 Yeah, I think tech Hawaii think tech tech talks. And today we're going to talk about the data divide and what we can do about it. And we have Jillian Daibald. She's in Washington. She's a policy analyst. And we like both of those things we like policy. And we like analysis. And she's at the center for data innovation. And she moderated a webinar in Washington a few days ago about the importance of the data divide and what we can do about it, what we should do about it, what policy makers should do about it. So we are so delighted to have you on the show, Jillian. Thank you for being. Thank you so much for having me. So what is the, what is the center for data innovation. Tell us about the organization what it does and how far it reaches. Yeah, so, like they said, we're based in Washington DC center for data innovation is part of the information technology and innovation foundation it is a mouthful. And we're a nonpartisan nonprofit think tank and we studied the intersection of data technology and public policy. And so part of our work involves educating policymakers and public like I'm doing now about the opportunities and challenges associated with data, as well as technology trends relating to artificial intelligence and open data and the internet of things. And so I'm pretty early in my career and I joined the center for data innovation a little over a year ago and I leave my teams work on digital inequalities. And I'm going to have also done some work on web scraping and AI and open data issues and I'm especially interested in data for social good so all of those good issues is kind of what we cover here at the center. I got it all except for the web scraping what's that what's web scraping you know it's when if you're a researcher and you need to get data from a publicly available website so you want. You know, a data a giant data set of like all tweets on a subject and you know that could be hundreds of thousands of data points. It's sort of an automated program that lets you collect all that data at once. Like a crawler. Little bit. It's kind of controversial but not controversial I think it's journalists researchers love it companies don't love it it's complicated. It's why it's a good one to look at you know what are the challenges that you know that you see that you deal with I mean imagine. You know of all the things that happen in the world of data there are plenty of them. And you have you know before you determine policy and you know advocate for one policy or another you have to identify the problems. So I know we only have half an hour here, we could spend a lot of time on this but can you give us some of the primary problems in, in data in data divide here in the United States well in the world today. Yeah, definitely I mean I think it's easiest to start kind of with just the definition right so the data divide is a term coined by the center actually before I started there about 10 years ago. It's been kind of we're bringing this issue back to light but it's the social and economic inequalities that result from a lack of collection of data and use of data by certain individuals and communities. So, even though right now, advances in technology have made it cheaper and easier than than ever to collect and use data some groups still lack really high quality data that they can put to productive use about themselves and that kind of hinders everything you know it hinders data driven innovation and it really impacts everything from their health outcomes to public safety to economic growth. Wow, so it's very important to understand the world around us I guess that's what I get out of it. And you these days in the complexity of the 21st century, we cannot understand the world around us without without having the data. What distinguishes somebody who's on the right side of the divide. And I don't mean that politically, maybe I do, who's on the right side of the divide and who's on the wrong side of the divide. How can you tell the difference be somebody who is who has it and doesn't. Yeah, I mean I think it's, it's the key question and it's really whether it comes down to whether data driven services work for you and you kind of feel confident that they are working for you and or whether they don't. So probably the best way to do that is to just go right into an example. So with financial services you know I would say you and I have maybe I'm assuming we have credit cards and the credit agencies, but not everyone does and that's sort of that's a divide in itself right. Whether you have access to credit or not and that comes down to data so the credit agencies they have they're built on certain systems that you know underweight loans and whatever. And they, they need certain data that allows them to collect and score individuals and that's usually based on so called traditional items about you know financial borrowing and your repayment loan repayment history. But those same systems are not equipped to take in so called alternative data sources so they leave out things about your on time rental payments so they really privilege home ownership. Or they leave out your utility payments your cell phone bills and your cash flow in your bank account and so as a result. You know a lot of consumers are kind of left unbanked or you know left with unnecessarily low or really inaccurate inaccurate sports so you know that's an obvious one you know whether or not you have access to a credit score that you believe actually represents your credit worthiness or whatever you want to say, and I think these kinds of examples really pop up in so many different facets of life. So I asked you this before you told me you're writing a paper about it. What's the difference between the digital divide and the data divide. Let me make a guess before you start. You know, here in Hawaii we have areas that are poor and rural, and nobody in the given neighborhood has a computer even. They don't have broadband. In fact, we have a state broadband strategy officer whose job it is and try to get broadband to areas that are disadvantaged. Is it digital divide or am I missing the book. No that's that that's the digital divide and I think like where policy, at least in Washington is now is that