Speaker 1: Yeah. Welcome to How to Citizen with Baritune Day, a
Speaker 1: podcast that reimagined citizen as a verb, not a legal status.
Speaker 1: This season is all about tech and how it can
Speaker 1: bring us together instead of tearing us apart. We're bringing
Speaker 1: you the people using technology for so much more than
Speaker 1: revenue and user growth. They're using it to help us citizen.
Speaker 1: I have been working over the past year to try
Speaker 1: to integrate my own thinking around technology, and last year
Speaker 1: I wrote a bit of a manifesto. Back in I
Speaker 1: was invited to speak at Google IOH, an annual developer
Speaker 1: conference held by Google. They wanted me to share my
Speaker 1: thoughts on what the future of technology could look like.
Speaker 1: I went on a journey to try to understand how
Speaker 1: all my data existed amongst the major platforms, amongst app developers,
Speaker 1: and what came out of that was a set of
Speaker 1: principles to help guide us more conscientiously into the future. Now.
Speaker 1: The first principle of my manifesto is all about transparency.
Speaker 1: Like I wanted to understand what was going on inside
Speaker 1: the apps, behind the websites I was spending all my
Speaker 1: time on. When I want to know what's in my food,
Speaker 1: I don't drag a chemistry set to the grocery store
Speaker 1: and inspect every item point by point. I read the
Speaker 1: nutrition label. I know the content, the calories, the ratings.
Speaker 1: I shouldn't have to guess about what's inside the product.
Speaker 1: I certainly shouldn't have to read thirty three thousand word
Speaker 1: legally's terms of service to figure out what's really happening inside.
Speaker 1: It's pretty simple. We make better decisions about the things
Speaker 1: we consume when we know what's in them. So if
Speaker 1: I'm checking out an app on the app store right
Speaker 1: and I see upfront that it's going to harvest my
Speaker 1: data and slang it on some digital street corner, can
Speaker 1: I interest you in so data? I can ask myself, Hey, self,
Speaker 1: are you okay with this app harvesting your data and
Speaker 1: slanging it on a digital street corner? And then, having
Speaker 1: asked myself that question, I can decide whether or not
Speaker 1: to download it. I don't have to hope that it
Speaker 1: won't screw me over. I can know, but check it out.
Speaker 1: This nutrition label idea hasn't just existed in the vacuum
Speaker 1: of my own brain. It's a real thing. There are
Speaker 1: actual people making nutrition labels in the world of tech.
Speaker 1: In the same way that I walk into a bakery
Speaker 1: and I see a cake that's been baked, and I
Speaker 1: might think to myself, I wonder what's in that cake.
Speaker 1: We would want the same thing for a data set,
Speaker 1: where even if you encounter that data set in the wild, you,
Speaker 1: as a data practitioner, will think to yourself, I wonder
Speaker 1: if this is representative. Cash of Malinsky is one of
Speaker 1: those people. These labels are a little different from what
Speaker 1: I propose that Google, I yoe. Their data nutrition labels
Speaker 1: aren't for consumers like me and you at the end
Speaker 1: of the assembly line. Instead, therefore, the people at the
Speaker 1: very beginning the data scientists. Now, Kasha's data nutrition labels
Speaker 1: are an easy to use tool to help data scientists
Speaker 1: pick the data that's right for the thing they're making.
Speaker 1: We interact with algorithms every day, even when we're not
Speaker 1: aware of it. They affect the decisions we make about hiring,
Speaker 1: about policing, pretty much everything. And in the same way
Speaker 1: that we the people ensure our well being through government
Speaker 1: standards and regulations on business activities. For example, data scientists
Speaker 1: needs standards to Kasha is fighting for standards that will
Speaker 1: make sure that artificial intelligence works for our collective benefit
Speaker 1: or at least doesn't undermine m Hi. Hello, how are
Speaker 1: you feeling right now? Kasha? I'm feeling pretty good the
Speaker 1: beginning of another way. Kasha is the co founder and
Speaker 1: lead of the Data Nutrition Project, the team behind those labels.
Speaker 1: They've also worked as a digital services technologist in the
Speaker 1: White House, on COVID analytics at Mackenzie and in communications
Speaker 1: at Google. Yeah. Yeah, so I've kind of I've jumped around. Yeah,
Speaker 1: so why don't you introduce yourself and just tell me
Speaker 1: what you do. My name is Kasha Shamalinski, and I
Speaker 1: am a technologist working on the ethics of data. And
Speaker 1: I'd say, you know importantly to me, although I have
Speaker 1: always been a nerd and I studied physics along time ago.
