I have really enjoyed talking with some members of the Capital One Digital Team this month, and what better way to wrap up the month than to talk about the woman who helped make all this possible -- Alana Washington! As the strategy lead on the data experience design (DXD) team at Capital One, she not only works with engineers on data visualization, but she's also building out a data journalism practice also!
We talked more about Alana's work and she talked about her nontraditional path into tech, her work with organizing UX Week 2018, and the importance of fairness in artificial intelligence and machine learning for people of color. Alana also gave some great information for designers looking to enter the AI/ML space, and gave her predictions on where AI is going into the future. Thank you so much for Alana for all your hard work at Capital One, as well as helping to organize this exciting month of interviews!
Big thanks to Capital One for sponsoring this month of Revision Path.
The Capital One Digital team is a diverse group of people who work together to build great products for the enterprise and to disrupt how people interact with their money, their bank, and their financial lives.
Curious about what they're working on and how they're growing?
Maurice Cherry: All right. Tell us who you are and what you do.
Alana Washington: Hi. I'm Alana Washington, and I am a strategy lead on the data experience design team at Capital One. Data experience design is this awesome intersection of physical experience design and data visualization, and I'm currently building out a data journalism practice here at Capital One.
MC: Data journalism at a bank. That's interesting. Talk about that a little bit.
AW: It's a tall order. One of the really amazing things about Capital One and one of the things that keeps me there, that drew me there literally from interview number one, was that there's an incredible values core to the bank. We have a mission to change banking for good, and I didn't really believe that everybody bought into this value when I was first interviewing, but two years in it's an incredible rubric that people use day to day. Are we doing something that right now is changing banking for good? If it's no, then I've seen people pivot and start to kind of reimagine or reorient their work.
AW: When I think about something like data journalism, our data visualization team kind of got into this rhythm of seeing two types of projects possible. We saw the kind of classic tools and analytics realm helping our machine learning engineers kind of evaluate the efficacy of their models by our data displays, and then we also saw this realm start to emerge where there are these powerful and awesome examples of the way that our technologists are implementing algorithms, et cetera, to help our consumers avoid or check fraud that might be approaching them.
AW: We've done some really cool other stuff in kind of the anti-money laundering space and spaces that I'm not even sure I'm allowed to talk about, but I hope to. That's where the data journalism practice comes in, is that there are these stories of wonderful ... The intersection of humanity and technology that I'm helping to unearth and externalize a bit to show that we are working to change banking for good and to show the wonderful people that are here and the work that they're doing, and also to some degree to help inspire others who are doing the same work at other places.
MC: I like that you're framing it as data journalism as opposed to, say, content marketing, because I feel like those are two separate types of I guess end goals. With content marketing, of course, you want to sort of market what the company does, but at least with this data journalism it's less about Capital One the product and more about the ideals behind Capital One. Is that kind of a good way to put it?
AW: Absolutely. That's actually a really important thing to dig into. This data journalism work, to some degree ... We're in this really great organization. We're part of design called integrated experiences. Our whole mission in this giant bubble that we're in is to find the interconnectivity to continue to seed and unearth the humanity across all of Capital One's offerings and all of Capital One's products and services.
AW: Data journalism is very much, at this moment, kind of this self-directed effort to unearth ... Forge this, the good of other technologists, but also for the fun and surprise and enjoyment of consumers as they want to see what it is that we're working on here to unearth those stories. It is absolutely like so far away from content marketing. It's kind of just using the data visualization and art and data science skills that we have to just seed these stories that are already in play that people might not even realize are affecting their day-to-day lives.
MC: That's fascinating. Is data journalism something new? I guess when I think about the intersection of data and journalism, I'm thinking when people put graphs and charts inside of an article or something. Is this something that started with Capital One? Or is this a fairly new kind of topic?
AW: I would say that there are some amazing ... You're absolutely thinking correctly in thinking of things like The New York Times and The Upshot and the work that they do there, or thinking of the work that Periscopic does out of Portland. Do good with data. We're lucky enough to have Kim Rees, my boss, who founded the data visualization practice here at Capital One, but she was also a co-founder of Periscopic, which also kind of tells you how important this practice of data visualization is to Capital One is that they said, "Kim, we know that you're out there working in this organization that specifically aims to do good with data." They've done some wonderful work, and said, "Come over and bring that same kind of energy to Capital One."
