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Measurement of algorithmic bias
Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions
Fairness_(machine_learning)
Subset of artificial intelligence
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn
Machine_learning
Topics referred to by the same term
algorithms Fairness (machine learning), a desirable property of machine learning algorithms Fair division in game theory Fair value in economics Fairness of human
Fairness
Interdisciplinary research area
Quantum machine learning (QML) is the study of quantum algorithms for machine learning. It often refers to quantum algorithms for machine learning tasks
Quantum_machine_learning
AI whose outputs can be understood by humans
(XAI), generally overlapping with interpretable AI or explainable machine learning (XML), is a field of research that explores methods that provide humans
Explainable artificial intelligence
Explainable_artificial_intelligence
Technological phenomenon with social implications
on the remedy of fairness, but definitions of fairness are often incompatible with each other and the realities of machine learning optimization. For
Algorithmic_bias
American computer scientist
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
Margaret_Mitchell_(scientist)
Intelligence of machines
this research area is that fairness through blindness doesn't work." Criticism of COMPAS highlighted that machine learning models are designed to make
Artificial_intelligence
Form of modelling that uses statistics to predict outcomes
that predictions are not biased in a discriminatory manner (see Fairness (machine learning)). Even if the model does not directly use sensitive information
Predictive_modelling
Latvian-American computer scientist
research develops privacy-preserving and fair algorithms, studies individual and societal impacts of machine learning and AI, and performs AI audits for algorithmic
Aleksandra_Korolova
Principle of justice holding that decisions should be based on objective criteria
distinct from the Common Law Fairness (machine learning) – Measurement of algorithmic bias Justice – Concept of moral fairness and administration of the
Impartiality
Field of machine learning
Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. While supervised learning and
Reinforcement_learning
Nigerian-Canadian computer scientist
Fairness, Accountability, and Transparency. In 2019, Raji was a summer research fellow at The Partnership on AI working on setting industry machine learning
Deborah_Raji
Decentralized machine learning
Federated learning (also known as collaborative learning) is a machine learning technique in a setting where multiple entities (often called clients)
Federated_learning
AI standards for robustness and data privacy
transparency, and explainability. Artificial intelligence Data science Fairness (machine learning) Privacy-enhancing technologies Ethics Guidelines for Trustworthy
Trustworthy_AI
Indian-American computer scientist
barriers and promote research on interpretability, fairness, privacy, and robustness of machine learning models. She has also developed several tutorials
Himabindu_Lakkaraju
Open-source data analysis software
databases. Fairness: add-on for evaluation and creation of fair machine learning models without discrimination. Widgets range from computing fairness metrics
Orange_(software)
Measure of fairness in machine learning models
accuracy equality and disparate mistreatment, is a measure of fairness in machine learning. A classifier satisfies this definition if the subjects in the
Equalized_odds
Educational mechanical device
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
Teaching_machine
Type of large language model
the problem before generating an output. During the 2010s, improved machine learning algorithms, more powerful computers, and an increase in the amount
Generative pre-trained transformer
Generative_pre-trained_transformer
Decision-making process conducted with varying degrees of human oversight
2000s machine learning has increasingly been developed and deployed. Key issues with the use of ADM in social services include bias, fairness, accountability
Automated_decision-making
American computer scientist
Morgenstern is an American computer scientist specializing in fairness in machine learning and algorithmic game theory. She is an associate professor in
Jamie_Morgenstern
Computational social scientist
work makes use of machine learning models to study the dynamics of social processes. Her current research focuses on issues of fairness, accountability
Hanna_Wallach
the country's first attempts at studying artificial intelligence and machine learning. OCR technology has benefited greatly from the work of ISI's Computer
Artificial intelligence in India
Artificial_intelligence_in_India
Problem in network theory
Explanation-based learning List of datasets for machine learning research Predictive analytics Seq2seq Fairness (machine learning) Embedding, for other
Link_prediction
Academic conference series
conference focuses on issues such as algorithmic transparency, fairness in machine learning, bias, and ethics from a multi-disciplinary perspective. The
ACM Conference on Fairness, Accountability, and Transparency