the digital divide really just pertains to internet to the broadband. So it's all worried about broadband deployment and adoption. We kind of I mean the term obviously builds off that it's using the same the same alliteration whatever data divide digital divide, and the data divide like I said is really just instead of do you have access and can you put broadband to use to have data. And so it's sort of taking a step back and just looking at the fundamental, you know, building blocks of information and say what information do you have about yourself it's not necessarily about can you get connected to the internet you know where the focus has been on that for the last year and it's reaching pretty high levels of adoption, obviously in the four rural areas you're speaking about there's still work to be done but you know I think now the thing to worry about is you know we're in the internet economy that that's agreed upon and we're kind of moving into this data economy so to speak where data is sort of the new, you know currency in some ways, although I don't love that analogy. It allows you to do a lot of things and to participate in the economy and so when there's that that that divide, you don't have the data about yourself. You're kind of going to be left behind in that and so that's kind of why we built off that digital divide terminology in this report and so the next one is going to be exploring you know what solutions. Do we have for the digital divide and what what's out there right now what kind of programs are we are we doing. What's the government doing and sort of. I'm going to be saying you know what should we be doing and do those solutions work for the data divide. The answer spoiler alert is sort of no. And sort of what do the solutions look like what does policy solutions look like for the data divide. We can't just say oh because they sound the same digital divide solutions work for data divide they kind of require a little bit different, different different thought pattern. Let's assume for a minute that I don't have a digital divide problem that I that I can get the broadband I can put a browser up. And I can you know I my own experiences I can learn anything I want really anything with a browser and broadband anything. That's because of Google. I hate to mention a commercial name but that is exactly because of Google. So why can't I, as a person on the wrong side of the data divide, get on the right side of the data divide just learning just Googling everything just finding YouTube is another commercial name YouTube videos or Vimeo videos or some kind of information on my system and educating myself. What is stopping me from doing that and if I do that, won't I be able to cross. Yeah I say that I've been waiting to say this to you. May I cross the divide. I think it's a good question but I think what it is is that you know, yeah you can Google anything but can you Google actual information about yourself, can you Google things about what's your possible your personal health outcome if x, y and z or you know where should you go to school based on your specific educational history. I think those are the kinds of things that it's a lot harder to bridge. The internet doesn't really just let us bridge that you can't just Google personal information about yourself and see patterns over time and so that's really what the data divide is and why it's a little more than just having access to information although obviously I want that for everyone and everyone should have that. There's more about, you know, information about yourself here is the real the real problem and what that's what that's what these communities and people I mean you and I probably there's divides here. That's what people are lacking is is sort of information about themselves as it pertains to health education financial services the environment. So if you're a certain gender or race or whatever all those things it's you can't find that information if you don't have enough data collected about you, you won't be able to see that kind of thing about yourself. Are you including communities. It sounds like you are in other words. I'm a redhead. I'm not a redhead I was never a redhead but I'm a redhead and I want to connect up with other red heads. Are you wrapping around the whole notion of social media and finding people of like mind like interest like persuasion is that part of being on the right side of the data divide. Yeah I think it's especially about information about that community so yeah we'll use the redhead example you know it's you can be connected to them by internet but again do you have information about people with that specific story and your outcome in X Y and Z so it's so yeah I mean it really is we look at it I think it's it's most important sort of at the community level because that's you know where a lot of friends happen and that's kind of where that's a data collection is done especially when you want to preserve privacy you know you have to look at it at the group level. So yeah I think that I think that thinking is sort of right. So you know the problem I see is that you know you suppose you cross over and you learn about programs that can help you and those programs help you take better care of yourself those programs help you learn about the possibilities that you can employ to become more successful more more wealthy in the community do more have more learn more all that but but here's the big but this is a moving target just as you and I speak you know there's hundreds of thousands of programmers out there and entrepreneurs and companies that are inventing new software to give new leverage new justice to bring that term into play. And so how can I possibly keep track of that how can you keep track of that how can we collectively achieve that kind of justice when it's moving all the time. I think I think it's a great point I think it's just that we really need to raise the baseline and that kind of is where I bring in the data equity term of that you know it's not necessarily I at least me I don't think the goal should be every single person has a smart watch. It's more that every single person has an X standard of data collection about them and so that you know