Holger Kuehnle: As a design­er work­ing with tech­nol­o­gy, I’ve always been real­ly pas­sion­ate about tech­nol­o­gy. I always saw tech­nol­o­gy as some­thing that empow­ers peo­ple, that helps peo­ple com­mu­ni­cate bet­ter, that helps peo­ple under­stand infor­ma­tion, and with that build knowl­edge and remove bias and bring us all togeth­er as a society. 

And then this hap­pened. Someone who seem­ing­ly no one wants to see become President became pres­i­dent. People in the UK—the United Kingdom—became very very afraid about the sheer num­ber of peo­ple liv­ing in Turkey and what would hap­pen if Turkey would join the EU, [what] that would mean to their social secu­ri­ty sys­tem. 76 mil­lion peo­ple in Turkey. And it prompt­ed them to vote to leave the EU. People start­ed get­ting very very very angry and agi­tat­ed about issues such as how much the United States spends on for­eign aid. And so, I’m look­ing at this and I’m not even quite sure what this means. Three bil­lion less, twelve bil­lion more. To me as and indi­vid­ual, and I’m assum­ing this goes for a lot of oth­er peo­ple on the stage here—for most of us—any bil­lion amount of amount of dol­lars sure sounds like a lot of dol­lars to me. 

So what’s going on here? In the first exam­ple, we’re look­ing at how many peo­ple attend­ed an event dur­ing a cer­tain spe­cif­ic moment in time, and we’re using that—we’re spend­ing a lot of media time—talking about how many peo­ple were there, how many peo­ple weren’t there, to make an assump­tion about whether this per­son is a legit­i­mate pres­i­dent or not. If you think that this per­son should not be pres­i­dent, your moment to do that as an active cit­i­zen was like­ly dur­ing the elec­tion. At this point this per­son­’s already stand­ing there up on stage get­ting inaugurated. 

And in the sec­ond exam­ple, we’re using a very large num­ber, 76 mil­lion peo­ple, cre­at­ing assump­tion that Turkey join­ing the EU would mean that all these peo­ple could sud­den­ly come to the United Kingdom and have a neg­a­tive impact on the social sys­tem there. 

And in this third exam­ple, we’re talk­ing about increas­es or decreas­es to a num­ber. A num­ber that’s actu­al­ly not men­tioned here. I don’t actu­al­ly know from look­ing at these head­lines how much mon­ey is being spent on for­eign aid over­all. And how has that been the past cou­ple of years—and prob­a­bly most impor­tant­ly so, for me to have a real­ly good opin­ion about whether that’s good or bad, what are we using this mon­ey for? What is actu­al­ly for­eign aid used for? 

So what do these exam­ples have in com­mon? They take a sin­gle num­ber out of con­text and bias toward a spe­cif­ic con­clu­sion that actu­al­ly fuels divi­sive­ness. The key here is con­text. You need con­text. And I’ll talk a lit­tle bit about how con­text is nec­es­sary to real­ly under­stand data, nec­es­sary to under­stand num­bers. And I want to do that with an example. 

So let’s imag­ine some­body shouts, ​“Ball!” What does that mean, ball? Should I duck, should I be scared? Like am I going to be hit by a ball? Am I on a sports field, should I hold our my hands and catch a ball and that’s a good thing? I don’t know. I have no idea of know­ing what ball means if you just say ​“ball.”

Is it a ball that I’m sup­posed to kick? Is it a ball that I’m sup­posed to uh… Either way, it’s sup­posed to be a foot­ball as well. 

Is it a small ball? Is it a large ball, a big heavy ball? I need a way to quan­ti­fy what this ball is. What does this ball mean to me from a quan­ti­ta­tive perspective? 

Well, how large is it com­pared to you how large I am? If it’s sort of you know, slight­ly small­er than my head, okay. I under­stand that. That gives me quan­ti­ta­tive con­text. That’s one of the key ele­ments of con­text I need to be able to quan­ti­fy what it means. 

