# Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective
## 5 Datasets with Gender Bias  
The UCI Machine Learning Repository (Available online: [[https://archive.ics.uci.edu/ml/index.php](https://archive.ics.uci.edu/ml/index.php)]([https://archive.ics.uci.edu/ml/index.php](https://archive.ics.uci.edu/ml/index.php)), accessed on 19 July 2022) is a well-known collection of databases, domain theories, and data generators. The machine learning community use them for educational purposes and the empirical analysis of algorithms. We focus on the dataset called 'Adult dataset', also known as 'Census Income dataset', which is based on census data from 1994. The prediction task posed is to determine whether a person makes over 50K a year. The dataset contains 48,842 observations split into a training set and a test set with 32,561 and 16,281 observations, respectively. There are 51 papers that cite this dataset.  
In the training set, there is information regarding 10,771 females and 21,790 males (33.08% vs. 66.92%). Thus, we find imbalance in the class classification, which may results in models that have poor predictive performance, specifically for the minority class. Only 10.95% of females belong to the class '\(>\)50K', while this percentage reaches 30.57% for males. Based on this historical dataset, it can be concluded that a higher percentage of males have a higher income.  
A classification tree is built using the training set and predictions are made for the test set [42; 43]. The independent variables employed are: age (continuous), sex (categorical), workclass (categorical), occupation (categorical), education (continuous), hours per week (continuous), and native country (categorical). The classification tree is a popular and easy-to-interpret method. The accuracy is 80.84%, considering all the individuals; 76.70% for males and 89.12% for females. The sensitivity (defined as the percentage of individuals in the category '<50K' for which the prediction is correct) is 91.79% for males and 100% for females. The specificity (defined as the percentage of individuals in the category '>50K' for which the prediction is correct) is 41.46% for males and 0% for females. Thus, while the accuracy reached by the classification method is relatively high, the values of specificity are low, extremely low for females. The method always predicts lower income, '<=50K', for females. The use of this algorithm for decision-making, for example, linked to granting mortgages or allowing rent could have serious negative social impacts.  
We may find examples of dataset with gender bias in other AI fields. A case in point is COCO, a large-scale object detection, segmentation, and captioning dataset. The process to build the dataset is detailed in [44], which is a work with more than 26K citations in Google Scholar. Recent authors have pointed out that the occurrence of men in image is significantly higher than women and the gender disparity reaches high values for specific contexts [45]. For instance, 90% of surfboard images only contain male players. The dataset imSitu [46] constitutes another example. imSitu supports situation recognition, the problem of producing a concise summary of the situation an image depicts. This resource exhibits gender bias. For instance, women are represented as cooking twice as often as men [47].