What is the role of causal inference in understanding and mitigating bias in machine learning?

Mar 5, 2019 ... Reports of successful applications of machine learning (ML) methods in structure-based virtual screening (SBVS) are increasing. Jul 25, 2022 ... Machine learning bias is sometimes called algorithm bias or AI bias. Predictive models may be trained on data containing human decisions or ... Jul 28, 2022 ... Long Story Short: Omitted Variable Bias in Causal Machine Learning ... We derive general, yet simple, sharp bounds on the size of the omitted ... Aug 12, 2020 ... What are Bias and Variance? · Bias: it's the difference between average predictions and true values. · Variance: it's the variability of our ... 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 ... Feb 7, 2024 ... The landscape is overrun with “neutral” analysts using some kind of sentiment analysis to lazily label something as right or left. This MIT ... Aug 8, 2023 ... Encord Active offers features to help reduce bias in datasets, allowing you to identify and address any potential biases in your data workflow. 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 ... Mar 9, 2023 ... We know that synthetic data remains one of the most reliable and cost-effective solutions out there that improves the performance of the overall solution. Oct 25, 2019 ... Machine learning systems disregard variables that do not accurately predict outcomes (in the data available to them). This is in contrast to ...