<html><head><meta http-equiv="Content-Type" content="text/html charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class=""><div class=""><div class=""><span class="" style="font-size: 14px;"><b class="" style="font-family: LucidaGrande; background-color: rgba(255, 255, 255, 0);"><u class="">Department of Computer Science </u></b><b class="" style="background-color: rgba(255, 255, 255, 0); font-family: LucidaGrande;"><u class="">Seminar</u></b></span></div></div><div class="" style="font-family: LucidaGrande;"><span class="" style="background-color: rgba(255, 255, 255, 0);"><br class=""></span></div><div class=""><div class="" style="font-family: LucidaGrande;"><span class="" style="font-size: 14px;">Thursday, April 6, 2017</span></div><div class="" style="font-family: LucidaGrande;"><span class="" style="font-size: 14px;">3:00 pm</span></div><div class="" style="font-family: LucidaGrande;"><span class="" style="font-size: 14px;">Ryerson 251 </span></div><div class="" style="font-family: LucidaGrande;"><br class=""></div><div class="" style="font-family: LucidaGrande;"><span class="" style="font-size: 14px;">Michael Correll</span></div><div class="" style="font-family: LucidaGrande;"><span class="" style="font-size: 14px;">(University of Washington)</span></div><div class="" style="font-family: LucidaGrande;"><br class=""></div><div class=""><span class="" style="font-size: 14px;"><span class="" style="font-family: LucidaGrande;">Title: </span><span class="" style="font-family: LucidaGrande;">Bias and Uncertainty in Information Visualization</span><br class="gmail-m_4827095062818533991gmail_msg"><br class="gmail-m_4827095062818533991gmail_msg"><span class="" style="font-family: LucidaGrande;">Abstract: </span></span></div><div class=""><font face="LucidaGrande" class=""><span class=""><span class=""><span class="" style="font-size: 14px;">We often turn to data to help us make sense of an uncertain world. However, the uncertainty in our data is often esoteric, complex, or counter-intuitive. It can be challenging to present this uncertainty, especially to audiences without backgrounds in statistics.<br class=""><br class="">Charts, graphs, and other visualizations of data address this issue by making people into “visual statisticians:” we can estimate statistical properties through visual inspection. However, just as statistical measures can be subject to bias, visualizations can also introduce bias. <br class=""><br class="">In this talk, I show how designers can intervene to create new visualizations that correct these biases, and improve the judgments of visual statisticians. From this perspective of designing for de-biasing, I focus on two common visualizations: error bars and thematic maps. I present visual alternatives for error bars that avoid “within-the-bar” bias while also promoting statistically grounded comparisons between means. I also present “Surprise Maps,” a technique for thematic maps that relies on Bayesian reasoning to highlight interesting regions that might otherwise be hidden in traditional maps. I conclude with a discussion of remaining challenges for visual de-biasing, and how we might use visualizations to encourage better, data-driven decision-making.<br class=""><br class=""></span><br class=""><span class="" style="font-size: 14px;">-----</span><br class=""></span><span class="" style="font-size: 14px;"> </span><span class="" style="font-size: 14px;"><br class=""></span></span></font><span class="" style="font-size: 14px;"><span class="" style="font-family: 'Lucida Grande';">Bio: </span></span><span class="" style="font-family: LucidaGrande; font-size: 14px;">Michael Correll is a postdoctoral research associate at the Interactive Data Lab at University of Washington. He received his PhD. in Computer Sciences from the University of Wisconsin-Madison in 2015. His</span><span class="" style="font-family: LucidaGrande; font-size: 14px;"> </span><span class="" style="font-family: LucidaGrande; font-size: 14px;">research focuses on information visualization, and more specifically on ways to present statistical information to general audiences. His other interests include graphical perception, visual rhetoric, and the</span><span class="" style="font-family: LucidaGrande; font-size: 14px;"> digital humanities.</span><div class=""><div class="" style="font-family: LucidaGrande;"><span class="" style="font-size: 14px;"><br class=""></span></div><div class="" style="font-family: LucidaGrande;"><span class="" style="font-size: 14px;"><br class=""></span></div></div><div class="" style="font-family: LucidaGrande;"><span class="" style="font-size: 14px;">Host: Gordon Kindlmann</span></div><div class="" style="font-family: LucidaGrande;"><span class="" style="font-size: 14px;"><br class=""></span></div><div class="" style="font-family: LucidaGrande;"><font size="2" class="">Refreshments in Ry. 255 after the talk</font></div></div></div></body></html>