And then next what’s essen­tial is under­stand­ing the his­tor­i­cal con­text. Where has this ball been before, where is it right now, and with those two com­po­nents I might infer with some amount of con­fi­dence, or less, where it’s going to be in the future. Is it fly­ing towards me? I need to under­stand the his­tor­i­cal context. 

And then next it mat­ters a great deal about where all this is hap­pen­ing. This bal­l’s fly­ing towards me. Alright, I under­stand. It’s sort of a small ball, slight­ly small­er than my head. I under­stand a lit­tle bit more what ​“ball” now sud­den­ly means. 

Well, if I’m hold­ing a ten­nis rack­et, and I’m on a ten­nis court, that’s a great thing. Ball’s fly­ing towards, fly­ing towards my ten­nis rack­et, every­thing’s great. 

But if the envi­ron­ment that the ball exists in changes. If let’s say I’m stand­ing on the oppo­site side of a win­dow and that bal­l’s fly­ing towards me, that’s kind of a bad thing. Like I’m not going to be very very hap­py about that. 

So, the rela­tion­ships that this ball exists in mat­ters. It’s the rela­tion­al con­text that the ball exists in. So in order to under­stand con­text we need to quan­ti­fy, we need to under­stand the his­toric con­text, and we need to under­stand all the rela­tion­ships that what we’re talk­ing about exist in.

So how does all that apply to num­bers, right? I’m stand­ing up here, sup­posed to talk about data. Well, I’m gonna give you a cou­ple of exam­ples of a project that I’ve been work­ing on as a design lead at Artefact with one of our clients. And that project is called USA Facts. USA Facts is an unpar­ti­san plat­form for aggre­gat­ing gov­ern­ment data, with the goal of mak­ing that data eas­i­er to under­stand, more acces­si­ble, with the over­ar­ch­ing mis­sion to dri­ve and fos­ter more civ­i­lized pub­lic dis­course that is based in facts. And what USA Facts does is it takes all this gov­ern­ment data, puts it into a for­mat that is uni­ver­sal to some extent and can be ana­lyzed and then pub­lish­es it on their web platform. 

It does that with­out any form of inter­pre­ta­tion. So USA Facts does not take sides. And that’s actu­al­ly quite chal­leng­ing. And if you have to imag­ine like, this data’s actually—you can today go and access all sorts of gov­ern­ment data. It’s avail­able. However it’s all caught up in dif­fer­ent reports. It’s being pub­lished by dif­fer­ent agen­cies that have dif­fer­ent sched­ules of when they pub­lish these reports. It might be in a spread­sheet some­where. And so USA Facts does all this work of dig­ging this data up and mak­ing it comprehensible. 

We’ve work­ing with them for a while and we’ve built visu­al­i­sa­tions in which we’re try­ing as much as pos­si­ble to put data in con­text. I’m gonna show you a cou­ple of exam­ples of how we’re doing this. 

Government Revenue & Expenditures; USA Facts

So, maybe we’ll go back to the pre­vi­ous exam­ple when we talked about for­eign aid. Remember we talked about three bil­lion less, twelve bil­lion more—well, we don’t know how much we’re spend­ing in total in for­eign aid. These are 2015 num­bers right now. This is a visu­al­iza­tion which breaks down the spend­ing, the total spend­ing of the United states across dif­fer­ent areas such as defense, wel­fare, and sub-units of those, of those spend­ing areas. So here we can see that in 2015 the United States spend $5.7 tril­lion. That is actu­al­ly real­ly a lot of dol­lars now, right? 

And so when we go over here and we scroll over a lit­tle bit to the right, we can see how that spend­ing is being bro­ken down. I can see it in rel­a­tive con­text to child safe­ty, defense…and, down here, for­eign aid. And we can see that in 2015, the United States spent $48.5 bil­lion. That is real­ly a lot of mon­ey as well. But if you look at it in rela­tion to the total spend­ing of the United States, it is only .86—making sure I have my num­bers right here—.86%. And that allows me to much bet­ter quan­ti­fy whether I think that the three bil­lion of twelve bil­lion more is bad, in con­text. Because I’m able to quan­ti­fy it, to have con­text that I can now see. 

