Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions...

machine learning may take longer to be adopted in other domains. Concern for fairness in machine learning, that is, reducing bias in machine learning...

Quantum machine learning is the integration of quantum algorithms within machine learning programs. The most common use of the term refers to machine learning...

on the remedy of fairness, but definitions of fairness are often incompatible with each other and the realities of machine learning optimization. For...

accuracy equality and disparate mistreatment, is a measure of fairness in machine learning. A classifier satisfies this definition if the subjects in the...

Mitchell is a computer scientist who works on algorithmic bias and fairness in machine learning. She is most well known for her work on automatically removing...

Understanding Fairness Wikiquote has quotations related to Fairness. Fairness or being fair can refer to: Justice: in particular, impartiality, objectivity...

this research area is that fairness through blindness doesn't work." Criticism of COMPAS highlighted that machine learning models are designed to make...

distinct from the Common Law Fairness (machine learning) – Measurement of algorithmic bias Justice – Concept of moral fairness and administration of the...

AI (XAI), often overlapping with interpretable AI, or explainable machine learning (XML), is a field of research within artificial intelligence (AI) that...

research develops privacy-preserving and fair algorithms, studies individual and societal impacts of machine learning and AI, and performs AI audits for algorithmic...

teaching machines can be found in the 1960 sourcebook, Teaching Machines and Programmed Learning. An "Autotutor" was demonstrated at the 1964 World's Fair. Edward...

Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...

work makes use of machine learning models to study the dynamics of social processes. Her current research focuses on issues of fairness, accountability...

barriers and promote research on interpretability, fairness, privacy, and robustness of machine learning models. She has also developed several tutorials...

Fairness, Accountability, and Transparency. In 2019, Raji was a summer research fellow at The Partnership on AI working on setting industry machine learning...

databases. Fairness: add-on for evaluation and creation of fair machine learning models without discrimination. Widgets range from computing fairness metrics...

scientist working primarily in the fields of machine learning, deep learning, representational learning, and generative models. He is a professor of computer...

Explanation-based learning List of datasets for machine learning research Predictive analytics Seq2seq Fairness (machine learning) Embedding, for other...

A large language model (LLM) is a machine learning model designed for natural language processing tasks, especially language generation. LLMs are language...

PyTorch is a machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, originally...

conference focuses on issues such as algorithmic transparency, fairness in machine learning, bias, and ethics from a multi-disciplinary perspective. The...

Digital rights Algorithmic bias Ethics of artificial intelligence Fairness (machine learning) Deborah Raji Emily M. Bender Joy Buolamwini Sasha Costanza-Chock...

using a GUI. AI fairness and explainability has been an area of debate for AI Ethicists in recent years. A major issue for Machine Learning applications...

2000s machine learning has increasingly been developed and deployed. Key issues with the use of ADM in social services include bias, fairness, accountability...

patterns in massive data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary...

regression, for example) to determine the most suitable machine learning algorithm, including deep learning paradigms. Once an algorithm is chosen, optimizing...

reinforcement learning (DRL) is a subfield of machine learning that combines principles of reinforcement learning (RL) and deep learning. It involves training...

fastText is a library for learning of word embeddings and text classification created by Facebook's AI Research (FAIR) lab. The model allows one to create...

competition platform and online community for data scientists and machine learning practitioners under Google LLC. Kaggle enables users to find and publish...