EQUATE & Machine Learning Blog

Category: Machine Learning

Towards an AI Accountability Policy

Posted: July 25, 2023 Under: EQUATE Fairness Machine Learning By Przemyslaw Grabowicz, Nick Perello, Yair Zick

It’s a new day for AI. AI systems can assist us in writing a new web script, decide whether we should worry about that weird spot in an X-ray, and find friends on social media. AI systems help in determining recidivism risk. Music-generating AI can render novel songs by Drake and the Weekend — that … Continue reading "Towards an AI Accountability Policy"

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AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning

Posted: October 06, 2022 Under: Artificial Intelligence and Machine Learning Computer Vision EQUATE Machine Learning By Lijun Zhang, Xiao Liu, Hui Guan

Full Abstract  Multi-task learning (MTL) jointly learns a set of tasks by sharing parameters among tasks. It is a promising approach for reducing storage costs while improving task accuracy for many computer vision tasks. The effective adoption of MTL faces two main challenges. The first challenge is to determine what parameters to share across tasks … Continue reading "AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning"

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Computing Must Pay Attention to Outcomes to Achieve Equity

Posted: April 07, 2022 Under: Fairness Machine Learning ML for Healthcare Privacy Social Science Transparency By Michelle Trim

There is a push right now across ACM that is gathering momentum. This push is for increased attention to cultural competency [12] in the training of computing professionals. In fact, the CS202X: ACM/IEEE-CS/AAAI Computer Science Curricula Taskforce [4] has a knowledge area subcommittee devoted to SEP or Society, Ethics, and Professionalism [5]. This subcommittee is … Continue reading "Computing Must Pay Attention to Outcomes to Achieve Equity"

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How to train models that do not propagate discrimination?

Posted: April 07, 2022 Under: Artificial Intelligence and Machine Learning Core ML EQUATE Fairness Machine Learning Social Science Transparency By Przemyslaw Grabowicz, Nicholas Perello, Aarshee Mishra

Powerful machine learning models can automatize decisions in critical areas of human lives, such as criminal pre-trial detention and hiring. These models are often trained on large datasets of historical decisions. However, past discriminatory human behavior may have tainted these decisions and datasets with discimination. Therefore, it is imperative to ask how can we ensure … Continue reading "How to train models that do not propagate discrimination?"

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Paper: Fairness Guarantees under Demographic Shift

Posted: April 06, 2022 Under: Core ML Fairness Machine Learning By Stephen Giguere (UT Austin, UMass Alumnus), Blossom Metevier (UMass), Yuriy Brun (UMass), Bruno Castro da Silva (UMass), Philip Thomas (UMass), Scott Niekum (UT Austin)

Full Abstract: Recent studies found that using machine learning for social applications can lead to injustice in the form of racist, sexist, and otherwise unfair and discriminatory outcomes. To address this challenge, recent machine learning algorithms have been designed to limit the likelihood such unfair behavior occurs. However, these approaches typically assume the data used … Continue reading "Paper: Fairness Guarantees under Demographic Shift"

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