Category: U

Unawareness of sensitive characteristics is a common issue when constructing models, as these attributes may be omitted from the training data. However, due to correlations between protected and non-protected features, a model trained with unawareness of a protected characteristic can still lead to disparate impact or violate fairness requirements. It is thus imperative to address this oversight to create an equitable system. Case Scenarios Where Unawareness (to a sensitive attribute) Can Be Used? Unawareness (to a sensitive attribute) can Help reduce bias in recruitment and hiring by excluding sensitive attributes from the evaluation process. This ensures that candidates are considered solely based on their qualifications and skills. Be used to decrease bias within credit scoring by omitting sensitive characteristics from the assessment process. This guarantees that credit decisions are only based on creditworthiness and risk factors. Facilitate reducing prejudice in healthcare by excluding sensitive details from patient data analysis. This ensures that patients obtain appropriate care and treatment regardless of race, gender, or other sensitive traits. Contribute to diminishing bias in criminal justice by eliminating sensitive data from the assessment process. This paves the way for defendants to be evaluated fairly and only on the facts of their case, not influenced by factors such as race or gender. Lessen prejudice in advertising by leaving out sensitive information from the targeting system. This allows ads to be targeted according to consumer interests and behavior, uninfluenced by race or gender.

Machine learning utilizes unsupervised learning as a method for data processing. This form of learning enables systems to identify and analyze unknown data without outside intervention. Unsupervised learning can recognize patterns that may be missed by manual inspection and can process large volumes of data that would otherwise be beyond human capacity. Applications of Unsupervised Learning Here are five common applications of unsupervised learning: Clustering involves grouping similar objects based on shared characteristics, typically used for market segmentation, image segmentation, and customer segmentation. Anomaly detection utilizes to identify unusual patterns or anomalies in data, frequently deployed in fraud detection, cybersecurity, and fault detection in industrial systems. Dimensionality Reduction is applied to reduce the number of features or variables in a dataset while preserving as much relevant information as possible. This is used for simplifying data visualization, minimizing storage requirements, and boosting model effectiveness. Generative models generate new data similar to the existing input data, often seen in image generation, music production, and language generation. Association rule Learning enables the identification of patterns in data through frequent item sets or rules which link together multiple factors, primarily employed in market basket analysis, recommender systems, and cross-selling.