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ML Fairness, short for Machine Learning Fairness, is an initiative by Google to implement fairness as a part of their machine learning techniques. [1] [2] [3] [4] The campaign is presented, as a means to stop political bias in artificial intelligence. [5]
Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of living beings, primarily of humans. It is a field of study in computer science that develops and studies intelligent machines. Such machines may be called AIs.
Fairness or being fair can refer to:
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance.
Meta learning is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing learning algorithms or to learn (induce) the learning algorithm itself, hence the alternative term learning to learn.
The ethics of artificial intelligence is the branch of the ethics of technology specific to artificial intelligence (AI) systems.
Google Brain was a deep learning artificial intelligence research team under the umbrella of Google AI, a research division at Google dedicated to artificial intelligence. Formed in 2011, Google Brain combined open-ended machine learning research with information systems and large-scale computing resources. The team has created tools such as TensorFlow, which allow for neural networks to be used by the public, with multiple internal AI research projects. The team aims to create research opportunities in machine learning and natural language processing. The team was merged into former Google sister company DeepMind to form Google DeepMind in April 2023.
DeepMind Technologies Limited, doing business as Google DeepMind, is a British-American artificial intelligence research laboratory which serves as a subsidiary of Google. Founded in the UK in 2010, it was acquired by Google in 2014, The company is based in London, with research centres in Canada, France, Germany and the United States.
Fei-Fei Li is a China-born American computer scientist, known for establishing ImageNet, the dataset that enabled rapid advances in computer vision in the 2010s. She is Sequoia Capital professor of computer science at Stanford University and former board director at Twitter. Li is a co-director of the Stanford Institute for Human-Centered Artificial Intelligence and a co-director of the Stanford Vision and Learning Lab. She served as the director of the Stanford Artificial Intelligence Laboratory from 2013 to 2018.
TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks.
Algorithmic bias describes systematic and repeatable errors in a computer system that create "unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm.
Crowdsource is a crowdsourcing platform developed by Google intended to improve a host of Google services through the user-facing training of different algorithms.
Timnit Gebru is an Eritrean Ethiopian-born computer scientist who works in the fields of artificial intelligence (AI), algorithmic bias and data mining. She is an advocate for diversity in technology and co-founder of Black in AI, a community of Black researchers working in AI. She is the founder of the Distributed Artificial Intelligence Research Institute (DAIR).
ACM Conference on Fairness, Accountability, and Transparency is a peer-reviewed academic conference series about ethics and computing systems. Sponsored by the Association for Computing Machinery, this conference focuses on issues such as algorithmic transparency, fairness in machine learning, bias, and ethics from a multi-disciplinary perspective. The conference community includes computer scientists, statisticians, social scientists, scholars of law, and others.
Andrea Frome is an American computer scientist who works in computer vision and machine learning.
Olga Russakovsky is an associate professor of computer science at Princeton University. Her research investigates computer vision and machine learning. She was one of the leaders of the ImageNet Large Scale Visual Recognition challenge and has been recognised by MIT Technology Review as one of the world's top young innovators.
Fairness in machine learning refers to the various attempts at correcting algorithmic bias in automated decision processes based on machine learning models. Decisions made by computers after a machine-learning process may be considered unfair if they were based on variables considered sensitive. For example gender, ethnicity, sexual orientation or disability. As it is the case with many ethical concepts, definitions of fairness and bias are always controversial. In general, fairness and bias are considered relevant when the decision process impacts people's lives. In machine learning, the problem of algorithmic bias is well known and well studied. Outcomes may be skewed by a range of factors and thus might be considered unfair with respect to certain groups or individuals. An example would be the way social media sites deliver personalized news to consumers.
The Algorithmic Justice League (AJL) is a digital advocacy non-profit organization based in Cambridge, Massachusetts. Founded in 2016 by computer scientist Joy Buolamwini, the AJL uses research, artwork, and policy advocacy to increase societal awareness regarding the use of artificial intelligence (AI) in society and the harms and biases that AI can pose to society. The AJL has engaged in a variety of open online seminars, media appearances, and tech advocacy initiatives to communicate information about bias in AI systems and promote industry and government action to mitigate against the creation and deployment of biased AI systems. In 2021, Fast Company named AJL as one of the 10 most innovative AI companies in the world.
Margaret 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 undesired biases concerning demographic groups from machine learning models, as well as more transparent reporting of their intended use.
Inioluwa Deborah Raji is a Nigerian-Canadian computer scientist and activist who works on algorithmic bias, AI accountability, and algorithmic auditing. Raji has previously worked with Joy Buolamwini, Timnit Gebru, and the Algorithmic Justice League on researching gender and racial bias in facial recognition technology. She has also worked with Google’s Ethical AI team and been a research fellow at the Partnership on AI and AI Now Institute at New York University working on how to operationalize ethical considerations in machine learning engineering practice. A current Mozilla fellow, she has been recognized by MIT Technology Review and Forbes as one of the world's top young innovators.
Himabindu "Hima" Lakkaraju is an Indian-American computer scientist who works on machine learning, artificial intelligence, algorithmic bias, and AI accountability. She is currently an Assistant Professor at the Harvard Business School and is also affiliated with the Department of Computer Science at Harvard University. Lakkaraju is known for her work on explainable machine learning. More broadly, her research focuses on developing machine learning models and algorithms that are interpretable, transparent, fair, and reliable. She also investigates the practical and ethical implications of deploying machine learning models in domains involving high-stakes decisions such as healthcare, criminal justice, business, and education. Lakkaraju was named as one of the world's top Innovators Under 35 by both Vanity Fair and the MIT Technology Review.
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