1. Does introducing empathy limit the amount of information you can communicate?

2. What information helps viewers foster empathy for an individual? (If you can include only one additional piece of information for a person, what would it be?)

3. Are there ways to make data displays in professional setting more personal (trading floors, doctors offices, the senate, etc.) in order to have human understanding influence large scale decisions?

1. Simple geometric shapes vs. symbolic icons for representing data: Is one better than the other for creating greater empathy for the viewers?

2. Does the element of interactiveness always create more empathy for the user? For example, an interactive map showing the population by clicking on specific locations display the population in that country. How impactful is this interactive element in carrying out the 'feel' of the population?

3. What is one of the most important factors in designing a successful data graphics that should be included to make the viewer 'care'?

1) The issue of empathy in data vis is interesting. However, a data set is an abstraction by default. A data set consists of numbers vs a story consists of vivid details of an event. Empathy is data vis might be useful in journalism in some cases, but I am not sure it applies to other industries?
2) The argument about using blocks vs wee figures is not that convincing to me. Blocks further abstract the topic right?
3) I am interested in learning how the Ebola visualization is done technically.

1.How can you find the balance in abstracting and still being able to tell the information accurately and easily?
2. How can we make people empathic outside of journalism topics?
3. Is there a conflict between designing with empathy and designing with too much of a bias?

1. People have limited empathy reserves, and a lot of things are competing for them. This overloading can cause people to ignore news items entirely. It's a hard question to ask, but how do we decide to what degree and regarding which people, topics or situations we deploy these calls for empathy?

2. Empathy can also lead to increased bias in decision-makers, sometimes causing resource usage that unfairly disadvantages certain groups. At the same time, it can lead to attitude changes that reduce stereotype-based prejudice or stigmatization. What impact do news cycles and popularized data visualization have on each?

3. What are characteristics of pieces that may benefit from more or less complex graphics? How are we deciding what to include, and what to leave out?

1. To what extent are two-dimensional mediums an effective method to communicate data that requires an empathetic response?
2. Are there ways to present concrete data in more arbitrary forms?
3. Would emphasis on empathy alter the truth of the data for viewers?

1. How do we humanize information?

I think the basketball graph did a great job of adding an understanding to the information because it provided relatable information but does using icons that are generic take away the impact of empathy? (For example the NY Times article about poverty?) Are we less inclined to care if they are just an outline? Are we more sensitive if they are faces (such as the fallen soldiers?) but to Harris's point, how do we make it more than just a momentary interest and point of entry?

1. Does using a comparable subject matter to display the information make it have less of an impact? (not a good example but how Jennifer Daniels uses fruit to explain planets to create better understanding) but when talking about poverty or people's lives does comparisons like this cheapen the value?

2. I think the "Eye for an eye" graphic by Bonnie Berkowitz, Dan Keating and Richard Johnson does an exceptional job at exploring race in a way that takes away stigmas. It creates an opportunity to create a dialogue in a unique way. How do we maintain this ability of near and far by still maintaining impact and accuracy?

Do we need to supply 2 visualization examples for each story? One where we focus on human empathy and another where we present the larger, grander picture?

Empathetic design can't always be the ideal solution for presenting data, even when the data set is small enough that it would seem a good fit. Will it ever be possible to create generalized patterns for data vis?

Should data visualizations be produced with extreme time and care (like a digital product) to make sure they are always read as intended, even without descriptions? For breaking news articles that are time sensitive, it's hard to expect a quality visualization to accompany the story.

How can non-digital data visualisations present many levels of data? How can we present both the 20,000ft and 2ft view without resorting to the digital interactive?

Does humanising data really work, given other readings we've done on confirmation bias and people's unwillingness to listen to facts given by a source they dont identify with?
How can we use a more human approach to overcome confirmation bias, and how can we counter the bias in ourselves?

Is it ok to convince people of a good argument on an important point using fudged or badly handled data?

Are the silhouettes used to humanize the visualizations accurate? or are they stock vector graphics scaled up and down to the size of the player's height?

What are some ways the brilliant maps tweet can be improved to make people more empathetic?

What are common techniques we can use to humanize a visualization?