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Found 6 papers, 2 papers with code
Navigating Fairness: Practitioners' Understanding, Challenges, and Strategies in AI/ML Development
We conducted semi-structured interviews with 22 AI practitioners to investigate their understanding of what a 'fair AI/ML' is, the challenges they face in developing a fair AI/ML, the consequences of developing an unfair AI/ML, and the strategies they employ to ensure AI/ML fairness.
Beyond Accuracy: An Empirical Study on Unit Testing in Open-source Deep Learning Projects
Deep Learning (DL) models have rapidly advanced, focusing on achieving high performance through testing model accuracy and robustness.
Ethics in the Age of AI: An Analysis of AI Practitioners' Awareness and Challenges
But what do people who build AI - AI practitioners - have to say about their understanding of AI ethics and the challenges associated with incorporating it in the AI-based systems they develop?
A Comparison of Similarity Based Instance Selection Methods for Cross Project Defect Prediction
However, the performance of LSH is based on high recall and in environments where precision is considered more important NN-Filter should be considered.
A Fine-grained Data Set and Analysis of Tangling in Bug Fixing Commits
Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits.
Software Engineering
Empirical Standards for Software Engineering Research
Empirical Standards are natural-language models of a scientific community's expectations for a specific kind of study (e. g. a questionnaire survey).
Software Engineering General Literature