AW: Kim's been working over the past couple of years to stand up this practice and I think that we're now finding at a point where we're ... It's a mammoth organization of 45,000 if not 50,000 associates, and so you can imagine how difficult it is ... Not difficult it is, but there's a learning curve to basically finding out where all the different arms of the company are, what everyone's working on, how their work intersects. We're finally I think at a place where we can start to unearth those stories.
AW: You're right in thinking that data journalism is both ... There's import on the words and on the context and on the annotation around the data visualization as much as there is kind of an anchoring graphic on something that people can explore. Whether that's displayed on a giant screen, which is a potential place for one of our near-future visualizations-to-go, or whether that's on something that we produce via medium that people can jump in and explore themselves. We want the data journalism practice to kind of offer a myriad of media that really allow people to kind of be provoked.
AW: Our physical experience design arm operates in the same way. They recently developed a gallery in our San Francisco space called The Future of Data Privacy of Personal Data, and it exists really to provoke people, to incite them to think through like, what does it actually mean to have data privacy? The hope being that a PM or a data scientist or a business leader goes through and comes out the other end in some way rethinking what they've always thought about data privacy. Data journalism aims to do the same thing no matter what topic it sets forth.
MC: That is fascinating. I really want to learn more about that because for ... I'm thinking just for myself, I love to write and I'm designer, but I also really like data, and so finding something that is sort of the perfect Venn diagram between those three sounds like what data journalism. I'll have to bend your ear a little bit more about that probably after the interview, but-
MC: What is a typical day like for you? You're doing this data journalism. You said you're also a strategy lead on design ... Data experience design team. What's a regular day like at Capital One?
AW: A regular day, we've got a distributed team, so I'm based in San Francisco. We've got an engineer of mine sits in New York and a couple of other folks probably joining her soon in New York. We've got offices in Richmond and Northern Virginia, so we're as much focused on like the connectivity of the team as we are about kind of advancing projects. Any given project at the moment has its own cadence, so a day for me could be starting out with just joining a stand-up for a data product that I'm working on and then could break into some sort of team meeting. Whether it be kind of our broader team of 20-plus people or it could just be a few people chatting about a project internally, I do not have as much heads-down space at the moment as I wish that I had. I have to get savvy with my calendar in 2019.
AW: I'd say a lot of it's done kind of in brainstorming and white boarding sessions that we kind of plot out what the next steps of our work are and kind of who ... I imagine our work is kind of a giant relay race because we have to work between so many different skill sets. We have to work between kind of the data science piece. Right now we're building as I mentioned kind of this installation for this giant screen at our headquarters in Northern Virginia and there's a whole tech stack of Four Wind players and Boon 2 systems and things that I am still very behind on the learning curve of that I have to kind of understand from the more technically-minded people on our team. Kind of what all goes into making this visualization able to be displayed.
AW: Once we go through all of those kind of team check-ins, then people might break apart and I might get a few hours, or a couple of hours, of heads-down time to actually play an illustrator in Excel. Post-it Notes and Sharpies are never too far from me to kind of sketch out what the next part of the story is for whatever it is that we're working on.
MC: Yeah, depending on what it is that you're working on, do you have a specific process across those projects?
AW: Data vis is I think one of the hardest ... One of the hardest challenges about data vis is that I need my machine as much as I need my pencil and paper, especially as I'm early concepting because I need to be able to touch the data as much as I possibly can to kind of evaluate those stories and if what I'm seeing is true and kind of dig into it. I need to be able to do what we call exploratory data analysis, so the ability to kind of like rapidly produce a chart of just kind of this John Tookey methodology of saying that you should be able to infer kind of some base statistics from just the visualization of a data set as opposed to like look at each data point on a large Excel sheet.
AW: It's as much as ... It's as important that I'm able to kind of do that work as I am able to then start to sketch out the context in which this data graphic might live. I think that's always been a little bit of a challenge is knowing when to go back to sketching and step away from the computer and then when to reinvigorate the sketch by going back to the data and confirming we're on a right trajectory.
MC: That just sounds to me fascinating because I love to do stuff with data like that, like going back and forth in that sort of way. Let's take it back. I have done my research in terms of your background and I know that you attended college at UC Davis in California.