ACM_Conference_on_Fairness,_Accountability,_and_Transparency
American computer scientist
scientist working primarily in the fields of machine learning, deep learning, representational learning, and generative models. He is a professor of computer
Rob_Fergus
Process of analyzing large data sets
patterns in massive data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary
Data_mining
Type of machine learning model
and performance via collaborative platforms such as Hugging Face. As machine learning algorithms process numbers rather than text, the text must be converted
Large_language_model
Digital advocacy non-profit organization
Digital rights Algorithmic bias Ethics of artificial intelligence Fairness (machine learning) Deborah Raji Emily M. Bender Joy Buolamwini Sasha Costanza-Chock
Algorithmic_Justice_League
Public availability of the learned parameters of an artificial intelligence model
reproduce the model. A model card is a document accompanying a trained machine learning model that describes a model’s intended uses, limitations, training
Open_weights
Canadian machine learning researcher (born 1992/3)
Christopher Olah (born 1992 or 1993) is a Canadian machine learning researcher and a co-founder of Anthropic. He is known for his work on neural network
Chris_Olah
Open-source machine learning platform
open-source platform for machine learning and MLOps on Kubernetes introduced by Google. The different stages in a typical machine learning lifecycle are represented
Kubeflow
Engineering applied to artificial intelligence
laboratories and made available as a service. Huyen distinguishes this from machine learning (ML) engineering, which involves building and deploying models developed
Artificial intelligence engineering
Artificial_intelligence_engineering
Interatomic potentials constructed by machine learning programs
Machine-learned interatomic potentials (MLIPs), or simply machine learning potentials (MLPs), are interatomic potentials constructed using machine learning
Machine-learned interatomic potential
Machine-learned_interatomic_potential
Machine learning practice of supervised learning
In machine learning, quantification (variously called learning to quantify, or supervised prevalence estimation, or class prior estimation) is the task
Quantification (machine learning)
Quantification_(machine_learning)
Ethiopian-born cognitive scientist
scientist who works at the intersection of complex adaptive systems, machine learning, algorithmic bias, and critical race studies. Birhane's work with Vinay
Abeba_Birhane
Internet platform for data science competitions
competition platform and online community for data scientists and machine learning practitioners under Google LLC. Kaggle enables users to find and publish
Kaggle
Deep reinforcement learning method
stage of chip floorplanning. It is based on reinforcement learning (RL), a machine learning method in which a system iteratively improves its decisions
AlphaChip
Machine learning library
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
ML.NET
Professor of computer science
their applications in fields ranging from machine learning, data science, health, and algorithmic fairness to networks, cloud computing, and sustainable
Aravind_Srinivasan
Ukrainian computer scientist (born 1990)
During this time, she also contributed to integrating fairness and accountability into machine learning education at Mila. Luccioni briefly worked with the
Sasha_Luccioni
Right to have an algorithm explained
algorithms, particularly artificial intelligence and its subfield of machine learning, a right to [an] explanation is a right to be given an explanation
Right_to_explanation
AI that generates content
on Fairness, Accountability, and Transparency. pp. 610–623. doi:10.1145/3442188.3445922. ISBN 978-1-4503-8309-7. Ferrara, Emilio (2023). "Fairness and
Generative_AI
Machine learning-powered structure design
artificial neural networks (ANN), a widely used model in the field of machine learning. NAS has been used to design networks that are on par with or outperform
Neural_architecture_search
and funding continued to grow under other names. In the early 2000s, machine learning was applied to a wide range of problems in academia and industry. The
History of artificial intelligence
History_of_artificial_intelligence
AI research laboratory
multi-agent reinforcement learning". DeepMind Blog. 31 October 2019. Retrieved 31 October 2019. Gao, Jim (2014). "Machine Learning Applications for Data Center
Google_DeepMind
Deep learning artificial intelligence research team
to artificial intelligence. Formed in 2011, it combined open-ended machine learning research with information systems and large-scale computing resources
Google_Brain
educator, and entrepreneur. His research is focused on the application of machine learning, knowledge representation, and artificial intelligence for the analysis
Nigam_Shah
French computer scientist (born 1960)
computer scientist working in the fields of artificial intelligence, machine learning, computer vision, robotics and image compression. He is the Jacob T
Yann_LeCun
deep learning methods to perform tomographic reconstruction of medical and industrial images. It uses artificial intelligence and machine learning, especially
