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"
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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 "
Read MoreIt’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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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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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"
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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"
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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"
Read MoreCommunityClick-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"
Read MoreThere 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"
Read More 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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