Your visual system can crunch vast arrays of numbers at a glance, providing the rest of your brain with critical values, statistics, and patterns needed to make decisions about your data. But that process can be derailed by biases at both the perceptual and cognitive levels. I’ll demonstrate 3 instances of these biases that obstruct effective data communication. First, in the most frequently used graphs – lines and bars – reproductions of average values are systematically underestimated for lines, and overestimated for bars. Second, when people see correlational data, they often mistakenly also see a causal relationship. I’ll show how this error can be even worse for some types of graphs. Third, we’ve all experienced being overwhelmed by a confusing visualization. This may happen because the designer – an expert in the topic – thinks that you’d see what they see. I’ll describe a replication of this real-world phenomenon in the lab, showing that, when communicating patterns in data to others, it is tough for people to see a visualization from a naive perspective. I’ll discuss why these biases happen in our brains, and prescribe ways to design visualizations to mitigate them. 

We live in a time where innovation is outpacing our ability to process their social/environmental implications. From technological innovations like self-driving cars, artificial intelligence and machine learning, IoT to system logistics improvements, crowdsourcing, and car & bike sharing, as if the future is being designed and realized faster than ever. More interestingly art and design are playing an increasingly central role in designing, rendering and implementing these futures. In his presentation Benedikt will talk about design as a vehicle to explore new roles, contexts and approaches in relation to the social, cultural and ethical implications of emerging and future technologies. Benedikt will illustrate these thoughts with various projects.

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