AW: Go Aggies. Yeah.
MC: What was your time like there?
AW: It was great. I ... A bit of an overachiever. I did English Literature with a Creative Writing focus and did my final thesis on a collection of poetry that talked about [inaudible 00:10:49] work identity, so I started way back doing that. I did a psychology kind of dual degree, but with a minor in African and African-American studies. UC Davis was an incredible place to go to college. My professors ... It's amazing. I went from a very tiny high school of like 200 and something kids, eighth through 12th grade, so you can imagine how close we were with our teachers-
MC: Oh wow.
AW: To this behemoth university, and I thought it would probably be impersonal and I'd never be able to talk to my professors. That just wasn't the case. I showed up for office hours, I was that person. I always raised my hand in a large auditorium and through that I was able to kind of cut through any sort of impersonality there was. It was a lovely time and so enriching. I was able to do a whole year abroad in London, which for the same cost as it would be to attend here locally at UC Davis. Yeah, it was great.
MC: What was it like once you ... I mean, I'm thinking like back at your early career, what was it like when you first got started? I think what you're mentioning now with data experiences on and data journalism, it feels like it's a far cry maybe from creative writing and psychology in college. What was your early career like?
AW: I'd say that the transition happened ... After undergrad, I joined ... I went to NYU for my Master's of Industrial Organizational Psychology, thinking I would follow that thread of psychology. That type of psychology is really ... I kind of distill it down. It's kind of very long, multi-syllabic name, but all of it is to say that the study of humans and their organizations and organizations and how they organize for their humans. There is ... It kind of spans everything from training and personnel selection all the way through group dynamics, conflict and negotiation. How I got to data is a heavy focus on research methods and statistics.
AW: I went through this psychology program and loved organizational development and had the opportunity to begin my career at HBO, which is incredible, and got to be a part of that team there. I started doing a lot of trainings there, started doing a lot of just educational opportunities for adults. A lot of that is kind of soft skills-based training, right? How do you impart emotional intelligence to a group of adults? HBO, as very organization kind of is centered these days, is kind of their currency of communication is in their PowerPoint Slide.
AW: I started thinking about like, "Well, I know things about adult learning theory and I know how I need to organize this information so that it lands as well as it possibly can for adults." I wanted to edify that knowledge with some kind of designers who work in that sphere, so I started studying Tufty and Duarte and kind of really focused on information design and, how could I clear the path design-wise so that this information would land? As I went down that rabbit hole I realized that I was transitioning to more designer mindset than I was really as excited about kind of the content of what it was that I was teaching anymore.
AW: At that time I knew somebody who was spinning up a data visualization practice. We talked a little bit about methodologies that I would use to approach soft skills and we started to have this exchange of, what happened if I replaced that with data? Now all of a sudden, all of the pain and suffering I went through during my statistics and research methods courses all of a sudden made sense because I could use them as kind of the meat of the things that I was designing. That's how I got formally into data visualization about, oh my gosh, eight years ago-
AW: At this point? Yeah.
MC: From there, went on to Capital One, right?
AW: Exactly, yeah. I spent five years at GKF, which is a global market research firm, which is an incredibly exciting place to be for a data visualization person because all of the meat there is data. I got to see like large-scale segmentations for ginormous-
AW: ... large-scale segmentations for ginormous companies, and I got to see how they whittled down all of their data and kind of all of their consumers into five key segments that they would kind of march an organization to serve, and then I got to see little tiny projects, concept testing between does this packaging look great? Or does this? Or really important work in the UX sphere of does this back of the packaging explain to you how to use your medication? Or, is there a better way that we could word this.
AW: So it was really exciting work for five years. I loved being part of that database team.
MC: Very nice. And speaking of UX, you were instrumental in helping put together the lineup for UX Week 2018. People that are listening, when Alana and I first met, I think this was back in what? 2017, I believe?
MC: Back when we had the old Slack community, rest in peace [crosstalk 00:15:57].
AW: I miss it daily.
MC: I mean, you've been a big supporter and patron of Revision Path, of course which I really appreciate, and I was even able to kind of give some recommendations on some people who should speak at UX Week 2018. How did the event go?