US Immigration Throughout History; USA Facts

Let’s take a look at anoth­er exam­ple of estab­lish­ing con­text in a visu­al­iza­tion. This one I’ll have to explain a lit­tle bit because it’s built for actu­al­ly being looked at in a brows­er so we’ll adapt it for stage a lit­tle bit. It’s actu­al­ly turned to the side because it’s meant for scrolling through. This visu­al­iza­tion shows the num­ber of peo­ple liv­ing in the United States that were not born in the United States—the foreign-born pop­u­la­tion. And it’s actu­al­ly turned to its side because it’s much more immer­sive to scroll that way. And it allows you to see that num­ber over time, in jux­ta­po­si­tion to his­tor­i­cal events that might’ve hap­pened that are impor­tant to consider. 

Another impor­tant piece here is that we’re show­ing this as a per­cent­age, not as an absolute num­ber. That’s anoth­er way of quan­ti­ta­tive con­text, is that you want to make sure that you look at it, as the num­ber goes up and down that you’re also con­sid­er­ing that pop­u­la­tion might’ve been going up and down total, which will actu­al­ly like to assess the impact much better. 

So we’re see­ing here that in 2012 we had about 12% of peo­ple liv­ing in the United States that were not born here. And if you look at sort of the polit­i­cal cli­mate, there are def­i­nite­ly peo­ple that say, ​“Oh yeah, there’s been an influx. There’s been a flood of immi­grants. They’re all com­ing here. And it’s been a ton. And the pre­vi­ous admin­is­tra­tion—” etc., etc., etc. 

So let’s take a look at what that looks like. Are we sud­den­ly in a flood of immi­gra­tion? Well, if we go back in time here a lit­tle bit, we can see that yeah, it’s been going up; it’s been up quite a bit. And now it goes to the 50s and the 60s, you can kind of see well, wait a minute there’s sort of a dip and now it’s going back up as we go back in time. And in 1910, we can see oh wait, it’s almost been 15% peo­ple in the United States that were not born here. 

And the con­text here is also that I see that that was actu­al­ly when a lot of peo­ple from Italy or Norway or Sweden came the United States. So we had a flood of immi­grants back then as well. And that helps me to actu­al­ly look at immi­gra­tion in much more con­text where I under­stand that if you talk about a flood, let’s take a look at actu­al­ly what flood looks like over time, so I can under­stand it bet­ter and see is it going up? Is that an unusu­al thing? Is that maybe some­thing that hap­pens in waves? And what are the poten­tial fac­tors that cause these waves? So that gives me a his­tor­i­cal con­text of the num­ber of when we say there are so many for­eign­ers liv­ing in the United States. Like myself. 

Let’s take a look at anoth­er exam­ple for con­text. This is a microsite that we designed for USA Facts last year for the midterm elec­tions. We call this a Voter Center. What it does is you can see in the bot­tom right here, it shows you stances of can­di­dates. These are things that can­di­dates have said on spe­cif­ic issues. And the Voter Center breaks down these issues into things that are rel­e­vant to active cit­i­zens mak­ing a decision—in this case voting—such as the econ­o­my, health­care, immi­gra­tion, defense. Things that real­ly are real­ly talked about a lot in the polit­i­cal agenda. 

And you can see the can­di­dates’ stances. And what USA Facts is doing is it’s jux­ta­pos­ing these stances with data—it’s not nec­es­sar­i­ly a fact-checking, because it’s not inter­pret­ing and it’s not biased, and not real­ly try­ing to take a stance. But it says ​“if you lis­ten to this politi­cian, here’s the types of data that you should look at to estab­lish con­texts and under­stand­ing of when some­body says, in this case, that say the econ­o­my is doing bad. Or the econ­o­my is doing well. And it’s because of the admin­is­tra­tion, or not the admin­is­tra­tion. You need to look at a mul­ti­tude of fac­tors. You need to look at…for exam­ple a pop­u­lar one for econ­o­my is GDP—it’s the strength of the economy. 