Deep tomographic reconstruction
Deep_tomographic_reconstruction
Machine learning technique
Predictive learning is a machine learning (ML) technique where an artificial intelligence model is fed new data to develop an understanding of its environment
Predictive_learning
Multilingual neural machine translation service
transitioned its translating method to a system called neural machine translation. It uses deep learning techniques to translate whole sentences at a time, which
Google_Translate
Chinese-American computer scientist
algorithmic game theory, prediction markets, and algorithmic fairness in machine learning. She is Gordon McKay Professor of Computer Science in the Harvard
Yiling_Chen
Class of artificial neural networks
{x} _{v}\right)} Attention in Machine Learning is a technique that mimics cognitive attention. In the context of learning on graphs, the attention coefficient
Graph_neural_network
"Explainability & Fairness in Machine Learning for Credit Underwriting" (PDF). FinRegLab. Retrieved 2025-09-07. "ZestFinance Introduces Machine Learning Platform
Applications of artificial intelligence
Applications_of_artificial_intelligence
Artificial Intelligence researcher (born 1982)
2020. Concept learning in description logics using refinement operators, J. Lehmann and P. Hitzler, Machine Learning DL-Learner: Learning concepts in description
Jens_Lehmann_(scientist)
Machine which dispenses products to customers
the large digital touch display, internet connectivity, deep learning and machine learning technologies, cameras and various types of sensors, more cost-effective
Vending_machine
Subfield of artificial intelligence
learning from data. At the same time, it seeks to address deep learning’s main limitations: lack of reliability, data and energy efficiency, fairness
Neuro-symbolic_AI
Computer vision and machine learning researcher
a research scientist at Facebook AI Research (FAIR). She works on computer vision and machine learning. Grauman studied computer science at Boston College
Kristen_Grauman
Statistical model of language
March 2003). "A neural probabilistic language model". The Journal of Machine Learning Research. 3: 1137–1155 – via ACM Digital Library. David Guthrie; et al
Language_model
Russian mathematician
the Vapnik–Chervonenkis theory of statistical learning and the co-inventor of the support-vector machine method and support-vector clustering algorithms
Vladimir_Vapnik
from the original on 2019-07-26. Retrieved 2019-07-26. "Machine Learning Fairness | ML Fairness". Google Developers. Archived from the original on 2019-08-10
Ethics of artificial intelligence
Ethics_of_artificial_intelligence
Model selection principle
statistics, theoretical computer science and machine learning, and more narrowly computational learning theory. Historically, there are different, yet
Minimum_description_length
Degradation of AI models trained on synthetic data
Nicolas (2024-06-05). "Fairness Feedback Loops: Training on Synthetic Data Amplifies Bias". The 2024 ACM Conference on Fairness, Accountability, and Transparency
Model_collapse
Programming library
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
FastText
Ukrainian computer scientist
Princeton University. Her research investigates computer vision and machine learning. She was one of the leaders of the ImageNet Large Scale Visual Recognition
Olga_Russakovsky
AI application in work environments
process. Advances in artificial intelligence, such as the advent of machine learning and the growth of big data, enable AI to be utilized to recruit, screen
Artificial intelligence in hiring
Artificial_intelligence_in_hiring
2026 text-generating language model
increase in the amount of digitized material have fueled a revolution in machine learning. New techniques in the 2010s resulted in "rapid improvements in tasks"
GPT-3
Competence to evaluate AI technologies
cognitive systems, robotics and machine learning. This includes recognizing that large language models (LLMs) are machine learning models trained on extensive
AI_literacy
Topics referred to by the same term
armed with nuclear missiles Machine learning fairness, various attempts at correcting algorithmic bias in machine learning models This disambiguation page
MLF
Metadata format for datasets in machine learning
Croissant is a metadata format design to support sharing of datasets for machine learning applications. It is a platform-agnostic schema used to standardize
Croissant_(metadata_format)
American philosopher (born 1950)
World of Possibility, 2019 How Machine Learning Pushes Us to Define Fairness: Harvard Business Review, Nov. 2019. Our Machines Now Have Knowledge We’ll Never
David_Weinberger
List of concepts in artificial intelligence
time, and may be used for automated planning. action model learning An area of machine learning concerned with creation and modification of software agent's
Glossary of artificial intelligence
Glossary_of_artificial_intelligence
American computer scientist
Machinery Conference on Fairness, Accountability and Transparency. Jennifer is also a senior advisor to Women in Machine Learning (WiML), an initiative
Jennifer_Wortman_Vaughan
British AI entrepreneur (born 1984)