obviously that's something that policymakers really need to prioritize and you know we need to be able to ask them to do so effectively about you know what are the key areas where those those data gaps really persist because like you said, yeah new things are coming up every day and it's not necessarily important that every single person can keep up with every individual fitness tracker, but it is really important that you know every hospital system is held to the same standard of you know data access or data sharing so that when you move to one or the other you can choose where it goes and you know whether whether your doctor has access to it that kind of thing so I think it's a tricky question obviously but I think it's the system level is sort of we need to worry about the systems rather than maybe the individual devices here you know I think we need to think about healthcare as an umbrella system or education as the system and so raising the baselines of those will kind of allow us to allow it to be dynamic and sort of just keep moving in the right direction rather than kind of getting bogged down and oh no they missed this one this one device or whatever. So okay justice just to dwell on that for a moment. What's how do you fit justice into all of this. What is justice in the context of the data divide. What do you seek what do I want. What is a better community and and how do we achieve justice. It's a is it a legal thing philosophical thing, a technological thing what is it. I mean I think it's a good question again I think it's, it's a technological thing and it's again ensuring and and you as an individual you know believing that these data collection systems are to your benefit so it sort of brings in the trust issue to me is really you know trusting that these data driven technologies they work for you and you know I think there's been a lot of you know concern or hostility around some of them because you know people are seeing there's you know when there's incomplete inputs you know it's it's coming out with with inaccurate outputs and that applies to so many different systems right now but I think that it's that creates you know the trust problem of. I don't believe that that system is actually working to my benefit it's actually harming me and you know that might be happening right now so I think. Where justice comes in or it's really the equity concern comes in is that we're raising the baseline for everyone so everyone can kind of feel a lot better about. You know, how these services are working and how this kind of decision making is working to someone's advantage or you know to believe that it is doing so called a fair decision making. Well you've opened a really really interesting connection between you know our life in these times and the whole notion of data equity. And I just, you know, the word trust sticks in my brain trust, we have such trouble these days in trusting information trusting fact trusting conspiracy theories if you like. Trusting you know trusting lies. And I mean that's just all over the media is all over our social cultural world right now. And doesn't that infect the problem for you, or those who would follow and analyze the data divide, you know it doesn't mean anything if you don't trust it, and we have a trust crisis. What what is your thought about that. I think it's like the key policy problem to solve right, and it applies in so many ways but I think in in tech policy specifically it kind of relates to this this house the tech lash or whatever. You want to call it the hostility towards big tech and all of that, and really just technology as a whole it doesn't necessarily have to be big part of the science sentiment in the country. You know, in a sense at least at least part of it is yeah. And so I think the way trust comes in here how you solve it again is sort of. It's only until we really improve these systems and by improve these systems I mean improve their input. So I trust that what is going into a system that's making a decision about whether I get credit or not I trust that that information is accurate complete. And actually represents who I am or what I what I do and whatnot. And I think that is kind of what's missing here that's the dated five really in its essence so it's sort of, you know but closing it that's the solution really is reducing it because reducing it will sort of naturally, at least I theorize it'll naturally kind of improve trust overall and at least sort of ameliorate some of the hostilities it's not going to solve you know the greater hardest and trust problem that we have in the United States that's not at all what I'm saying but I do think it'll sort of reduce some of the hostilities towards technology as a whole and I think that's really important. It is I mean to move forward we have to trust it. We have to trust this huge system on which we live. So, I want to talk for a little bit about your webinar in Washington a few days ago, where you moderated no easy task on subjects so complex. Can you give us a thumbnail of what it was like of who is there subjects covered. You don't have to be very specific. I don't want you to borrow is no boring we don't do boring, but could you tell us how that went. It was great. I think the highlight was really that we had us chief data scientist Denise Ross, give a keynote speech and so I think that again was obviously the highlight of it. Denise is working works with the Office of Science and Technology Policy in the White House, and her, her remarks kind of just surrounded all these good issues about the data divide and really went into the concept of kind of equitable data engagement and all of that which is, it's sort of when in the report I'm recommending you know policymakers need to prioritize this through concrete federal action I think the work of OSTP Office of Science and Technology Policy is, is really doing just that and that's kind of what she was speaking about she was speaking about all these opportunities for the public to actually participate and say I'm missing, or I am from X background and there's not data in X agency about me. She opened up a request for information on that kind of subject, which I think is really exciting and that's