But that’s real­ly only one fac­tor, so if we keep going, you can kind of see here, you can go down and see that there are oth­er fac­tors that you might want to con­sid­er. There’s like, how much pri­vate invest­ment is there? What might be the gold price? How many busi­ness­es are open­ing? How many busi­ness are clos­ing? What’s the employ­ment look­ing like? What’s the medi­an annu­al wave. Those are things that you would need to look at to con­sid­er if you want to have a clear image of how well the econ­o­my might be doing. 

Now you may look at this and say, ​“Whoa. That’s kind of a lit­tle bit of a…you know, eye chart. It’s a lit­tle bit of a rough sort of slide here. There’s a lot of num­bers here. You’re throw­ing up a wall of num­bers.” At a design con­fer­ence; it’s a pret­ty bold move. 

I agree. There are a lot of num­bers here. And I do think that there’s a lot of com­plex­i­ty here. You’re absolute­ly right. And you sort of might be rub­bing your eyes a lit­tle bit. 

And I’m actu­al­ly gonna jump into some­thing that Bill brought up a lit­tle bit ear­ly this morn­ing, is we’ve done a great job I think as a dis­ci­pline, as inter­ac­tion design­ers, in tak­ing tech­nol­o­gy and mak­ing it easy to use. We went from hav­ing these machines—and Bill had a pic­ture of it as well—of these com­put­ers that would fill entire rooms or mul­ti­ple rooms, and you need­ed a physics degree or a math­e­mat­ics degree, and maybe even lat­er a com­put­er sci­ence degree, in order to oper­ate them and actu­al­ly derive val­ue out of them.

And I want to give cred­it to inter­ac­tion design to say we went from these these room-fill[ing] machines to some­thing that fits in the palm of your hand and for exam­ple allows you to man­age your men­tal health. We cre­at­ed We cre­at­ed these from these machines that worked for a very few and they could be used by very few peo­ple, to mak­ing them so easy to use that almost every­one was able to use them. And that’s a ter­rif­ic accom­plish­ment. So can sort of give our­selves a hand a lit­tle bit. This is great. 

However. There are cer­tain com­plex­i­ties that can’t be sim­pli­fied as easy as just reduc­ing them. We’ve done a great job at cre­at­ing plat­forms and tech­nolo­gies that allow us to com­mu­ni­cate with every­body, near­ly every­one, on the plan­et in a mat­ter of sec­onds very very easily. 

[Donald Trump, via Twitter]

But maybe some top­ics can not real­ly be dis­cussed with a suf­fi­cient amount of con­text that is nec­es­sary to ful­ly under­stand them in 140 char­ac­ters or slight­ly more. You need more con­text to under­stand them. They don’t fit. They are com­pli­cat­ed top­ics. They are not eas­i­ly simplified. 

We’ve made tech­nol­o­gy so easy to use that in cer­tain cas­es I can sit on my couch and get hours and hours of con­tent that’s specif­i­cal­ly tailored—that smart algo­rithms that are real­ly great will feed me end­less hours of con­tent that will fit my taste and help me enjoy my life. And in this case I don’t even have to do any­thing to get more of it. Some of the great­est inter­ac­tion design of all is sort of the inter­ac­tion design that does­n’t require the user to do any­thing, right? That’s great. Doing noth­ing is great. 

But in cer­tain cas­es, doing some­thing is actu­al­ly real­ly impor­tant. There are top­ics where we want peo­ple to take actions. We want peo­ple to take very very informed action. This map shows the edu­ca­tion­al attain­ment of women age 25 and old­er in some coun­tries in Africa, in this case specif­i­cal­ly Nigeria. So I look at this map and I can see alright, the edu­ca­tion­al attainment—this is the a num­ber of years of edu­ca­tion that women get on aver­age is about sev­en. I may know that in oth­er coun­tries such as the United States, Germany, the UK, that num­ber is about thir­teen on aver­age. So 

I can see now like well you know, Nigeria…it’s not doing so great in that regard. Okay. But I don’t actu­al­ly know what to do about the sit­u­a­tion. If I’m some­body who’s invest­ed in improv­ing the sit­u­a­tion I don’t real­ly know where to start and say, ​“Well, I guess we’ve got to make edu­ca­tion better.” 