Speaker 1: I come from a family of artists. Actually, the painting
Speaker 1: behind me is by my brother. There's another one in
Speaker 1: the room by my mom um. And so I come
Speaker 1: from a really kind of multidisciplinary group of people who
Speaker 1: are driven by our passions. And that's kind of what
Speaker 1: I've tried to do too, and it's just led me
Speaker 1: on many different paths. Where does the interest in technology
Speaker 1: come from? For you? You know, I don't think that
Speaker 1: it's really an interest in technology. It's just that we're
Speaker 1: in a technological time. And so when I graduated from
Speaker 1: university with this physics degree, I had a few options,
Speaker 1: and none of them really seemed great. Uh. You know,
Speaker 1: I could go into defense work, I could become a spy,
Speaker 1: or I could make weapons, and that really wasn't so
Speaker 1: interesting to me. Was being was Was spy really an option? Uh? Yes,
Speaker 1: so you know I could do that, um, but I
Speaker 1: didn't end and none of these are really interesting because
Speaker 1: I wanted to make an impact and I wanted to
Speaker 1: drive change, and I think that was around you know, um,
Speaker 1: early thousands, and technology was the place to be. That's
Speaker 1: where you could really have the most impact and solve
Speaker 1: really big problems. Um. And so that's where I ended up.
Speaker 1: So I actually don't think that it's really about the
Speaker 1: technology at all. I think that the technology is just
Speaker 1: a tool that you can use to to kind of
Speaker 1: make an impact in the world. I love the way
Speaker 1: you describe the interest in technology is really just an
Speaker 1: interest in the world. So do you remember some of
Speaker 1: the first steps that led you to what you are
Speaker 1: doing now? So when I graduated, I actually applied to
Speaker 1: many things and didn't get them. And what I realized
Speaker 1: that I really didn't know how to do it at all.
Speaker 1: Always tell a story. Um, and coming out of a
Speaker 1: fairly technical path, I couldn't really make eye contact. I
Speaker 1: hadn't talked to a variety of people. I mean, I
Speaker 1: was definitely one of the only people who had my
Speaker 1: identity in in that discipline at that time. I went
Speaker 1: to a school where the the head of the school
Speaker 1: at the time was saying that women might not be
Speaker 1: able to do science because biologically they were inferior in
Speaker 1: some way. Oh that's nice, very welcoming environment. Oh yeah,
Speaker 1: super welcoming. And I was studying physics and at the time,
Speaker 1: I you know, it was female identified. I now identify
Speaker 1: as non binary. Um. But it wasn't like a great
Speaker 1: place to be doing science, and I just felt like
Speaker 1: coming out of that, I was, UM. I didn't know
Speaker 1: how to talk to people. I didn't know what it
Speaker 1: was like to be part of a great community. And
Speaker 1: so I actually went into communications at Google, which was
Speaker 1: strange duringdustory industry. I went from this super nerdy, very
Speaker 1: male dominated place to like a kind of like the
Speaker 1: party wing of of technology at the time. Right, So
Speaker 1: people who are doing a lot of marketing and communications
Speaker 1: and talking to journalists and telling stories and trying to
Speaker 1: figure out like what's interesting it has this fit into
Speaker 1: the greater narratives of our time. So while at Google,
Speaker 1: I got to see inside of so many different projects
Speaker 1: that I think was a great benefit to being part
Speaker 1: of that strategy team. So I got to work on
Speaker 1: core Search, I got to work on image search, I
Speaker 1: got to work on Gmail and Calendar, and I started
Speaker 1: to see the importance of first of all, knowing why
Speaker 1: you're building something before you start to build it, right,
Speaker 1: And there were so many times that I saw really
Speaker 1: really cool product at the end of the day, something
Speaker 1: an algorithm or something technical that was just really cool,
Speaker 1: but there was no reason that it needed to exist.
Speaker 1: Right from from a person perspective, from a society perspective,
Speaker 1: I am relieved to hear you say that. There's been
Speaker 1: one of my critiques of this industry for quite some time.
Speaker 1: It's like, whose problems are you trying to solve? And
Speaker 1: so you were at the epicenter of one of the
Speaker 1: major companies seeing some of this firsthand. Yeah, that's exactly right,
Speaker 1: And it was endemic. I mean It just happens all
Speaker 1: the time, and it's not the fault of anyone in particular.
Speaker 1: You just put a bunch of really smart engineers on
Speaker 1: a technical problem and they just find amazing ways to
Speaker 1: solve that. But then at the end of the day
Speaker 1: you say, well, how are we actually going to use this?
