Addressing bias in machine learning algorithms: A pilot study on emotion recognition for intelligent systems

By Ayanna M. Howard, Cha Zhang, Eric Horvitz. 2017

88 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Christian Janiesch
2. Sheng Zhang
3. Harsha Nori
4. Patrick Zschech
5. Yin Tat Lee
6. Kai Heinrich
7. Renqian Luo
8. Naoto Usuyama

Citations in the corpus

• Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018, background): “The past several years have seen multiple controversies over AI/ML enabled systems yielding biased or discriminatory results [37], [38].”
• Machine learning and deep learning (2021, methodology): “These biases will be heavily adopted by the model (Fuchs 2018; Howard et al. 2017).”
• Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine (2023, background): “The challenge of bias and fairness remains relevant and pressing in the context of model optimization, fine-tuning, and prompt engineering [13, 20, 38].”