Abstract:

Gender inclusiveness in software companies is receiving a lot of attention these days, but it overlooks a potentially critical factor: software itself. Research into how individual differences cluster by gender shows that males and females often work differently with software for problem-solving (e.g., tools for programming, debugging, spreadsheet modeling, end-user programming, game-based learning, visualizing information, etc.). In this talk, I'll present a method we call GenderMag to reveal gender biases in user-facing software.  At the core of the method are 5 facets of gender differences drawn from a large body of foundational work on gender differences from computer science, psychology, education, communications, and women's studies. I'll also present highlights from real-world investigations of software practitioners' ability to identify gender-inclusiveness issues,  using GenderMag,  in software they create/maintain. Results from the field studies were that software practitioners identified a surprisingly high number of gender-inclusiveness issues in their own software. We present these results and more, along with tales from the trenches on what it’s like to use GenderMag, where the pitfalls lie, and all the things we are still in the process of learning about it. 

Biography:

Margaret Burnett is a Distinguished Professor at Oregon State Unversity, an ACM Distinguished Scientist, and a CHI Academy member. Her research on gender inclusiveness in software -- especially in software tools for programming and problem-solving -- spans over 10 years. Prior to this work, most gender investigations into software had addressed only gender-targeted software, such as video games for girls. Burnett and her team systematically debunked misconceptions of gender neutrality in a variety of software platforms, and then devised software features that help avert the identified problems. She has reported these results in over 30 publications, and has presented keynotes and invited talks on this topic in 8 countries. She serves on a variety of HCI and Software Engineering committees and editorial boards, and on the Academic Alliance Advisory Board of the U.S. National Center for Women & Information Technology (NCWIT). More on Burnett can be found at: http://web.engr.oregonstate.edu/~burnett/

Abstract:

The real-world big data are largely unstructured, interconnected, and in the form of natural language text.  One of the grand challenges is to turn such massive data into structured networks and actionable knowledge.  We propose a text mining approach that requires only distant or minimal supervision but relies on massive data.  We show quality phrases can be mined from such massive text data, types can be extracted from massive text data with distant supervision, and entities/attributes/values can be discovered by meta-path directed pattern discovery.  Finally, we propose a data-to-network-to-knowledge paradigm, that is, first turn data into relatively structured information networks, and then mine such text-rich and structure-rich networks to generate useful knowledge.  We show such a paradigm represents a promising direction at turning massive text data into structured networks and useful knowledge.

Biography:

Jiawei Han is Abel Bliss Professor in the Department of Computer Science, University of Illinois at Urbana-Champaign.  He was a professor in the School of Computing Science, SFU, from 1987 to 2001.  He has been researching into data mining, information network analysis, database systems, and data warehousing, with over 800 journal and conference publications. He has chaired or served on many program committees of international conferences in most data mining and database conferences.  He also served as the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data and the Director of Information Network Academic Research Center supported by U.S. Army Research Lab (2009-2016), and is the co-Director of KnowEnG, an NIH funded Center of Excellence in Big Data Computing since 2014.  He is Fellow of ACM, Fellow of IEEE, and received 2004 ACM SIGKDD Innovations Award, 2005 IEEE Computer Society Technical Achievement Award, and 2009 M. Wallace McDowell Award from IEEE Computer Society.  His co-authored book "Data Mining: Concepts and Techniques" has been adopted as a textbook popularly worldwide.