AW: It was awesome. It was a dream to be asked by Jesse James Garrett and the organizing group there to be apart of something that is like such an institution, formally started by the Adaptive Path Group, which is kind of the service design arm of Capital One. It's since been reorged and kind of fully embedded in Capital One at this point. And yeah, it was the 16th year of UX Week this year, so it's a big year. Big shoes to fill, to program, and I was kind of asked to work on the service design track. And yes, Sela was great. She led a wonderful workshop for us. Myself, Jamika, who I believe that you have an interview going with at some point. [inaudible 00:16:56] from Slack, and [Dina Brown inaudible 00:16:59]. We did an inclusive service design workshop which was awesome. We had some great people from the New York City governor's office. The New York City Service Design practice gave a workshop.
AW: And then Liz Jackson who is this incredible designer who talked to us on the main stage as well about designing with disability and did some work there. But my kind of crowning excitement of the year was certainly having Hannah Beachler as keynote of UX Week, who is now, as of last week, the first African American full stop, who has ever been nominated for a production design Oscar for her work on Black Panther.
AW: So it was quite a week.
MC: Wow. So, Hannah Beachler is like on my short list of folks I would love to have on this show. I just wanna put that out there. Yeah, I think I read part of the talk that you had done with her. I think it was transcribed. I read part of that.
AW: Yeah. We got really lucky in that she also was available to have an interview before she came in and talked to all of us. And it was really important ... or it was really exciting for me, so having already kind of laid the ground work of doing this African, African-American studies, and having kind of studies diasporic identity development and what that means. Being a person is half black and half white, I've kind of always wrestled with who am I, and what is my cultural identity. And then also having the tie that binds and that Hannah lives in New Islands and my dad's family is from New Islands, in New Iberia, Louisiana. Kind of what does that kind of feeling of my dad lived in New Iberia 'till he was 18 and then moved with my grandmother to San Jose, California. And so having these like two very culturally different experiences of blackness in the US and seeing kind of how he overtime has talked about that.
AW: Hannah and I got to get into all of that together, which was really exciting. She too grew up in Ohio and then moved to Louisiana so also shares that experience. Which is really exciting and she's incredibly open and for me the idea that a designer would have to create ... this is like firmly in the realm of Afrofuturism, right? Of like choosing to live in a future that has never been architected before. Choosing to be the architect of that future, there's such a weight with making sure that you get that right and correct. But there's also this, for me at least, there's somewhat of this fear of like anything is possible and that's not something that I've ever really let myself believe before. And so it was incredibly exciting to hear her talk about that, and then, this is my last fan girl moment, to even just think about. There were so many interviews that kind of glossed over the fact that she ran a department of 400 people with a budget with embarrassing amount of zeroes behind it. Right?
AW: Like that is CEO type behavior. And how many designers get the opportunity to lead at that level and to execute to the degree that Black Panther was executed was just so inspiring to me.
MC: Wow. Yeah. I would love to have a chance to talk with her about. It's so funny. So like of course we did an episode on the design of Black Panther. This was-
AW: I loved that episode.
MC: Yeah. Back in February of last year, I think, we did that. And then someone who I interviewed fairly recently, Courtney Pinter [inaudible 00:20:31] who is a designer in Zurich, Switzerland. She designs for this company called Givaudan. And they design scents, and flowers and stuff like that. From what I remembered from what she said from our conversation, I believe she said that her half-sister's godmother is Ruth Carter.
MC: Something to that effect. Like it was something like super close and then now you're mentioning Hannah Beachler. I'm like, this has to come together.
AW: I mean Ruth is out there, [inaudible 00:21:02]. Ruth is out there doing the damn thing too. She is also nominated. I mean Ruth, bow down and like she is ... the work that she has done is amazing. I think there was a really great article that came out recently. I think it was in the Time's and it talked about the powerful women that Ryan put in place to lead the departments on this film. And it was incredibly inspiring in general, like, I've been mesmerized the way that people that work with him talk about working with him. And it has been so inspiring the way in which he hires somebody and then says, "You be you. The thing that I hired you for is to be you. And do that authentically. That is how we all win."
AW: And yeah. So, yay to Ruth. Yay to Hannah. What an inspiring film and year it has been.