by Google. After leaving DeepMind, he co-founded Inflection AI, a machine learning and generative AI company, in 2022. Suleyman's Syrian father worked
Mustafa_Suleyman
Statistical law in machine learning
In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up
Neural_scaling_law
Computer scientist
topics have included differential privacy, location obfuscation, fairness in machine learning, the logic of concurrent systems, and the design of programming
Catuscia_Palamidessi
Artificial intelligence division of Meta Platforms
translation, and computer vision. FAIR released Torch deep-learning modules as well as PyTorch in 2017, an open-source machine learning framework, which was subsequently
Meta_AI
Cloud-based service and infrastructure
cloud services including computing, data storage, data analytics, and machine learning, alongside a set of management tools. It runs on the same infrastructure
Google_Cloud_Platform
Software designed to play StarCraft II
DeepMind became a subsidiary of Google in 2014, after demonstrating self-learning bots with superhuman ability at a variety of Atari 2600 games. In February
AlphaStar_(software)
Czech computer scientist and NLP researcher
Frederick Jelinek. He also spent several months in Yoshua Bengio's machine-learning laboratory at the Université de Montréal. After completing his PhD
Tomáš_Mikolov
Dutch-American AI researcher (born 1986)
specializing in natural language processing and machine learning. In 2016, Kiela joined Facebook AI Research (FAIR) as a postdoctoral researcher, later becoming
Douwe_Kiela
Virginia. 23 April 2019. "McIntire Professors Urge "Fairness by Design" Approach to Machine Learning in Harvard Business Review - Experience McIntire".
Ahmed_Abbasi
Serbian engineer
innovative applications of machine learning to diverse societal and business problems. Her current research focuses on issues of fairness, accountability, transparency
Aleksandra_Mojsilovic
Machine learning model for speech
Whisper is a machine learning model for speech recognition and transcription, created by OpenAI and first released as open-source software in September
Whisper (speech recognition system)
Whisper_(speech_recognition_system)
Machine used to stitch fabric
the 19th-Century Sewing Machine Industry." RAND Journal of Economic 44#4 2013, pp. 757–778. online Thomson, Ross. "Learning by Selling and Invention:
Sewing_machine
American computer scientist (born 1977)
of negligible fairness–accuracy trade-offs in machine learning for public policy. Kit Rodolfa, Hemank Lamba, Rayid Ghani. Nature Machine Intelligence 2021
Rayid_Ghani
Concept in information theory
referred to as the (order-1 true) diversity. In statistical modeling and machine learning, perplexity is also used to measure how well a proposed probability
Perplexity
American computer scientist
Machinery Conference on Fairness, Accountability, and Transparency. Her research seeks to prevent discrimination in machine learning. Friedler earned her
Sorelle_Friedler
American computer scientist
ethics of artificial intelligence, responsible data science, and fairness in machine learning. Beyond computer science, she has also published research on
Julia_Stoyanovich
Irish computer scientist
and fairness in machine learning. In 2025, she co-founded Adaption, a startup focused on creating AI systems capable of continuous real-time learning and
Sara_Hooker
1969 semi-autobiographical film by Gordon Parks
The Learning Tree is a 1969 American coming-of-age film written, produced and directed by Gordon Parks, who also scored the film. It depicts the life of
The_Learning_Tree
Analysis of social structures using network and graph theory
but fairness considerations have only recently gained traction within the design of social network analysis methods. Since the early 2020s, fairness-aware
Social_network_analysis
American software engineer
his vehicle automation machine learning company comma.ai. Since November 2022, Hotz has been working on tinygrad, a deep learning framework. Hotz attended
George_Hotz
American artificial intelligence company
Anthropic carries out and publishes research on the interpretability of machine learning systems. It has done research on "features" (patterns of neural activation
Anthropic
Conformance of AI to intended objectives
uncertainty, formal verification, preference learning, safety-critical engineering, game theory, algorithmic fairness, and social sciences. Programmers provide
AI_alignment
capabilities to save time and improve accuracy. Through the use of machine learning, artificial intelligence can substantially aid doctors in patient diagnosis
Artificial intelligence in healthcare
Artificial_intelligence_in_healthcare
American computer laboratory
goal is to pioneer research on how quantum computing might help with machine learning and other difficult computer science problems. The lab is hosted at
Quantum Artificial Intelligence Lab
Quantum_Artificial_Intelligence_Lab
American computer scientist (born 1976)
University, with research expertise in artificial intelligence, machine learning, deep learning, computer vision, and cognitive neuroscience. Li is a co-director
Fei-Fei_Li
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