Speaker 1: And that would fall to the comms team, right or
Speaker 1: the marketing team to say, okay, now what are we
Speaker 1: going to do with this? Um So that was one
Speaker 1: thing and that's why I actually ended up moving into
Speaker 1: product management, where I could think about why we want
Speaker 1: to build something to begin with, and to make sure
Speaker 1: we're building the right thing. Um So I got closer
Speaker 1: to the technology after that job. The second thing that
Speaker 1: I became aware of is the importance of considering the
Speaker 1: whole pipeline of the thing that you build, because the
Speaker 1: thing that you build, it's d n A, is in
Speaker 1: the initial data that you put into it. And I'm
Speaker 1: talking specifically about algorithmic systems here. So one example I
Speaker 1: have from my days when I was at Google. I
Speaker 1: actually I worked out of the London office and there
Speaker 1: was a new search capability and it was trained entirely
Speaker 1: on one particular accent and then when other people tried
Speaker 1: to use that, if they didn't have that very specific accent,
Speaker 1: it wasn't working so well. And I really didn't know
Speaker 1: much about AI at the time. I hadn't studied it,
Speaker 1: but I realized, you know, bias in bias out like
Speaker 1: garbage in garbage out. You you feed this machine something,
Speaker 1: the machine is going to look exactly like what you
Speaker 1: fed it. Right, you are what you eat. We'll be
Speaker 1: right back. We use these terms data, we use this
Speaker 1: terms algorithm and artificial intelligence. And so before we keep going,
Speaker 1: I'd love for you to pause and kind of explain
Speaker 1: what these things are in their relationship to each other. Data, algorithms,
Speaker 1: artificial intelligence. How how does Kasha define these? Yeah, thank
Speaker 1: you for taking a moment. I think that that's um.
Speaker 1: Something that's so important in technology is that people feel
Speaker 1: like they aren't allowed to have an opinion or have
Speaker 1: thoughts about it because they don't quote unquote don't understand it.
Speaker 1: But you're right, it's just it's just a definition all issue. Often. So,
Speaker 1: data is anything that is programmatically accessible that is probably
Speaker 1: in enough volume to be used for something by a system.
Speaker 1: So it could be records of something, it could be
Speaker 1: whether information. It could be the notes taken by a
Speaker 1: doctor that then get turned into something that's programmatically accessible.
Speaker 1: There's a lot of stuff and you can feed that
Speaker 1: to a machine. I'm really interested in algorithms because it's
Speaker 1: kind of the practical way of understanding something like AI.
Speaker 1: It's it's a mathematical formula and it it takes some
Speaker 1: stuff and then it outputs something. So that could be
Speaker 1: something like you input where you live and your name,
Speaker 1: and then the algorithm will churn and spit out something
Speaker 1: like you know what race or ethnicity it thinks you are.
Speaker 1: And that algorithm, in order to to make whatever guesses
Speaker 1: it's making, needs to be fed a bunch of data
Speaker 1: so that it can start to recognize patterns. When you
Speaker 1: deploy that algorithm out in in the world, you feed
Speaker 1: it some data and it will spit out what it
Speaker 1: believes is the pattern that it recognizes based on what
Speaker 1: it knows. You know, there's different flavors of AI. I
Speaker 1: think a lot of people are very afraid of kind
Speaker 1: of the terminator type AI. I'll be back as as
Speaker 1: we should be, because the terminator is very scary. I've
Speaker 1: seen the documentary many times and I don't want to
Speaker 1: live in that world. Yeah, legitimately very scary. UM. And
Speaker 1: so there's this there's this question of Okay, is the
Speaker 1: AI going to come to eat our lunch? Right? Are
Speaker 1: they smarter than us? And all the things that we
Speaker 1: can do, and that's like you know, generalized AI or
Speaker 1: or even kind of super AI. We're not quite there yet. Currently,
Speaker 1: we're in the phase where we have discrete AI that
Speaker 1: makes discreet decisions and we leverage those to help us
Speaker 1: in our daily lives or to hurt us. Sometimes data
Speaker 1: as food for algorithms. I think it's a really useful metaphor.
Speaker 1: And a lot of us out in the wild who
Speaker 1: aren't specialized in this, I think we're not encouraged to
Speaker 1: understand that relationship. I agree, And I think the the
Speaker 1: relationship between what you feed the algorithm and what it
Speaker 1: gives you is so direct, and people don't necessarily know
Speaker 1: that or see that. And what you see is the
Speaker 1: harm or the output that comes out of the system,
Speaker 1: and what you don't see is all the work that
Speaker 1: went into building that system. You have someone who decided
Speaker 1: in the beginning they wanted to use AI, and then
Speaker 1: you have somebody who went and found the data, and
Speaker 1: you have somebody else who cleaned the data, and you've
Speaker 1: got somebody or somebody's who then built the algorithm and
Speaker 1: train the algorithm, and then you have the somebodies who
Speaker 1: coded that up, and then you have the somebody's that's
Speaker 1: deployed that, and then you have people who are running that.