MC: Yeah. And I mean for people that are listening, I mean they might be familiar with Hanna. I think certainly from UX Week and of course through Black Panther. And with Ruth Carter as well. But like Ruth Carter has been in the game for a long time. Like before Black Panther. I mean, we're talking about Malcolm X. We're talking Amistad. Selma. I know she did a lot of Spike's movies like Chi-Raq. Like some of the recent ones like Chi-Raq, The Sweet Blood of Jesus. I think maybe Old Boy, i think. She did that. But she's been in the game for a long time doing costumes. I think she started with School Days which was filmed mostly at my alma mater at [Morehouse 00:22:32].
AW: Oh, wow.
MC: Yeah. So lot of years in the game. So definitely certainly absolutely very well deserved.
AW: And let's just say the last couple of years, I have had ... I mean, like, I've watched the Oscars maybe with [inaudible 00:22:46] interest. And this year, you better believe. My Oscars party is going to be lit. Like I get so excited to be front row and sharing on these amazing, amazing women. Yeah. Like actually it brought excitement and bigger back to the Oscars for me.
MC: Nice. So outside of this, what are you most excited about at the moment? Is there like a particular kind of subject matter or field that you are really into right now?
AW: Yeah. So, over the last year, probably about spring time of last year, four of our head of the center for machine learning came to my boss. So Kim, who I mentioned earlier of Periscopic, now of Capital One, leading our data visualization or data experience design team. They are really close business partners and really close just partners in general in working. He wanted to start a practice or kind of an initiative around fairness in AI. And, realized that he had engineers who were going to participate in this practice, or kind of this ... these initial steps in scoping. But that we didn't have a human centered design voice in the room and so he asked our team of designers to participate. And Kim kind of knowing how important diversity and inclusion is to me, kind of knowing how important working for my community is for me, invited me to participate.
AW: And so what began as kind of this initial meeting, saying like I don't know how we're going to approach this, but we are going to work on it, grew into kind of co-founding the initiative of fairness in AI for our design practice at Capital One. And so, I've kind of morphed from that since and I'm thinking about how I can use my design skills as a data visualization designer to unpack that. But over the past year, I have spoken, I have been to conferences, I have received knowledge, I looked at [inaudible 00:24:37] matrix algebra, that I thought that I would, and I also had the opportunity to co-program Capital One's first ever internal, we call it a workshop, but it's really an internal conference of like 200 plus people of fairness in AI at Capital One, which is really exciting.
MC: Why is fairness in AI important?
AW: It's incredibly important because we're seeing new news results daily of companies and algorithms that unintentionally or intentionally are predatory on underserved and underprivileged communities. And the idea for me that that would perpetuate is egregious. Especially because in meeting kind of the engineers that I've worked with and in other designers, like we're almost ... or at least maybe at self-selecting that I've met these people. That they are kind of governed by this credo to do no harm. And, I think that in this kind of era of big data, the potential that we would be able to kind of just write an algorithm and train it on a set of data and produce it into the world is just so easily at our fingertips. But we have to kind of start to think about the process and think kind of eight steps ahead of ourselves to think, well, what are the ramifications of the data that is missing or that is absent from my training set that will produce an algorithm that might affect people disproportionate, or set them at an advantage?
AW: I think that's obviously very incredibly important in the financial sector. It's incredibly important across all sectors from recruiting to policing in every which way. And so, it's just been so important to me to understand one, kind of [inaudible 00:26:18] close that technical understanding gap. I think as a designer, I always have had this imposter syndrome of, especially working with the PhD level researchers that I did at GFK. You know, how much do I know about research? How much can I speak their language so that they'll invite me into the design process more? And help me and I can help them and we can produce the best thing for our customers. The same is true now working with machine learning engineers and data scientists. How can I demonstrate that I have a grasp and an understanding of how they work and what they work on such that they will invite me into their process and then I can be that human centered design voice of reason throughout.
MC: Let's get to that a little more because perhaps some people think of AI, I think it's very technical. There's a lot of code. You need to have a very sort of high understanding of the subject. And I know that we mostly have designers that listen to this show and this might be something ... this might be a concept that turns them off because they don't necessarily have a technical background. How can designers get involved with understanding AI and entering that space?
