How can we bridge the gap between research on fairness and the development of real-world AI applications?

Feb 21, 2022 ... A model's ability to generalize is influenced by both the diversity of the data and the way the model is trained, researchers report. Apr 3, 2025 ... Bias Variance Tradeoff. If the algorithm is too simple (hypothesis with linear equation) then it may be on high bias and low variance condition ... Tackling issues of bias and fairness when building and deploying machine learning and data science systems has received increased attention from the research ... Jun 1, 2023 ... In this review article, we discuss the implicit bias in training neural networks using gradient-based methods. May 1, 2022 ... It is the difference between the average estimator value (averaged over random samples) and the true value of the parameter. Mar 3, 2021 ... Abstract. Western societies are marked by diverse and extensive biases and inequality that are unavoidably embedded in the data used to train ... Detecting bias in your ML model can be done proactively or reactively, and depending on your specific case, either one is viable. Reactive bias detection ... Feb 15, 2023 ... Conclusions. Our study illustrates the racial bias that can result when creating ML models without proper bias mitigation. Healthcare-related ML ... Nov 10, 2017 ... Machine learning can perpetuate and even amplify behavioral biases. By design, a social-media site filtering news based on user preferences ... Feb 20, 2024 ... In this review, we present a framework for understanding the balance of opportunities and challenges for minimizing bias in medical imaging.