Research Topic · Peer-Reviewed

Discrimination

in machine learning Discrimination in machine learning is the process of distinguishing between different categories of data. It is used to help create more accurate models of data and to identify potential biases or outliers that may exist in datasets. It also helps to determine a machine learning model’s ability …

Curated from this journal's research 📚 12 peer-reviewed articles cited Cited 93× across the literature 🔖 ISSN 2643-6655 🗓 Reviewed June 2026

Overview

in machine learning Discrimination in machine learning is the process of distinguishing between different categories of data. It is used to help create more accurate models of data and to identify potential biases or outliers that may exist in datasets. It also helps to determine a machine learning model’s ability to accurately classify data. Discrimination in machine learning enables better decision making by providing insight into the data, its structure, and the correlations between different attributes. This helps to create more accurate predictions and models in order to make better decisions. Overall, discrimination in machine learning provides the tools to recognize differences in data that may lead to better resource allocation and more effective decision making.

Research published in this journal

12 peer-reviewed articles, ranked by relevance. Each links to its DOI.

How this research is being cited

The 12 articles above have been cited 93 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.

A sample of recent works citing this journal's research on Discrimination, linking to each citing work.

Editorial oversight

Curated from peer-reviewed research published in Child and Adolescent Psychiatry (ISSN 2643-6655).

Journal editorial board
Laura Orsolini · United Kingdom

This page summarises published research for orientation; it is not medical or professional advice.