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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 may take longer to be adopted in other domains. Concern for fairness in machine learning, that is, reducing bias in machine learning
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
not fairness, whereas Individual explanation increases both perceived fairness and trustworthiness. Group explanation decreases the perceived fairness and
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)
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
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
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
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
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
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
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
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
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
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)
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
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
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
Type of large language model
pre-training (GP) was a long-established technique in machine learning. GP is a form of self-supervised learning wherein a model is first trained on a large, unlabeled
Generative pre-trained transformer
Generative_pre-trained_transformer
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
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
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
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
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
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
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)
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
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
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
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
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
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
Canadian machine learning researcher
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
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
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
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
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
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
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
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 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
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
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 that generates content
manufacturing and decision plans such as in prototype autonomous spacecraft. Machine learning uses both discriminative models and generative models to predict or
Generative_AI
Legal and scientific dispute over 2021 Nature paper by Google
placement, a stage of chip floorplanning, based on reinforcement learning (RL), a machine learning method in which a system iteratively improves its decisions
AlphaChip_(controversy)
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
educator, and entrepreneur. His research is focused on the application of machine learning, knowledge representation, and artificial intelligence for the analysis
Nigam_Shah
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
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
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
"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
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
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
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
Model selection principle
statistics, theoretical computer science and machine learning, and more narrowly computational learning theory. Historically, there are different, yet
Minimum_description_length
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
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
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
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
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
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
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
Use of large language models as automated evaluators
research has also explored leveraging multiple LLM evaluators to improve fairness and scalability, and the idea of “LLM juries” has been proposed as a practical
LLM-as-a-Judge
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
2020 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
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
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)
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
Software system
based on the publicly available data. Another general criticism of machine-learning based algorithms is that since they are data-dependent, if the data
COMPAS_(software)
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
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
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
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
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
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
approaches, AI algorithms are modified to promote fairness and robustness. One option is federated learning, which enables models to be trained across decentralised
Artificial intelligence in healthcare
Artificial_intelligence_in_healthcare
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
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
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)
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
Educational approach aiming to promote learning by using video game design and elements
gamification of learning is an educational technology approach that seeks to motivate students by using video game design and game elements in learning environments
Gamification_of_learning
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)
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
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
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
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
Semi-annual technology conference held by Nvidia
Topics focus on AI, computer graphics, data science, machine learning and autonomous machines. Each conference begins with a keynote from Nvidia CEO
Nvidia_GTC
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)
Moral behaviours of man-made machines
systems. Fairness: People involved in conceptualizing, developing, and implementing machine learning systems should consider which definition of fairness best
Machine_ethics
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
AI accelerator ASIC by Google
application-specific integrated circuit (ASIC) developed by Google for neural network machine learning. Tensorflow, Jax, and PyTorch are supported frameworks for TPU. Google
Tensor_Processing_Unit
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
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
American computer scientist
Framework for Balancing Fairness and Accuracy in Debiasing Machine Learning Models, (with R.Nagpal, A.Khan and M.Borkar), Machine Learning and Knowledge Extraction
Amar_Gupta
Subfield of machine learning
decision-making and processes for applying computational technologies such as machine learning, natural language processing, reasoning, and semantics at scale. The
Decision_intelligence
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 computer scientist
research concerns the spread of information on social networks, and fairness in machine learning. She is a research professor at the University of Southern California
Kristina_Lerman
FAIRNESS MACHINE-LEARNING
FAIRNESS MACHINE-LEARNING
Girl/Female
Hindu, Indian, Kannada, Marathi
Fairness
Surname or Lastname
English
English : variant of Harness.
Female
German
German form of Scottish Malvina, MALWINE means "smooth-brow."
Female
English
Variant spelling of English Maureen, MAURINE means "obstinacy, rebelliousness" or "their rebellion."
Female
French
Feminine form of French Marin, MARINE means "of the sea."
Girl/Female
Tamil
Fairness
Surname or Lastname
English
English : variant spelling of Machen.Spanish (MachÃn) : probably a nickname from machÃn ‘boor’, ‘lout’, often applied to a blacksmith’s apprentice.French : nickname from Old French machin ‘scheming’.