Speaker 1: And so when the algorithm comes out the end and
Speaker 1: there's a decision that's made you get the loan, you
Speaker 1: didn't get the loan. The algorithm recognizes your speech, doesn't
Speaker 1: recognize your speech see ease, you doesn't see you. People think, oh,
Speaker 1: just change the algorithm. Oh no, you have to go
Speaker 1: all the way back to the beginning because you have
Speaker 1: that long chain of people who are doing so many
Speaker 1: different things, and it becomes very complicated to try to
Speaker 1: fix that. So the more that we can understand that
Speaker 1: the process begins with the question do I need AI
Speaker 1: for this? And then very quickly after where are we
Speaker 1: going to get the data to feed that? So that
Speaker 1: we make the right decision. The sooner we understand that
Speaker 1: as a society, I think the easier it's going to
Speaker 1: be for us to build better AI because we're not
Speaker 1: just catching the issues at the very end of what
Speaker 1: can be a year's long process. Mm hm. So so
Speaker 1: what problems does the Data Nutrition Project aimed to tackle?
Speaker 1: We've kind of talked about them all in pieces. At
Speaker 1: its core, the Data Nutrition Project, which is this research
Speaker 1: organization that I co found a bunch of very smart people.
Speaker 1: We were all part of a fellowship that was looking
Speaker 1: at the ethics and governance of AI. And so when
Speaker 1: we sat down to say what are the real things
Speaker 1: that we can do to drive change um as practitioners,
Speaker 1: as people in the space, as people who had built
Speaker 1: AI before, we decided let's just go really small. And
Speaker 1: obviously it's actually a huge problem and it's it's very challenging.
Speaker 1: But instead of saying, let's look at the harms that
Speaker 1: come out of an AI system, let's just think about
Speaker 1: what goes in. And I think we're maybe eating a
Speaker 1: lot of snacks. We were hold up at the M
Speaker 1: I T Media Lab, right, So we were just all
Speaker 1: in this room for many many hours, many many days,
Speaker 1: and I think somebody at some point picked up, you know,
Speaker 1: a snack package, and we're like, what if you just
Speaker 1: had a nutritional label like the one you have on food,
Speaker 1: you just put that on a data set. What would
Speaker 1: that do? I mean, is it possible? Right? But if
Speaker 1: if it is possible, would that actually change things? And
Speaker 1: we started talking it over and we thought, you know,
Speaker 1: we think it would. In our experience in data science
Speaker 1: as practitioners, we know that data doesn't hum with standardized documentation,
Speaker 1: and often you get a data set and you don't
Speaker 1: know how you're supposed to use it or not use it.
Speaker 1: There may or may not be tools that you use
Speaker 1: to look at things that will tell you whether that
Speaker 1: data set is healthy for the thing that you want
Speaker 1: to do with it. The standard process would be a
Speaker 1: product manager CEO would come over to the desk of
Speaker 1: data scientists and say, look, we have all this information
Speaker 1: about this new product we want to sell. We need
Speaker 1: to map the marketing information to the demographics of people
Speaker 1: who are likely to want to buy our product or
Speaker 1: click on our product. Don't make it happen. And the
Speaker 1: data scientist goes okay, and the person goes, oh yeah,
Speaker 1: by tuesday, and the persons like, oh okay, let me
Speaker 1: go find the right data for that. There's a whole world.
Speaker 1: You just google a bunch of stuff and then you
Speaker 1: get the data, and then you kind of poke around
Speaker 1: and you think, as seems pretty good, and then you
Speaker 1: use it and you build your algorithm on that. Your
Speaker 1: algorithm is going to determine which demographics or what geographies
Speaker 1: or whatever it is you're trying to do. You train
Speaker 1: it on that data you found, and then you deploy
Speaker 1: that algorithm and it starts to work in production. And
Speaker 1: you know, no fault of anybody, really, but the industry
Speaker 1: has grown up so much faster than the structures and
Speaker 1: the scaffolding to keep that industry doing the right thing.
Speaker 1: So there might be documentation on some of the data,
Speaker 1: there might not be in some cases. We're working with
Speaker 1: a data partner that was very concerned how people were
Speaker 1: going to use their data. The data set documentation was
Speaker 1: an eighty page PDF. Zero that data scientist who's on
Speaker 1: deadline for Tuesday is not going to read eighty pages.
