EQUATE – EQUATE & Machine Learning Blog

Can public support for US presidential candidates be accurately estimated by correcting biases in social polls published on X?

Posted: November 05, 2024 Under: Digital Civics EQUATE Fairness Transparency By Przemyslaw Grabowicz

Thousands of social polls on X suggest that Trump is leading the election race by a landslide. While many recognize the bias in these polls, there’s an unexpected—and fascinating—twist, reminiscent of Asimov’s sci-fi. Informal political polls have grown in popularity on X, the platform formerly known as Twitter. For instance, one such poll, conducted recently … Continue reading "Can public support for US presidential candidates be accurately estimated by correcting biases in social polls published on X?"

Fair Machine Learning Post Affirmative Action

Posted: December 13, 2023 Under: Artificial Intelligence and Machine Learning EQUATE Fairness By Nick Perello, Przemyslaw Grabowicz

The U.S. Supreme Court, in a 6-3 decision on June 29, effectively ended the use of race in college admissions. Indeed, national polls found that a plurality of Americans – 42%, according to a poll conducted by the University of Massachusetts – agree that the policy should be discontinued, while 33% support its continued use … Continue reading "Fair Machine Learning Post Affirmative Action"

My Model is Unfair, Do People Even Care? Visual Design Affects Trust and Perceived Bias in Machine Learning

Posted: October 16, 2023 Under: EQUATE Fairness Social Science By Aimen Gaba, Zhanna Kaufman, Jason Cheung, Marie Shvakel, Kyle Wm Hall, Yuriy Brun, and Cindy Xiong Bearfield

Full Abstract Machine learning technology has become ubiquitous, but, unfortunately, often exhibits bias. As a consequence, disparate stakeholders need to interact with and make informed decisions about using machine learning models in everyday systems. Visualization technology can support stakeholders in understanding and evaluating trade-offs between, for example, accuracy and fairness of models. This paper aims … Continue reading "My Model is Unfair, Do People Even Care? Visual Design Affects Trust and Perceived Bias in 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"

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"

Equi-explanation Maps: Concise and Informative Global Summary Explanations

Posted: July 18, 2022 Under: EQUATE Transparency By Tanya Chowdhury, Razieh Rahimi, James Allan

Full Abstract We attempt to summarize the model logic of a black-box classification model in order to generate concise and informative global explanations. We propose equi-explanation maps, a new explanation data-structure that presents the region of interest as a union of equi-explanation subspaces along with their explanation vectors. We then propose E-Map, a method to … Continue reading "Equi-explanation Maps: Concise and Informative Global Summary Explanations"

ClioQuery: Interactive Query-Oriented Text Analytics for Comprehensive Investigation of Historical News Archives

Posted: July 17, 2022 Under: Artificial Intelligence and Machine Learning EQUATE Natural Language Processing Social Science Transparency By Abram Handler, Narges Mahyar, Brendan O’Connor

Historians and archivists often find and analyze the occurrences of query words in newspaper archives, to help answer fundamental questions about society. But much work in text analytics focuses on helping people investigate other textual units, such as events, clusters, ranked documents, entity relationships, or thematic hierarchies. Informed by a study into the needs of … Continue reading "ClioQuery: Interactive Query-Oriented Text Analytics for Comprehensive Investigation of Historical News Archives"

Model Explanations with Differential Privacy

Posted: June 07, 2022 Under: EQUATE Privacy Transparency By Neel Patel, Reza Shokri, and Yair Zick

Full Abstract: Black-box machine learning models are used in critical decision-making domains, giving rise to several calls for more algorithmic transparency. The drawback is that model explanations can leak information about the data used to generate them, thus undermining data privacy. To address this issue, we propose differentially private algorithms to construct feature-based model explanations. … Continue reading "Model Explanations with Differential Privacy"

CommunityClick-Virtual: Supporting Inclusive Participation during Online Public Engagement Events

Posted: May 26, 2022 Under: Digital Civics EQUATE Social Science Visual Analytics By Narges Mahyar, Ali Sarvghad, Mahmood Jasim

CommunityClick-Virtual: Supporting Inclusive Participation during Online Public Engagement Events Public engagement is paramount for participatory democracy. For decades, traditional methods, such as town halls, public forums, and workshops have remained the modus operandi for public engagement. The goal of these engagements is to ensure inclusivity of public participation so that decision-makers can engage, exchange thoughts, … Continue reading "CommunityClick-Virtual: Supporting Inclusive Participation during Online Public Engagement Events"

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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