AW: So, there are amazing resources out there for you. I don't even care if it's like a five minute YouTube that just explains what is machine learning. Or I found some machine learning flashcards that I printed out from the Internet to help kind of collapse my [inaudible 00:27:38] productivity of understanding it. Walking into rooms, if you're at a conference, I've been to a couple of conferences where like, there's like the design track and then there might be a tech track. And then there might be a math track. And I've liked forced myself to go to the math sessions and realize there's more conversation happening in those math sessions than I realized that there was. And just kind of like building an awareness by proximity to the content, I think has been really helpful. And then just like there's some few ... there's just a couple of basic [inaudible 00:28:08] of lessons in terms of what a model is set out to do.
AW: Like what a neural network is. And just having those base line definitions or understandings will help in the long run in terms of understanding how to design for that tech. And what I would also say is this community of practitioners working, especially in the fairness space, but in machine learning in general, they are incredibly willing and open to answer any questions. But I think the worst thing that you can do is kind of not ask a question and assume, because that assumption ... that assumptive kind of disposition, I think, turns people off. But if you walk in and you just say like, "Hey, can you explain a neural network to me?" Or, "Can you explain how AlphaGo was able to do what it did?" Then you get people like leading forward and directly engaged with you that will help you understand.
MC: Nice. Now I think that's good to know certainly for people that are looking not just to get into this field, but maybe just design or tech in general. Don't go in with any assumptions. Go in open and willing to learn.
AW: Absolutely. And, they are excited to have the design support. So at least kind of, in my most recent experience, there is this push to figure out AI and there's this kind of giant store of data that makes that possible. But then that also means that people are working kind of heads down under the constraints of like whatever business metric it is that they need to maximize or optimize for. And so, the minute that a designer walks into the room, and kind of helps unmask or kind of un-net on the context in which this algorithm might live or the context in which this business product might live, I see everybody relax quite a bit. So there's something for designers to also provide and space to kind of think about the what ifs and the why's that actually I see everybody excited about. So you also-
AW: ... that actually I see everybody excited about, so you also have something to offer as a designer, which is great. You're not just taking-
MC: Yeah, no, that's really good to know. I would be so torn going to a conference that had a math track, a design track, and a data track.
AW: I know.
MC: My degree is in math.
AW: I know.
MC: I would be completely torn between that sort of stuff. Have you heard of this conference called Black in AI?
AW: Yes, and I actually very, very, very luckily got to meet Timnit earlier this week, introduced by a mutual friend in the Google AI Practice, but yeah, Black in AI does a wonderful workshop at NIPS, which is kind of the yearly program that's, or pardon me, the yearly conference that's run. There's also a fairness, accountability and transparency conference that's actually coming up this week, I believe in Atlanta. They're one, an awesome group to belong to in general. There's a Google group and tons of resources, tons of just opportunities are surfaced through that group, but then also their participation yearly at NIPS is great.
MC: Yeah. We'll put a link to that in the show notes so people can check it out. I know they've done the event now for two years. It was in Long Beach the first year, it was in Montreal last year and they haven't announced anything yet for 2019, the year is still young so I'll put a link so people can check that out.
AW: Awesome. I'd also recommend people check out one of the organizers was also on a panel at AI Now, which just happened recently and that is a phenomenal, it was about two or three hours long, kind of symposium at NYU, I believe, in New York, and very much recommend that people watch that. Kind of all of the people that I follow and like to hear from are definitely in that space. Kate Crawford and Timnit was there and Virginia Eubanks and more, and then there is also Data 4 Black Lives which is a conference that just happened at MIT and all of those videos.
AW: I know it's so hard in our daily lives to like pull aside and watch a YouTube video of a conference, but you get so much from it. So highly recommend looking at both of those recordings and then hopefully we can all meet up at the next sessions next year.
MC: Yeah, I had my eye on the Data 4 Black Lives live stream just to kind of check in on it. You know, I have to say there's some really, and I have to give it up to to Cambridge for this because it's probably the last city I would think that this kind of stuff would go on to be quite honest with you, but like between Black in AI and the Black in Design conference, there's something happening in Cambridge there where they are really kind of having some next-level conversations about how black people are involved in the tech and design spaces and not just in terms of design, not necessarily product design, but like urban designing, service design, things of that nature. I know Black in Design 2019 is going to be coming up later on this year and I'm really excited to go back to that.
AW: I will see you all there.
MC: All right, yeah.