Speaker 1: So our thought was, hey, can we distill the most
Speaker 1: important components of a data set and its usage to
Speaker 1: something that is maybe one sheet two sheets right, using
Speaker 1: the analogy of the nutrition label, put it on a
Speaker 1: data set, and then make that the standard so that
Speaker 1: anybody who is picking up a data set to decide
Speaker 1: whether or not to use. It will very quickly be
Speaker 1: able to assess is this healthy for the thing I
Speaker 1: want to do. It's a novel application of a thing
Speaker 1: that so many of us understand. What are some of
Speaker 1: the harms you've seen some of the harms you're trying
Speaker 1: to avoid by the data scientists who are building these
Speaker 1: services not having access to healthy data. Yeah. Let's say
Speaker 1: you have a data set that health outcomes and you're
Speaker 1: looking at people who have had heart attacks or something
Speaker 1: like that, and you realize that the data was only
Speaker 1: taken from men in their sixties. If you are now
Speaker 1: going to use this as a data set to train
Speaker 1: an algorithm to provide early warning signs for who might
Speaker 1: have a heart attack, you're gonna miss entire demographics of people,
Speaker 1: which may or may not matter. That's a question. Does
Speaker 1: that matter? I don't know, But perhaps it matters what
Speaker 1: the average size of a body is, or the average
Speaker 1: age of a body is, or maybe there's something that
Speaker 1: is gender or sex related, and you will miss so
Speaker 1: all of that. If you just take the data at
Speaker 1: face value, you don't think about who's not represented here.
Speaker 1: I remember examples that I used to cite in some talks.
Speaker 1: It was the Amazon hiring decisions. Amazon software engineers recently
Speaker 1: uncovered a big problem. Their new online recruiting tool did
Speaker 1: not like women. It had an automated screening system for resumes,
Speaker 1: and that system ignored all the women because the data
Speaker 1: set showed that successful job candidates at Amazon were men.
Speaker 1: And so the computer like garbage in, garbage out. The
Speaker 1: way we've discussed said, well, you've defined success as mail.
Speaker 1: You've fed me a bunch of female that's not success.
Speaker 1: Therefore my formula dictates they get rejected, and that affects
Speaker 1: people's job prospects. You know, that affects people sense of
Speaker 1: their self worth and self esteem. That could open up
Speaker 1: the company to liability, all kinds of harms in a
Speaker 1: system that was supposed to reread efficiency and and help. Yeah,
Speaker 1: that's a great example, and it's, you know, a very
Speaker 1: true one. And I think that one was pretty high profile.
Speaker 1: Imagine all the situations that either have never been caught
Speaker 1: or we're kind of too low profile to make it
Speaker 1: into the news. It happens all the time because the
Speaker 1: algorithm is a kind of a reflection of whatever you've
Speaker 1: fed it. So in that case, you had historical bias,
Speaker 1: and so the historical bias in the resumes that they
Speaker 1: were using to feed the algorithm showed that men were
Speaker 1: hired more frequently and that was success. It also comes
Speaker 1: down to, in terms of the metrics, how you're defining things.
Speaker 1: If your definition of success is that someone was hired,
Speaker 1: you're not necessarily saying that your definition is that person
Speaker 1: was a good ended up being a good worker. Or
Speaker 1: even if you're looking at the person's performance reviews and
Speaker 1: saying success would be that we hire somebody who performs well.
Speaker 1: But historically you hired more men than women. So even then,
Speaker 1: if your success metric is someone who performed well, you're
Speaker 1: already taking into account the historical bias that there are
Speaker 1: more men than women who are hired. So there are
Speaker 1: all different kinds of biases that are being captured in
Speaker 1: the data. Something that the Data Nutrition Project is trying
Speaker 1: to do with the label that we've built is highlight
Speaker 1: these kinds of historical issues as well as the technical
Speaker 1: issues in the data, and that I think is an
Speaker 1: important balance to strike. It's not just about what you
Speaker 1: can see in the data. It's also about what you
Speaker 1: cannot see in the data. So in the case that
Speaker 1: you just called out there with the resumes, you would
Speaker 1: be able to see that's not representative with respect to gender,
Speaker 1: and maybe you'd be able to see things like these
Speaker 1: are all English language resumes, But what you would not
Speaker 1: be able to see are things like socio economic differences
Speaker 1: or people who never applied, or you know, what the
Speaker 1: job market looked like whenever these resumes were collected, So
Speaker 1: you'll kind of not be able to see any of
Speaker 1: that if you just take a purely technical approach to
Speaker 1: what's in the data up. So the data set nutrition
Speaker 1: label tries to highlight those things as well to data
Speaker 1: practitioners to say, before you use this data set, here
Speaker 1: are some things you should consider, and sometimes will even
Speaker 1: go as far as to say you probably shouldn't use
Speaker 1: this data set for this particular thing because we just
Speaker 1: know that it's not good for that, and that's always
Speaker 1: an option, is to say don't use it. Right. It
Speaker 1: doesn't mean people won't do it, but at least we
Speaker 1: can give you a warning, and we kind of hope
Speaker 1: that people have the best of intentions and are trying
Speaker 1: to do the right thing. So it's about explaining what
Speaker 1: is in the data set or in the data so
Speaker 1: that you can decide as a practitioner whether or not
Speaker 1: it is healthy for your usage. After the break, it's
Speaker 1: snack time. I'm back hungry. So I'm holding a package
Speaker 1: your food right now, and I'm looking at the nutrition
Speaker 1: label nutrition facts. It's got servings per container, the size
Speaker 1: of a serving, and then numbers and percentages in terms
Speaker 1: of the daily percent of total fact cholesterol, sodium, carbohydrates, protein,
Speaker 1: and a set of vitamins that I can expect in
Speaker 1: a single serving of this product. And then I can
Speaker 1: make an informed choice about whether and how much of
Speaker 1: that food stuff I want to put in my body,
Speaker 1: how much garbage I want to let in. In this case,
Speaker 1: it's pretty healthy stuff. It's uh dried mangoes. If you're curious,
Speaker 1: what's on your data nutrition label? Yeah, a great question.
