Hands down, driving is one of the most dangerous things we all do each day. And the stats are scary. Every year on US roads, 40,000 people die and 6,000,000 are injured. Driving is the #1 cause of death among teens. And while autonomous vehicles promise a safer future, we needn’t wait that long. If we could eliminate 5 seconds of distracted driving and speed up car crash responses by 8 minutes, we could have safer roads today.
In this talk, Rafi Finegold, VP Consumer Experience at TrueMotion, will discuss how technology is being used at scale to quantify and understand driving behavior, and how UX and behavioral science techniques are being used to influence it.
Technologies are increasingly mediating our professional, social, and personal lives, bringing many benefits, but also introducing new challenges to privacy. In fact, we must go beyond the typical conversation around informational privacy (who can see what) and focus on a broader range of social privacy issues such as interactional (e.g., pressure to interact), psychological (e.g., impression management), or physical (e.g., concern others will come join me). Based on empirical research, this talk investigates various privacy issues around technology use and how addressing them is tied to understanding individual differences in user perceptions, preferences, and mental models. We explore how these differences shape technology use and non-use for a range of technologies. For example, differing mental models that underlie generational differences for preferred interaction with the internet of things, individual traits (e.g., an FYI communication style) that shape social media usage, and human-algorithm dynamics that change over time and impact work effectiveness. This talk discusses the challenges in designing for users who, in actuality, have diverse preferences and needs and concludes by providing design principles that can be used when considering your end-user.
Xinru is an Assistant Professor in Computer Information Systems at Bentley University and an affiliate of the Bentley Health Thought Leadership Network and the Bentley Data Innovation Network. Her work explores psychological and social factors that influence attitudes towards and use (or non use) of social media. Xinru has spoken on expert privacy panels in industry such as at the User Experience Professionals Association conference and the Facebook Research Speaker Series. Her latest work focuses on Privacy, Ethical Privacy Design and Research Practices, Technology Non Use, the influence of Individusal Traits or Life Phases on Social Media Use, Supporting Social Media use for those with Disabilities, and Human-Algorithm Interaction. Much of her current work focuses making meaningful translations between academic research and practice. Xinru’s research has been funded by Disney Research, Samsung, Yahoo! and the National Science Foundation. Her dissertation received the 2015 iSchools Doctoral Dissertation Award and a 2014 Yahoo! Best Dissertation Fellowship Award. Xinru has also worked in the information risk industry, leading interaction design and as a product manager. Utah’s Women Tech Council chose her as a Rising Star Tech Award finalist, recognizing “women driving innovation, leading technology companies, and [who] are key contributors to the community.” Xinru holds a Ph.D. in Information and Computer Science, concentration Informatics, from University of California, Irvine, and B.S. and M.S. in Computer Science, specialization Human-Computer Interaction, from Stanford University. She is actively involved in her academic community, publishes and holds many editorial leadership roles in her field’s top conferences such as CHI, CSCW, ICIS, ICWSM and Ubicomp, and enjoys volunteering in various community programs working with youth.
What is visual evidence? How do we know we can trust a data set? The methods of data visualization can help us discover patterns and inconsistencies in data sets, but are bound to the limits of symbolic representation – they can only begin when data already exists. Consequently, data is silent about its origins remains disconnected from the phenomena it supposedly represents. In my talk, I will discuss autographic visualization as a countermodel to data visualization. Autographic visualization describes a set of design principles and visual practices to reveal traces and material evidence. Autographic visualization aims to make data collection accountable and engaging, which can play an important role in discourses around climate change, pollution, and evidence construction
Dietmar Offenhuber is Associate Professor at Northeastern University in the fields of Art + Design and Public Policy. He holds a PhD in Urban Planning from MIT, Master degrees from the MIT Media Lab and TU Vienna. His research focuses on the relationship between design, technology, and governance. Dietmar is the author of the award-winning monograph “Waste is Information” (MIT Press), works as an advisor to the United Nations and published books on the subjects of Urban Data, Accountability Technologies and Urban Informatics.
