Procedural Fairness
Procedural fairness in artificial intelligence focuses on ensuring that AI systems make decisions equitably across different demographic groups, mitigating biases that can lead to discriminatory outcomes. Current research emphasizes developing and evaluating fairness-aware algorithms and models, including those based on adversarial learning, data augmentation techniques like mixup, and distributionally robust optimization, across various applications like healthcare, process analytics, and recommender systems. This research is crucial for building trustworthy AI systems and addressing societal concerns about algorithmic bias, impacting both the development of ethical AI guidelines and the practical deployment of AI in sensitive domains.
Papers
FairLENS: Assessing Fairness in Law Enforcement Speech Recognition
Yicheng Wang, Mark Cusick, Mohamed Laila, Kate Puech, Zhengping Ji, Xia Hu, Michael Wilson, Noah Spitzer-Williams, Bryan Wheeler, Yasser Ibrahim
Trusting Fair Data: Leveraging Quality in Fairness-Driven Data Removal Techniques
Manh Khoi Duong, Stefan Conrad
A survey on fairness of large language models in e-commerce: progress, application, and challenge
Qingyang Ren, Zilin Jiang, Jinghan Cao, Sijia Li, Chiqu Li, Yiyang Liu, Shuning Huo, Tiange He, Yuan Chen
The Unfairness of $\varepsilon$-Fairness
Tolulope Fadina, Thorsten Schmidt
Small but Fair! Fairness for Multimodal Human-Human and Robot-Human Mental Wellbeing Coaching
Jiaee Cheong, Micol Spitale, Hatice Gunes