Speaker 1: And now I'm like kind of hungry. I'm like, oh,
Speaker 1: it's a snack time. I feel like it's snack time. Um.
Speaker 1: This is the hardest part to me about this project
Speaker 1: is what the right level of metadata is. So what
Speaker 1: are the right elements that you want to call out
Speaker 1: for our nutritional label? You know, what are the facts
Speaker 1: and the sodiums and these kinds of things, Because you know,
Speaker 1: that The complication here is that there are so many
Speaker 1: different kinds of data sets. I can have a data
Speaker 1: set about trees in Central Park, and I can have
Speaker 1: a data set about people in prison. So we've kind
Speaker 1: of identified that the harms that were most worried about
Speaker 1: have to do with people. Um not to say that
Speaker 1: we are, you know, not worried about things like the
Speaker 1: environment or other things, but when it touches people or
Speaker 1: communities is when we see the greatest harms from an
Speaker 1: algorithmic standpoint in society. And so we kind of have
Speaker 1: a badge system that should be very quick, kind of
Speaker 1: icon based that says this data sets about people are
Speaker 1: not This data set includes subpopulation data, so you know,
Speaker 1: includes information about race or gender or whatever status. Right,
Speaker 1: this data set can be used for commercial purposes or not.
Speaker 1: We've identified, let's say, tend to fifteen things that we
Speaker 1: think are kind of high level almost like little food
Speaker 1: warning symbols that you would see on something like organic
Speaker 1: or it's got a surgeon General's warning right exactly. So
Speaker 1: at a very high level we have these kind of icons.
Speaker 1: And then underneath that there are additional very important questions
Speaker 1: that we've highlighted that people will answer who own the
Speaker 1: data set? And then finally there's a section that says,
Speaker 1: here's the reason it was made. The data set was made,
Speaker 1: it's probably an intended use. Here are some other use
Speaker 1: cases that are possible or ways that other people have
Speaker 1: used it, and then here are some things that you
Speaker 1: just shouldn't do. So how do we make this approach
Speaker 1: more mainstream? Mainstream is a tough word because we're talking
Speaker 1: about people who build AI, and I think that is
Speaker 1: becoming more mainstream for sure. Um, but we're really focused
Speaker 1: on data practitioners, so people who are taking data and
Speaker 1: then building things on that data. But there's kind of
Speaker 1: a bottoms up approach. It's very anti establishment in some ways,
Speaker 1: in very hagriculture. And so we've been working with a
Speaker 1: lot of data petitioners to say what works, what doesn't,
Speaker 1: is as useful as it not. Make it open source right,
Speaker 1: open licenses, use it if you want, and just hoping
Speaker 1: that if we make a good thing, people will use it.
Speaker 1: A rising tide lifts all boats, we think, so you know,
Speaker 1: we're not cag about it because we just want better data.
Speaker 1: We have better data out there, and if people have
Speaker 1: the expectation that they're going to see something like this,
Speaker 1: that's awesome. There's also the top down approach, which is
Speaker 1: regulation policy, And I could imagine a world in which
Speaker 1: in the future, if you deploy an algorithm, especially in
Speaker 1: the public sector, you would have to include some kind
Speaker 1: of labeling on that right to talk about the data
Speaker 1: that it was trained on and provide a label for that.
Speaker 1: So it's kind of a two way approach, you know. Yeah, no,
Speaker 1: I mean when I think of analogus, like most of
Speaker 1: us don't know civil engineers personally, but we interact with
Speaker 1: their work on a regular basis through a system of trust,
Speaker 1: through standards, through approvals, through certifications, and data scientists are
Speaker 1: on par with like a civil engineer in my mind,
Speaker 1: and that the erect structures that we inhabit a regular basis.
Speaker 1: But I have no idea what rules they're operating by.
Speaker 1: I don't know what's in this algorithm, you know, I
Speaker 1: don't know how what ingredients you used to put this
Speaker 1: together that's determining whether I get a job or vaccination.