Girl/Female
Australian, Japanese
Child of Machi
Male
Scottish
Pet form of Scottish Gaelic Lachlann, LACHIE means "lake-land."
Girl/Female
Indian, Telugu
Fairness
Girl/Female
Bengali, Indian
Machine
Male
French
French form of Latin Macarius, MACAIRE means "blessed."
Boy/Male
American, Australian
Weighing Machine
Female
Scottish
Feminine form of Scottish Lachlan, LACHINA means "lake-land."
Female
Native American
Native American Hopi name KACHINA means "sacred dancer; spirit."
Male
English
Pet form of English Sacheverell, SACHIE means "roe-buck leap."
Female
Hawaiian
Hawaiian name MAHINA means "moon; moonlight."
Boy/Male
Muslim
Fairness
Female
French
French feminine form of Latin Martinus, MARTINE means "of/like Mars."Â
Boy/Male
Indian
Fairness
FAIRNESS MACHINE-LEARNING
FAIRNESS MACHINE-LEARNING
Boy/Male
British, English
From the Red Meadow
Girl/Female
Indian
Daughter of al Mahdi (Daughter of al-mahdi)
Boy/Male
Tamil
Book
Girl/Female
Tamil
Full Moon, A festival, A special day
Girl/Female
Indian
Name of a poetess
Surname or Lastname
English (chiefly West Midlands)
English (chiefly West Midlands) : nickname for a cripple or hunchback, from English cromp, crump ‘bent’, ‘crooked’, ‘stooping’ (from Old English crumb). Compare Croom.Americanized spelling of German Krump, the variant Krumpp, or German and Dutch Kramp.
Boy/Male
Muslim/Islamic
One who shows the way Fearless or Brave
Boy/Male
Hindu, Indian, Tamil
Gem Spear; Gem Sword
Boy/Male
Tamil
Shreyanshu | à®·à¯à®°à¯‡à®¯à®¾à®‚à®·à¯
Girl/Female
Tamil
Aparoopa | அபாரூபா
Extremely beautiful
FAIRNESS MACHINE-LEARNING
FAIRNESS MACHINE-LEARNING
FAIRNESS MACHINE-LEARNING
FAIRNESS MACHINE-LEARNING
FAIRNESS MACHINE-LEARNING
v. t.
To subject to the action of machinery; to effect by aid of machinery; to print with a printing machine.
n.
One who or operates a machine; a machinist.
a.
Of or pertaining to machines.
n.
The working parts of a machine, engine, or instrument; as, the machinery of a watch.
n.
Lightness of spirits; gayety; levity; as, the airiness of young persons.
v. t.
To make ready for draught; to equip with harness, as a horse. Also used figuratively.
n.
Feebleness, as of color or light; lack of distinctness; as, faintness of description.
n.
Fairness; beauty.
n.
A combination of persons acting together for a common purpose, with the agencies which they use; as, the social machine.
pl.
of Tachina
imp. & p. p.
of Machine
a.
The state of being fast and firm; firmness; fixedness; security; faithfulness.
a.
Of or pertaining to cows; pertaining to, derived from, or caused by, vaccinia; as, vaccine virus; the vaccine disease.
n.
The state or quality of being fit; as, the fitness of measures or laws; a person's fitness for office.
n.
The state or quality of being airy; openness or exposure to the air; as, the airiness of a country seat.
n.
In general, any combination of bodies so connected that their relative motions are constrained, and by means of which force and motion may be transmitted and modified, as a screw and its nut, or a lever arranged to turn about a fulcrum or a pulley about its pivot, etc.; especially, a construction, more or less complex, consisting of a combination of moving parts, or simple mechanical elements, as wheels, levers, cams, etc., with their supports and connecting framework, calculated to constitute a prime mover, or to receive force and motion from a prime mover or from another machine, and transmit, modify, and apply them to the production of some desired mechanical effect or work, as weaving by a loom, or the excitation of electricity by an electrical machine.
n.
Machines, in general, or collectively.
v. t.
To wind marline around; as, to marline a rope.