But if I add com­plex­i­ty to this real­iza­tion, I can now see that the prob­lem is actu­al­ly a lit­tle bit more nuanced. This is a map cre­at­ed by the Institute for Health Metrics and Evaluation. It’s part of the University of Washington. And what they do is they map health data specif­i­cal­ly but also oth­er fac­tors that fac­tor into health, such as edu­ca­tion, on a five by five-kilometer basis; these esti­mates do that. 

And it adds a lot of com­plex­i­ty. If you look at this now I see wow, there’s a lot going on here now. But what I also see is hm, the edu­ca­tion is actu­al­ly much worse in the north and it’s in the south, and in the south there are actu­al­ly pock­ets where it’s doing quite well. That’s interesting. 

So if I look at this I can now say if I am involved in this and I want to invest in edu­ca­tion in Nigeria, I might focus my efforts, my invest­ments, to the north. And I may actu­al­ly look at the south and see what’s going well there. And what are the oth­er fac­tors that actu­al­ly impact the sit­u­a­tion being the way it is in the north ver­sus the south. So the com­plex­i­ty here actu­al­ly inspires action. Whereas before, the sim­pli­fi­ca­tion sort of blan­kets the prob­lem where I don’t real­ly know where to start to address it. 

And so for us as inter­ac­tion design­ers, as we’re look­ing at prob­lems, as we’re being tasked with com­mu­ni­cat­ing infor­ma­tion, we should look at when some­body’s telling you, ​“Well, we need to say that the quan­ti­ty of Blah ix ​‘X,’ we should strive to say we should help users, help our audi­ence under­stand the quan­ti­ta­tive of nature of X. How much is a lot of X, how much is a lit­tle X? We should also look at Understanding of well his­tor­i­cal­ly has X been a lot and has X been a lit­tle? We need the his­tor­i­cal con­text. And we also should look at all the fac­tors that mat­ter around X. What’s the rela­tion­al con­text that X exists in? And for those peo­ple in the audi­ence that’re sort of real­ly big on data, like­ly more than me because I’m just a design­er, I’m not mean­ing that these mean cor­re­lat­ed fac­tors. That does­n’t mean that there’s a sta­tis­ti­cal sig­nif­i­cance of say­ing ​“well, if you look at the X then Y is always going up or down…” These are just fac­tors that mat­ter to what might dri­ve X, what X may dri­ve in regard… Those are the things to look at. Help users quan­ti­fy the num­bers, help uses see the num­bers in his­tor­i­cal con­text, and also show them all the fac­tors that exist that I should under­stand in order to form my opin­ion about what we should do. 

And I think as inter­ac­tion design­ers, we’ve focused so much on mak­ing things easy. I think we should use the tools… Surya talked about our tool­box­es this morn­ing. We should use our tool­box to make com­plex­i­ty under­stand­able. We need to use the tools at our dis­pos­al to build data lit­er­a­cy by show­ing the con­text that data exists in. Because with that data, and with con­text around the data, we’ll be able to build understanding—we’ll be able to under­stand com­plex issues, and I think we should all work togeth­er, strive, to build­ing under­stand­ing for every­one around us. No mat­ter what side of the issue they stand on, we need to talk about the same data. We need to talk about the same under­stand­ing that we have of the data. We need to all have the same con­text about the data. 

As inter­ac­tion design­ers we’re real­ly empow­ered to make it easy. We’ve man­aged to har­ness the com­plex­i­ty of tech­nol­o­gy and make it avail­able to any­one. Let’s all har­ness the pow­er of num­bers for us to dri­ve bet­ter deci­sion­mak­ing, and mak­ing those num­bers eas­i­er to under­stand so that we can inspire the right action that helps us live in a bet­ter world that is less biased, more knowl­edge­able, and yields out­comes for all. Thank you.