Five E’s: Reflecting on the Design of Recommendations
by Elizabeth F. Churchill (Google, USA)
Many case studies illustrate unintended consequences of well-intentioned systems. We have seen problems caused by information “filter bubbles”; problems caused by inappropriate, discriminatory or outright dangerous recommendations; issues of poor data quality leading to erroneous conclusions; and lack of clear methods, techniques, and tools for understanding how systems work and how to undo or reverse problems that have been caused.
These events, case studies, and stories have led to calls for what I call the three E’s of accountability in application, product, system, and service offerings–that they be more Explainable, Equitable, and Ethical. I’d like to raise two more critical E’s in socio-technical system design and development processes: Expedience and Exigence. It is critical that we address these two if we are going to realize the call for the first three E’s.
In this talk, I will reflect on the nature of recommendation through the lens of these 5 E’s to kick-start a conversation about recommendation ‘design’. I will draw on the psychology of human information processing, reasoning, and decision making, and will share observations, anecdotes, and cautionary tales to motivate some directions forward for recommendation and recommender design. I will then invite us to discuss: What, concretely, can researchers, developers, and designers do to address the 5 E’s?
About the Speaker
Elizabeth Churchill is a Director of User Experience at Google. Her field of study is Human Computer Interaction and User Experience, with a current focus on the design of effective designer and developer tools.
Elizabeth has built research groups and led research in a number of well known companies, including as Director of Human Computer Interaction at eBay Research Labs in San Jose, CA, as a Principal Research Scientist and Research Manager at Yahoo! in Santa Clara, CA and as a Senior Scientist at PARC and before that at FXPAL, Fuji Xerox’s Research lab in Silicon Valley.
Working across a number of research areas, she has published research, patented prototypes, and taught courses at a number of universities. She has more than 50 patents granted or pending, 7 academic books, and over 100 publications in theoretical and applied psychology, cognitive science, human-computer interaction, mobile and ubiquitous computing, computer mediated communication and social media. In 2016, she received the Citris-Banatao Institute Athena Award for Executive Leadership.
The current Secretary/Treasurer and incoming Vice President of the ACM, Churchill served as on the Executive Committee of the ACM’s Special Interest Group on Computer-Human Interaction (SIGCHI), for 8 years, 6 years of those as Executive Vice President and 2 as Vice President for Chapters. She has also held leadership committee positions on a number of ACM SIGCHI associated conferences. Elizabeth is a Distinguished Scientist and Distinguished Speaker of the ACM, and a member of the SIGCHI Academy.
Scalable Structured Prediction for Richly Structured Socio-Behavioral Data
by Lise Getoor (University of California, Santa Cruz, USA)
Online recommender systems, content-provider sites, and social media platforms provide richly structured socio-behavioral data. However, using this noisy and incomplete data to make decisions and recommendations is challenging. It often requires complex forms of structured prediction that rely on both the logical structure in the domain and probabilistic dependencies among interlinked entities. In this talk, I will describe some common inference patterns that are useful for socio-behavioral networks and introduce probabilistic soft logic (PSL). PSL is a highly scalable open-source probabilistic programming language being developed within my group that is well-suited for structured prediction over socio-behavioral data. Finally, I will review some of our recent work using PSL for hybrid recommender systems, explanation, and fair decision making.
About the Speaker
Lise Getoor is a Professor in the Computer Science Department at UC Santa Cruz and founding Director of the UC Santa Cruz Data Science Research Center. Her research areas include machine learning and reasoning under uncertainty, with a focus on graph and network data. In addition, she works in data management, data integration, and visual analytics. She has over 250 publications, including 11 best paper awards. She is a Fellow of the Association for Artificial Intelligence, an elected board member of the International Machine Learning Society, has served on the board of the Computing Research Association (CRA) and AAAI Council, and has served as Machine Learning Journal Action Editor, Associate Editor for the ACM Transactions of Knowledge Discovery from Data, and JAIR Associate Editor. She was co-chair for ICML 2011, and has served on the senior PC of conferences including AAAI, ICML, ICWSM, KDD, SIGMOD, UAI, VLDB, WSDM and WWW. She received her PhD from Stanford University in 2001, her MS from UC Berkeley, and her BS from UC Santa Barbara, and was a Professor in the Computer Science Department at the University of Maryland, College Park from 2001-2013.
Recommending Social Cohesion
by Christopher Berry (Canadian Broadcasting Corporation, Canada)
Public media produces a public good in the form of social cohesion. Generally, countries with strong social cohesion enjoy better security, economies, and qualities of life. CBC-Radio-Canada has long used technology to bring Canadians together, against all the forces that drive us apart. Recommendation Systems are just such a technology. Join me in some optimism about how we can build the future we want to see.
About the Speaker
Christopher is a data scientist. He turns data into product. He does it at the Canadian Broadcasting Corporation where he leads the product intelligence team. Previously, he founded authintic (sold to 500px in 2014), and led teams at Syncapse and Critical Mass.
Recently, he brought machine intelligence, addressable segmentation, and testing to CBC. He’s done breakthrough social analytics / contagion programs for AB-Inbev, Research In Motion, and Coca-Cola. He participated in eCommerce redesigns at Gucci and Dell, mobile integrations for Best Buy USA, and banking innovation at Citi, JP Morgan Chase and USAA. He was also part of the team responsible for the Webby awarded redesign of NASA.gov.
Christopher has spoken at Strata San Francisco, SXSW, DAA Symposium, multiple eMetrics conferences in New York, London, San Francisco, and Toronto, IMC, NCDM, and the Toronto Data Mining Forum. He’s an organizer of Machine Intelligence Toronto, and active among INFORMS researchers. Previously, he co-chaired the Digital Analytics Association’s Research Committee and led the Peer Review Journals Project.