AW: I still have chills thinking about, was it 2017? I in general had never been to a conference that looked the way it did, like looking out into the audience, which also let me tell you, like allies and accomplices, you are invited, you are welcome and what a beautifully inclusive, lovely venue and space that was. And then also just to attend a conference where your whole heart and your mind were both served up with delight, right? Like you got to do and we got to hear from Leslie Salmon Jones who was a former Alvin Ailey dancer, started Afro Flow Yoga. She led us through some dance and through some yoga together and then we would hear from, was it Roger Bonair-Agard who did spoken word and then we heard from like DeRay Mckesson.
AW: There was this wonderful tapestry of folks that, like, by the end of the weekend you couldn't not be moved. It was art that affects all of us, and so, very much, yeah, I have allies and accomplices who afterwards said, like, will we be welcome, and I said I will buy you your ticket for next time. Like it is important that we all go, yeah.
MC: And the tickets are cheap, gotta say that.
AW: Right, right?
MC: Most of these conference you go to where the tickets are like $500 and up, I think Black in Design was less than a hundred. It was very affordable.
AW: Yes, yes, and they take care of you. Like, I was so impressed by the food, by just the atmosphere in general. Yeah, you're right, though. Something special is happening in that Cambridge area. I'm a little jealous to live so far away from it, but I'll come visit.
MC: Yeah. Shout out to the African American Student Union there at the Harvard Graduate School of Design because they are really doing, I mean for the Black in Design conference, certainly they're doing something and even what's going on with the Data 4 Black Lives event. Yeah, there's really some great conversations going on in those two events that hopefully people get a chance to kind of learn more about.
MC: Where do you see AI going into the future?
AW: I think unchecked. I think that unfortunately the boundaries are limitless. One of the things I've been talking about in the past year is that humans are not evolving, are not co-evolving with tech at tech speeds and so one of the things that I would hope or kind of encourage organizations to do is have a growth strategy that is mindful around AI. Just because we can entrench people's lives with AI and kind of like seamlessly integrate them into this like Westworld-like experience, we don't have to. We can grow slowly and we can grow mindfully and I think that we are more entrenched already than we realize that we are.
AW: So from banking, from the way that we order a car via Lyft or Uber, the way that we look at our email, what's foregrounded, what's below the line, kind of what's deemed unimportant. All of that is AI at work-
MC: Smart speakers, too, and voice assistants, yeah.
AW: Smart speakers, too, absolutely the way that we kind of get set up with doctor's appointments or even the treatments that they provide us or are recommended to us. All of that is embedded within AI. My hope is that companies now are kind of looking at each other and asking each other to behave responsibly in this space and my hope is that the consumer gets even savvier about knowing what to demand and what to expect of the technology that's provided to them.
AW: I think that any sort of laziness will just continue to allow technology to prey upon us if we say like, oh, I'm sure somebody else has thought of that, because most times either somebody else has not thought of that or there's not kind of a fever pitch or a quorum of people that are able to kind of get the brakes to stop before a tech is implemented.
AW: The future of AI could either be incredibly bleak or it could be incredibly exciting. I'm kind of split right now in deciding how jaded I'm going to be about the future.
MC: Feels like we're talking like, I want to say it feels like we're talking in a science fiction movie, but this is reality right now.
AW: Right? Yeah, I think one thing that is inspiring, especially kind of just going back to Hannah Beachler, right, is kind of applying this notion of Afrofuturism to AI and the idea that, you know, the training data that algorithms are being trained on, especially let's take for instance Amazon Prime, when it originally launched its Same-Day feature, it was quickly noted, I think this piece was produced in Bloomberg and it kind of reported on the tech that, you know, you could map those initial same-day delivery offerings with redlining maps and I don't believe that that team went out and said let's reproduce red lining in the way that we offer our same-day delivery service.
AW: Like, I'm going to hope and believe optimistically that that didn't happen. But what that tells you is that ZIP Code is an incredibly powerful, historical, confounding variable. What that also tells me then is that there's power in this idea of Afrofuturism and this idea of building a future that has never before been imagined and then building the technology to meet that future. Because, especially in this country, so many of those confounding variables exist and can creep in, whether it's a group of images that an algorithm can be trained on and just kind of what's in those images, what's available, is it more suburban landscapes or are there some urban landscapes and what are the shades of faces that are in that collection of training data, you know, to ZIP Codes to continue on down the line.