Speaker 1: What's your biggest dream for the Data nutrition project? Where
Speaker 1: does it go? So I could easily say, you know,
Speaker 1: our dream would be that every data set comes with
Speaker 1: a label. Cool, But more than that, I think we're
Speaker 1: trying to drive awareness and change. So even if there
Speaker 1: isn't a label, you're thinking about, I wonder what's in
Speaker 1: this and I wish it had a label on it.
Speaker 1: In the same way that I walk into a bakery
Speaker 1: and I see a cake that's been baked, and I
Speaker 1: might think to myself, I wonder what's in that cake,
Speaker 1: and I wonder, you know, if it has this much
Speaker 1: of something, or maybe I should consider this when I
Speaker 1: decide whether to have four or five pieces of cake.
Speaker 1: We would want the same thing for a data set
Speaker 1: where even if you encounter that data set in the wild,
Speaker 1: someone's created it. You just downloaded it from some repository
Speaker 1: on gith hub. There's no documentation that you, as a
Speaker 1: data practitioner, will think to yourself, I wonder if this
Speaker 1: is representative. I wonder if the thing I'm trying to
Speaker 1: do with this data is responsible, considering the data, where
Speaker 1: it came from, who touched it, who funded it, where
Speaker 1: it lives, how often it's updated, whether they got consent
Speaker 1: from people when they took their data, And so we're
Speaker 1: trying to drive a culture change. I love that and
Speaker 1: I love the idea that when I go to a bakery,
Speaker 1: one of the questions I'm not asking myself is is
Speaker 1: that muffin safe to eat? Right? Is that Kate gonna
Speaker 1: kill me? It literally doesn't enter my mind because there's
Speaker 1: such a level of earned trust in the system overall
Speaker 1: that you know, these people are getting inspected, that there's
Speaker 1: some kind of oversight that they were trained in a
Speaker 1: reasonable way, so I know there's not arsenic in the muffins.
Speaker 1: So this brings me to zooming out a little bit
Speaker 1: further to artificial intelligence and the idea of standards, because
Speaker 1: I'm getting this picture from you that there's kind of
Speaker 1: a wild West in terms of what we're feeding into
Speaker 1: the systems that ultimately become some form of AI. What
Speaker 1: does the world look like when we have more standards
Speaker 1: in the tools and components that create AI. I think
Speaker 1: that our understanding of what AI is and what kinds
Speaker 1: of AI there are is going to mature. I imagine
Speaker 1: that there is a system of classification where some AI
Speaker 1: is very high risk and some AI is less high risk,
Speaker 1: and we start to have a stratified view of what
Speaker 1: needs to occur in each level in order to reach
Speaker 1: an understanding that there's no arsenic in the muffins. So
Speaker 1: at the highest level, when it's super super risky, maybe
Speaker 1: we just don't use AI. This seems to be something
Speaker 1: that people forget, is that we can decide whether or
Speaker 1: not to use it. Like, would you want an AI
Speaker 1: performing surgery on you with no human around? If it's
Speaker 1: really really good? Do you want that? Do you want
Speaker 1: to assume that risk? I mean that is dealing with
Speaker 1: your literal organs, your heart. So I think that you know,
Speaker 1: ideally what happens is you've got a good combination of
Speaker 1: regulation and oversight, which I do believe in, but then
Speaker 1: also training and you know, good human intention to do
Speaker 1: the right thing. So when I think about these algorithms,
Speaker 1: I think of them as kind of automated decision makers,
Speaker 1: and I think they can pose a challenge to our
Speaker 1: ideas of free will and self determination because we are
Speaker 1: increasingly living in this world where we think we're making choices,
Speaker 1: but we're actually operating within a narrow set of recommendations.
Speaker 1: What do you think about human agency in the age
Speaker 1: of algorithms? WHOA These are the big questions? Um, Well,
Speaker 1: I mean I think that we have to be careful
Speaker 1: not to give the machines more agency than they have.
Speaker 1: And there are people who are making those machines. So
Speaker 1: when we talk about, you know, the free will of
Speaker 1: people versus machines, it's like the free will of people
Speaker 1: versus the people who made the machines. To me, technology
Speaker 1: is just a tool, and I personally don't want to
Speaker 1: live in a world that has no algorithms and no
Speaker 1: technology because these are useful tools. But I want to
Speaker 1: decide when I'm using them and what I use them for.