kind of what she went into was you know, again, sort of just shed it, the way more data will kind of shed light on issues that we didn't even know about you know it's not necessarily, or it's not due to it's not on purpose that some federal agencies are missing information about some groups. And it's sort of a fact in the end what happens with data collection, especially at such a massive scale is the federal statistical system is that they don't even know where the gaps are so I think she was talking about how it's all the opportunities kind of to the contribute and say I'm directly affected or my community is directly affected or my school district is that kind of thing and I think it was it was a really great speech to kind of again touched on all the issues that I wanted to hear about and then in the final. And we see it is your webinar available for us. It is it's available on on our website data innovation.org under events, or on YouTube under the same channel name. Let's talk about data analysis. What I mean is, you know, you can have all the data in the world but the end of the day, it can make so much more of it. If you have tools to analyze it. And that of course includes AI doesn't she talk about that. She definitely talked about the need, I mean high quality so raising the standard across the she was specifically talking about federal data, raising the standard there across the board and then she talked about. And I think it's a really key theme that I'm thinking about OSTP is definitely thinking about about disaggregation so you know, you have this giant pool of data but can you split it up specifically by race gender age income, whatever other kind of like what you want. And can you cross, can you cross this out, you know, can you look at it in two ways, you know, can you see just white women or whatever. So she she yeah she spoke about that and she spoke about that specific part again as a focus right now. It sort of seems obvious I think maybe maybe not, but it seems obvious you know our census data would be disaggregated by all this and it is largely but you know when you look at some more specific agencies that might relate to public health or something like that. It's often not split and or maybe it's just split by two races instead of you know however many there should be. So I think there's some it's sort of sometimes egregious examples actually and that's that's another big focus of the current administration and so she definitely touched on the disaggregation aspect of analysis. So what else was there in terms of the speakers the panel. Who did you have to moderate. Yeah, we had Chris would he's the executive director of LGBT tech so they kind of focus on all the good tech policy issues like data equity and things like that but from the specifically LGBT angle. I mean, Dominique Harrison who heads. I'm going to forget her full title but she's at city ventures innovation and she's kind of heading their diversity equity inclusion as it pertains to data research. And we had a program manager from Microsoft who works on their accessibility team. You wanted to not say and then lastly I had Dr Tracy Morris, who is from the director of the American Indian Policy Institute so she works on different things and tech policy issues, especially broadband as it relates to Native Americans and Native American access and inclusion. So I mean they, they all brought, I think really interesting perspectives and kind of different I think they agreed on more than maybe I thought they all would, which, which is a good thing you know I mean everyone is sort of we're seeing this issue and I think what's interesting is that on the data divide issue there's actually a lot of consensus surrounding the issue but no one's necessarily talking about it in the exact same terminology so kind of the term data divide itself provides like one way to unite all these groups who are worried about all these issues, especially from the diversity equity and inclusion angle to sort of just all put all the efforts into one, you know one stream line which is great. So that's valuable, you know just to get the nomenclature down together and have agreement on the nomenclature is a valuable result. What else, who else, what else did you do. Breakout sessions that you have group discussions that you have surveys and polls and Q&A. I bet you're asking for a lot from a from an hour long webinar. You know that we had so we had this the keynote speech and then the panelists had about 45 minutes of discussion and audience questions and I think the audience questions were really interesting because they actually wanted to take the issue which my panelists are all from the US and a lot about the US context but the audience was super interested and you know how do we think about this issue on the global scale especially when the issues are global like climate change or things like that so I think we got into that a little bit at the end and as a shameless plug I think I will having an event kind of taking it to the to the international level in a few months. I'm glad to hear that. That's a good direction. Yeah, looking at how the, you know, international institutions are kind of dealing with it or thinking about the issue or not. So that's exciting. Well, you talked earlier about, you know, finding, you know, the issues around the divide, and then discussing policy and indeed you are a policy analyst yourself. And that is, Billy and DiBone a policy analyst. So what came out of this webinar in terms of policy. And when I say policy I mean not just conceptually what should be done but exactly what should be done where. I think it is what Denise in her speech talked about there, at least to if not more open requests for comments right now which means policy analysts like me are going to be I mean I am currently writing comments, but also members of the public should be writing it and saying this is how this specific lack of data is really affecting me so I think that's where policy is at right now and obviously you know it's sort of slow moving but in certain issues of the bit I mean obviously this is a massive umbrella issue I think we're seeing some progress so especially within LGBT data and inclusion