Notes from Stephen Few’s Data Visualization talk in Vancouver on Jan 15 2016
FRI, 15 JAN 2016 AT 5:30 PM You Are What You Design: The Ethics of Data Visualization with Stephen Few Open Studios, Vancouver, BC [Eventbrite link]
By: Vancouver Data Visualization Meetup Vancouver Data Visualization Symposium 2! #VanDataViz
Presented in partnership with the Vancouver Institute for Visual Analytics
Taking inspiration from thoughtful designers in other fields, Stephen Few discussed interfaces and displays that raise the users up rather than dumb the data down.
Key Points that resonated with me:
Stephen Few: We visualize data to Identify patterns in data.
What is the essence of what you are trying to say with the data? Avoid "dumbing down the data".
What is the story in the data or the meaning in the data?
Visual thinking facilitates finding and discovering patterns and relationships, comparisons of the magnitude, finding the structure in the data.
How we look at data when trying to make sense of it, rather than when trying to communicate information, may be different.
Many complex problems require visual thinking. Visual thinking is different from verbal or textual.
Avoid eye candy that does not add to the information content or clouds the underlying meaning or makes data inaccessible.
Can technologies be good or bad? Can technology make us less capable? Can we lose the ability to think for ourselves?
Technology can make us smarter or make us dumber. What are human needs? Where does the human end and the technology begin?
Designers of technology have an ethical responsibility to design things that have a positive effect on the world.
What are the unintended consequences of what we do? How do we explain or manipulate our environment through design?
Are we responsible for the consequences of what we bring into the world?
Technology and design has the potential to move humankind forward.
Have to understand the context of the data and what you are visualizing. Look out for hidden biases.
Add nudges rather than restrictions. Don't forbid the user from making mistakes. Design so that mistakes are harder to make.
How will users know when they are making a mistake?
How do you promote more ethical thinking? We should model good behavior and speak up about bad behavior.
Sometimes there is no question about whether a situation or decision is good or bad, and the data can show this.
Morality is not a black/white issue. Can data be the arbiter? Sometimes there is no clear answer to make the best decision.
Try asking the question during design: What is the most evil thing or worst mistake that someone might do with this?
I don't think the inventor of the automobile expected global warming to happen, or the major impact on our cities - suburban sprawl.
What is the minimum amount of cognitive load required for engagement? Can you make it too easy? Need the user's investment in the process.
A large number of hits does not always mean that people understood the content. It's not a very good proxy for engagement.
We should not ignore the effectiveness of text and words, even when using or choosing visual thinking.
My raw notes from the evening (possibly more detailed, but incomplete):
We visualize data to Identify patterns in data.
Magnitude, outliers
What is the story in the data or the meaning in the data?
Finding and discovering patterns and relationships, comparisons of the magnitude, finding the structure in the data.
How we look at data when trying to make sense of it, rather than when trying to communicate the information may be different.
Many complex problems require visual thinking. Visual thinking is different from verbal or textual.
Don't waste people's time with nonsense.
Avoid eye candy that does not add to the information content or clouds the underlying meaning or makes the data inaccessible.
Donald Norman. Book: things that make us smart. Recommendation
Technology can make us smarter, or make us dumber. What are the human needs? Where does the human end and the technology begin?
Can technologies be good or bad? Can technology make us less capable? Can we lose the ability to think for ourselves?
What technologies are useful
Mike monteiro outspoken designer. Book: design is a job
Ethics of Facebook privacy controls. Outing of a lesbian woman, alienation. Why do we approach the job of design the way we do. What are the unintended consequences of the things that we do? What is our responsibility to the world that we live in. How do we explain or manipulate our environment through design. How to avoid carelessness in design. What are the consequences of our work?
Designers of technology have an ethical responsibility to design things that have a positive effect on the world.
What are the unintended consequences of the things that we do? How do we explain or manipulate our environment through design? How do we avoid carelessness in design. What are the consequences of our work?
Are we responsible for the consequences of what we bring into the world? Technology and design has the potential to move humankind forward.
What are the ethical responsibilities of what we design to bring into the world.
Viktor papanek. Design for the real world
Shape their tools and environments and therefore society.
Esthetic value vs functional. Attractiveness
To be a good data visualization designer we must know and learn Purposes of data visualization Activities of data visualization How the human brain Perceives and thinks Best practices of data visualization
Tools should be developed by people with these understandings
Have to understand the context of the data and what you are visualizing it. Look out for hidden biases.
Data visualization and tools should improve the quality of decision making rather than degrade the quality of decision making.
People who understand the problem should have the responsibility of checking the visualisations for accurate meanings.
Add nudges rather than restrictions. Don't forbid the user from making mistakes but design so that mistakes are harder to make.
How will users know when they are making a mistake?
How do you promote more ethical thinking? In user activities we should model good behavior and speak up about bad behavior.
We are often picking between nudges, for example the non min for y axis.
Morality is never a back and white issue. Can data be the arbiter? Sometimes there is no clear answer to make the "best" decision.
Sometimes there is no question about whether a situation or decision is good or bad, and the data can show this.
Adjusting the axis to show variances of data, while losing the bigger picture.
Asking the question during design: What is the most evil thing or worst mistake that someone might do with this?
Teamwork in design?
NASA control rooms, shared information. Operational control rooms Private displays
Large screens navigation is personal. Navigation failure.
Special memory How many piles can you remember?
Resize and reposition. Tool moon to.
What is the minimum amount of cognitive load required for engagement? Can you make it too easy? Need investment in the process. progress. When someone else is driving? How do we keep the user engaged in the thinking process so we are actually learning? Example of cake mix being too easy. The level of abstraction that the engagement occurs on doesn't have to be at the lowest level that was present before technology got involved in the process.
Sugar makes the medicine go down. Flash can attract hits, we can get informed. Trade off, need to add a little bit of bling.
Number of hits does not always reflect understanding. It's a not very good proxy for engagement.
We should not ignore the effectiveness of text and words, even when using visual thinking.
VR 3d mind mapping Vr visual space, spatial thinking, allowing large screens to be cheap for anyone to use. Can allow anyone with a cheap phone and headset (mobile VR) to have the full power of the boardroom. Screens are expensive. Sharing in presence through vr. Spatial mind mapping via Virtual reality. Using the analogy of a desk where you have piles and move stuff around, but there may be limits to how much spatial memory you can have. No need to be able to climb around a 3d bar chart.
Humans can process 2d information better than depth information: we have 2.5 dimensional processing, we use tricks such as occlusion and blurriness in background as tricks to gauge depth since our eyes can’t pick out depth so easily.
