Book Cover: Mitigating Bias in Machine Learning Edited by Carlotta A. Berry, Brandeis Hill Marshall https://www.mhprofessional.com/mitigating-bias-in-machine-learning-9781264922444-usa This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries. Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced. Mitigating Bias in Machine Learning addresses: Ethical and Societal Implications of Machine Learning Social Media and Health Information Dissemination Comparative Case Study of Fairness Toolkits Bias Mitigation in Hate Speech Detection Unintended Systematic Biases in Natural Language Processing Combating Bias in Large Language Models Recognizing Bias in Medical Machine Learning and AI Models Machine Learning Bias in Healthcare Achieving Systemic Equity in Socioecological Systems Community Engagement for Machine Learning

Figure 9.9 ML life cycle with bias indicators and mitigation techniques. (inspired by Herhausen & Fahse, 2022; Huang et al., 2022; and van Giffen et al., 2022) • Preprocessing bias mitigation techniques attempt to remove discrimination by adding more data or modifying the available training data. • In-processing bias mitigation techniques affect the algorithm itself and the learning procedure by imposing constraints, updating the objective function, or regularization. • Postprocessing bias mitigation techniques may be implemented following model deployment or during the re-evaluation period in which adjustments are made to the model decision thresholds or the model output, including relabeling. From book: Mitigating Bias in Machine Learning Edited by Carlotta A. Berry, Brandeis Hill Marshall https://www.mhprofessional.com/mitigating-bias-in-machine-learning-9781264922444-usa