Speaker 1: And so my perspective is really from the point of
Speaker 1: view of a person who has been making the tools,
Speaker 1: and I think that we need to make sure that
Speaker 1: those folks have the free will to say, no, I
Speaker 1: don't want to make those tools, or this should not
Speaker 1: be used in this way, or we need to modify
Speaker 1: this tool in this way so those tools don't run
Speaker 1: away from us. Um So, I guess I I kind
Speaker 1: of disagree with the premise that it's people versus machines
Speaker 1: because people are making the machines and we're not at
Speaker 1: the terminator stage yet. Currently it's people and people, right,
Speaker 1: So so let's so let's like work together to make
Speaker 1: the right things um for people. Yes, Kasha, thank you
Speaker 1: so much for spending this time with me. I've learned
Speaker 1: a lot and now I'm just thinking about Arsenic and
Speaker 1: my muffins. Thanks so much for having me. I've really
Speaker 1: enjoyed it. Garbage in, garbage out. It's a cycle that
Speaker 1: we see that doesn't just apply to the world of
Speaker 1: artificial intelligence, but everywhere. If I feed my body junk,
Speaker 1: it turns to junk. If I fill my planet with filth,
Speaker 1: it turns to filth. If I inject my Twitter feed
Speaker 1: with hatred, that breeds more hatred. It's pretty straightforward, but
Speaker 1: it doesn't have to be this way. In essence, kashas
Speaker 1: to standardize thoughtfulness, and that fills me with so much hope.
Speaker 1: We're all responsible for something or someone, so let's always
Speaker 1: do our best to really consider what they need to thrive.
Speaker 1: If we put a little more goodness into our ai,
Speaker 1: our bodies, our planet, our relationships, and everything else, we'll
Speaker 1: see goodness come out. And that's a psychle I can
Speaker 1: get behind goodness in, goodness out. This is just one
Speaker 1: part of the how does citizen conversation about data? Who
Speaker 1: does data ultimately benefit? If the data is not benefiting
Speaker 1: the people, the individuals, the communities that provided that data.
Speaker 1: Then who are we uplifting at the cost of others justice?
Speaker 1: Next week, we dive deeper into how it's collected in
Speaker 1: the first place, and we meet an indigenous geneticist reclaiming
Speaker 1: data for her people. See you. Then we asked Kasha
Speaker 1: what we should have you do, and they came up
Speaker 1: with a lot. So here's a whole bunch of beautiful
Speaker 1: options for citizening. Think about this. Like people, machines are
Speaker 1: shaped by the context in which they're created. So if
Speaker 1: we think of machines and algorithmic systems as children who
Speaker 1: are learning from us, we're to parents. What kind of
Speaker 1: parents do we want to be? How do we want
Speaker 1: to raise our machines to be considerate, fair, and to
Speaker 1: build a better world than the one we're in today.
Speaker 1: Watch Coded Bias. It's a documentary that explores the fallout
Speaker 1: around m I T media lab researcher Joy will Ameni's
Speaker 1: discovery that facial recognition don't see dark skin face as well,
Speaker 1: and this film is capturing her journey to put for
Speaker 1: the first ever legislation in the US that will govern
Speaker 1: against bias and the algorithms that impact us all. Check
Speaker 1: out this online buying resource called the Privacy Not Included
Speaker 1: Buying Guide. Mozilla built this shopping guide which tells you
Speaker 1: the data practices of the app or product that you're considering,
Speaker 1: and it's basically the product reviews we need in this
Speaker 1: hyper connected era of data theft and hoarding and non
Speaker 1: consensual monetization. Donate if you've got money, you can distribute
Speaker 1: some power through dollars to these groups that are ensuring
Speaker 1: that the future of AI is human and just, the
Speaker 1: Algorithmic Justice League, the a c L You, and the
Speaker 1: Electronic Frontier Foundation. If you take any of these actions,
Speaker 1: please brag about yourself online. Use the hashtag how to citizen.
Speaker 1: Tag us up on Instagram at how to Citizen. We
Speaker 1: will accept general direct feedback to our inbox common at
Speaker 1: how to citizen dot com and make sure you go
Speaker 1: ahead and visit how to citizen dot com because that's
Speaker 1: the brand new kid in town. We have a spanky
Speaker 1: new website. It's very interactive. We have an email list
Speaker 1: you can join. If you like this show, tell somebody
Speaker 1: about thanks. How to Citizen with Baryton Day is a
Speaker 1: production of I Heart Radio Podcasts and Dust Light Productions.
Speaker 1: Our executive producers are Me Barryton Day Thurston, Elizabeth Stewart,
Speaker 1: and Misha Yusuf. Our senior producer is Tamika Adams, our
Speaker 1: producer is Ali Kilts, and our assistant producer is Sam Paulson.
Speaker 1: Stephanie Cohen is our editor, Valentino Rivera is our senior engineer,
Speaker 1: and Matthew Lai as our apprentice. Original music by Andrew Eathan,
Speaker 1: with additional original music for season three from Andrew Clausen.
Speaker 1: This episode was produced and sound designed by Sam Paulson.
Speaker 1: Special thanks to Joel Smith from My Heart Radio and
Speaker 1: Rachel Garcia at dust Light Productions.