When Machine Learning technologies are used in contexts that affect citizens, companies as well as researchers need to be confident that there will not be any unexpected social implications, such as bias towards gender, ethnicity, and/or people with disabilities. There is significant literature on approaches to mitigate bias and promote fairness, yet the area is complex and hard to penetrate for newcomers to the domain. This article seeks to provide an overview of the different schools of thought and approaches that aim to increase the fairness of Machine Learning. It organizes approaches into the widely accepted framework of pre-processing, in-processing, and post-processing methods, subcategorizing into a further 11 method areas. Although much of the literature emphasizes binary classification, a discussion of fairness in regression, recommender systems, and unsupervised learning is also provided along with a selection of currently available open source libraries. The article concludes by summarizing open challenges articulated as five dilemmas for fairness research.

Research on Fairness and Bias Mitigation in Machine Learning often uses a set of reference datasets for the design and evaluation of novel approaches or definitions. While these datasets are well structured and useful for the comparison of various approaches, they do not reflect that datasets commonly used in real-world applications can have missing values. When such missing values are encountered, the use of imputation strategies is commonplace. However, as imputation strategies potentially alter the distribution of data they can also affect the performance, and potentially the fairness, of the resulting predictions, a topic not yet well understood in the fairness literature. In this article, we investigate the impact of different imputation strategies on classical performance and fairness in classification settings. We find that the selected imputation strategy, along with other factors including the type of classification algorithm, can significantly affect performance and fairness outcomes. The results of our experiments indicate that the choice of imputation strategy is an important factor when considering fairness in Machine Learning. We also provide some insights and guidance for researchers to help navigate imputation approaches for fairness.

Analyzing the induction kinetics and promoter elements regulating the expression of the transcription factor Egr-1, we found elevated levels of Egr-1-encoding mRNA in synovial fibroblasts of rheumatoid arthritis (RA) patients when compared to controls. By contrast, synovial lymphocytes and macrophages do not show an elevated Egr-1 transcription. Therefore, the overexpression of Egr-1 may serve as a diagnostic marker to characterize synovial fibroblasts of RA patients. To study the regulatory mechanisms controlling Egr-1 expression we analyzed the function of transcription factor binding sites located in the Egr-1 promoter. Individual transcription factor binding sites within the Egr-1 promoter were specifically mutated and Egr-1 promoter activity was tested using reporter gene constructs. Our experiments demonstrate that serum response elements are the main positive regulators and binding to a cAMP responsive element represents the major negative regulator for Egr-1 expression in synovial fibroblasts. In addition, we functionally defined a new element, which was not yet described in the human Egr-1 promoter and which serves as a second negative regulatory element for Egr-1 expression. Therefore increased serum response factor activity or failure of Egr-1 repressing signals may account for Egr-1 overexpression in RA synovial fibroblasts.

Two-Sided Matching is a well-established approach to find allocations and matchings based on the participants' preferences. While its most prominent applications are College Admissions and School Choice problems, this paper applies the concept to the matching of mentors to mentees in a higher education context. Both mentors and mentees have preferences with whom they ideally want to be matched, as well as who they want to avoid. As the general formulation for these types of preferences is NP-hard, several existing approximation algorithms and heuristics are compared with respect to their ability to find a matching with desirable properties. The results show that a combination of evolutionary heuristics and local search approaches works best in finding high-quality solutions, allowing us to find mentor-mentee pairs which are close to the respective ideal match.