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
Evaluating the fairness of task-adaptive pretraining on unlabeled test data before few-shot text classification
Kush Dubey
Positive-Sum Fairness: Leveraging Demographic Attributes to Achieve Fair AI Outcomes Without Sacrificing Group Gains
Samia Belhadj, Sanguk Park, Ambika Seth, Hesham Dar, Thijs Kooi
Temporal Fairness in Decision Making Problems
Manuel R. Torres, Parisa Zehtabi, Michael Cashmore, Daniele Magazzeni, Manuela Veloso
Exploring Bias and Prediction Metrics to Characterise the Fairness of Machine Learning for Equity-Centered Public Health Decision-Making: A Narrative Review
Shaina Raza, Arash Shaban-Nejad, Elham Dolatabadi, Hiroshi Mamiya