AW: Well, my hope is that we get a little bit more futurism, we get a little bit more unbridled, creative dreaming about what's possible, about what begets equity and justice and joy for all, and then we work backwards from there because I think starting where we are today is just going to continue to replicate what has already existed, which for us is just a long, storied hundreds of years' history of not-so-wonderful treatment, to say the least.
MC: When you look back at your career, what do you wish you would've known when you first started?
AW: I wish I would've known, I think, that every decision that I make is not so monumental. I think that I definitely was kind of shrouded in this fear to make sure that I get things right. I think that goes with my, I'm just incredibly risk averse. I think I had one manager one time tell me that, like, you know, you had a strong year. Next year, let's exercise 10% less caution. I had another friend who once told me or I think in a story about somebody else said like it's okay to take up space and that I think is something that I just have not so freely done is taken up space and exercised less caution.
AW: So my hope would be in the future that I do more of that and retroactively I wish that I had done that a bit more and made a bit more waves and spoken up for what I knew was true or correct kind of along the way instead of just kind of acquiescing or kind of like reframing the problem statements such that I would be more comfortable as opposed to actually believing in the problem statement and working to correct what was happening, whether that be at like changing jobs or whether that be on a project level or kind of working directly with a client and knowing something that was better for them in the long run. You know, I can apply that at any degree, at any scope.
MC: Mm-hmm (affirmative). What's next for you? Like you're speaking at a conference coming up soon, right?
AW: Yeah. So I'll be at ConveyUX in March and I, in the meantime, continuing to build out this data journalism practice and hire up for a team. We're building out a visualization for an installation at the headquarters of our company in Northern Virginia, which is exciting. I've never, as I kind of mentioned earlier, like never built a data visualization for a stream of this caliber, and so some part of the learning curve is understanding like how do you build visualizations for streaming data. That has been something that we've not yet cracked, and I'm really excited to crack that, just in my own nerdery and then continuing to just think about like how can I use this data visualization or data journalism practice to kind of use my craft of data vis in that fairness space, how can I build things that continue to unearth bias or continue to show how we have worked to not let bias creep into our algorithms, to continue to use data journalism to think about bold, beautiful, equitable futures that have not yet existed in current data or you know, what might possibly be.
AW: That's what's next for me. So it's decidedly the most artistic and the most, like, open sky that I've ever worked, which is exciting and is also terrifying because I don't fashion myself to be an artist, but I'm also really excited for the challenge and really excited for this space that I'm lucky enough to have had Capital One create for me to do this work. Like, when I tell people the work that I do and then I tell them that I work at a bank, they're so confused, like you do what now? Like, where do you work? Like, oh, you get paid to do that? Yeah, it's great.
MC: Nice. Well, just to wrap things up here, where can our audience find out more about you and about your work online?
AW: So I am @AlanaWashington on Twitter. I'm on LinkedIn. You can find me there. I'm a happy mentor, happily would love to coach anybody that ever has questions or what have you. I also am a very excited collaborator so also reach out to me there if you have something that you want to noodle on design wise together, time permitting. I have a portfolio site that is in desperate need of rehabilitation but I've just not had time in the last couple of years to do it, at AlanaJWashington.com but you can find me there as well.
MC: Sounds good. Well, Alana Washington, I want to thank you so, so much for coming on the show and I have to say, you know, this is coming at the end of a month of great interviews with black women at Capital One and you were the one that helped make all of this possible. So I want to thank you, of course, for that, the great voices we've heard from this month, from Jamika, from Belindah, from Arneice, and then of course from you as well. I think even just with everything you're talking about, whether it's the data experience design, the data journalism or even, you know, the fairness of AI, I really love how you're using some of this combination of design and data to really make a better future for all of us. And I mean, I'm really excited to see what comes out of this, like, what are the possibilities that we need to know about because of this sort of research and information. So thank you so much for coming on the show. I appreciate it.
AW: Thank you so much for having me, and it's been an honor to bring some of our wonderful designers, my friends, my mentors, my family here at Capital One to the podcast for the last month, and thank you for the opportunity that you provide all of us to lift all of us up. I really appreciate it.