
We present ReproducedPapers.org: an open online repository for teaching and structuring machine learning reproducibility. We evaluate doing a reproduction project among students an…
Are Nearby Neighbors Relatives?: Testing Deep Music Embeddings
Jaehun Kim, Julián Urbano, Cynthia C. S. Liem +1
Deep neural networks have frequently been used to directly learn representations useful for a given task from raw input data. In terms of overall performance metrics, machine learn…