there's been some legislative action, kind of on an enshrining data collection about that community. Likewise with Native American data actually and I think there's kind of it's currently kind of on a piece by piece basis of you know x demographic or location I think there has been some movement around rural data as well, but right now again it's it's sort of about the Biden administration once as much information as they can get so it's on it's kind of on the public it's on the it's on the policy world it's on everyone to sort of contribute that information that's that's where we're at. How do you distinguish between data, you know that is, you know, for example there was a piece of the paper this morning about Ed Snowden, and Vladimir Putin has made him a citizen of Russia. You know that there's this that's troubling in some ways because he had a lot of data in his mind. He took away all kinds of classified data. And of course we've seen issues around classified data in Mar-a-Lago and all that, you know, big deal. But how do you distinguish between data that should be public that should be available to people and data that should not. That's the key question and I know we're kind of running running out of time but the key question here is data already has strong de identification protocols that's collected by the federal government so I think that in my opinion, most data should be open and the view of the federal government as well is that the more information available for researchers for you and I the better. That's my general philosophy and obviously I think it's a controversial one but we can kind of we can debate that all day. Well, let me go a step further. Let's say let's say that that that philosophy is the enlightened philosophy, and I certainly agree with you. What how is society, the human experience, the world, referring to your next webinar. How does that improve the world, how does it improve the quality of life, the quality of governing and self governing the quality of the economy and, and, you know, and all those things that come with a more enlightened life. What are you seeking in terms of connecting the data, you know how being on the right side of the data divide and a better world. What is that world like because of the data. I think it's a more equitable world so you know these systems are including many more people and you know maybe the communities that kind of stand to benefit the most so it's. Trusting that the medicine you're receiving is the correct medicine for you and not only is it the correct medicine for you but it will work the best for you for you know it will really you know more data will really enhance our ability to sort of come up with new mitigation or adaptation strategies for climate change it's it's all things like that I think it'll with more data it will both empower individuals so that we'll have this more participatory relationship with with government or with whatever institution because you are empowered to say, I know that this is happening to me and I have concrete evidence in the form of data. I think that's another thing that you'd see. So you know, the sort of data driven world it's really important that it isn't kind of including as many people as possible and I think that it will include with more data collection we will be able to include more people and sort of, again, it kind of to loosely to that trust issue it's more people also feel feel better about the data that's being collected about them and the fact that it is being collected about them because it'll sort of see the benefits to it first hand. Yeah, you talk about the number of people and I'm wondering, what's what's what's your reach. I mean, how many members or followers, what have you are following the Center for data innovation. How many people showed up in the webinar. And what do you see in terms of increasing that reach going forward, especially with your notion of expanding, you know your research analysis and policy beyond the United States and into a global sphere. Yeah, I mean I think it's a great question I think the webinar, well, I'll preface it by saying the policy world, everyone. You asked me this off camera but you know everyone's kind of a nerd about their specific issues so I think that's that's important to preface but you know the webinar had about 300 attendees which is actually, we were really happy with that fact and I think it kind of is because it includes this issue the data divide includes so many different umbrellas so you might just do climate policy up the data divide applies to you you might just do data analysis as a raw discipline that applies to you only want to work on healthcare this applies to you so we were able to kind of bring in so many different policy viewpoints and and these these are the webinars we host at the Center for data innovation. They're open to the public, I would say but they're largely targeted at the policy community so we're all kind of getting on the same page and learning from each other about these topics so you get around 300 people and I think when it comes to the center of health, you know we have a newsletter and social media accounts that reaches many many more I, I wish I knew the exact amount of my head but I don't. You know, thousands of people honestly and so I think there is, and again there's so much potential with this issue to really raise the phrase awareness about it, just because it encompasses so many different facets of policy, daily life, whatever you want to say. I have a one C3 nonprofit exempt organization, supported I suppose by a wide swath of members all around. Who helped you, who helped you operate that's great. So one more time your website. It is data innovation.org and you can find the report on there under report. Thank you Jillian Jillian dive all the, the moderator of this webinar and sounds terrific I'm sorry I wasn't there but I'll try to be at the next one, and I'll try to circle back with you, so we can have a similar discussion about the next one. I hope so. Yeah, thanks. Thank you Jillian and great talking to you. Thank you. Thank you so much for joining us on Facebook, Instagram, Twitter and LinkedIn and donate to us at thinktecawaii.com. Mahalo.