Towards more sustainable and trustworthy reporting in machine learning

• Published in: Data mining and knowledge discovery Vol. 38; no. 4; pp. 1909 - 1928
• Main Authors: Fischer, Raphael, Liebig, Thomas, Morik, Katharina
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Chemistry and Earth Sciences; Computer Science; Data Mining and Knowledge Discovery; Information Storage and Retrieval; Machine learning; Physics; Statistics for Engineering; Sustainability; Trustworthiness; Resource-awareness; Benchmarking; Trustworthiness; Reporting; Sustainability

With machine learning (ML) becoming a popular tool across all domains, practitioners are in dire need of comprehensive reporting on the state-of-the-art. Benchmarks and open databases provide helpful insights for many tasks, however suffer from several phenomena: Firstly, they overly focus on predic...