Speaker 1: Welcome to tech Stuff, a production from I Heart Radio. Okay, there,
Speaker 1: I'm Lauren Vogelbaum, sitting in for Jonathan Strickland today for
Speaker 1: I Heart Radio's International Women's Day podcast Takeover. Uh. Some
Speaker 1: of y'all might remember me from the way back I
Speaker 1: was Jonathan's co host here for a minute, when I
Speaker 1: was just a ton of little baby podcaster, or from
Speaker 1: Forward Thinking, another show that we worked on together, or
Speaker 1: from other podcasts that I work on myself, or you
Speaker 1: might find my voice new and strange. Um. But in
Speaker 1: any case, hello, thank you for existing and for being
Speaker 1: here today. In honor of International Women's Day, I wanted
Speaker 1: to do this episode about ethics in technology, and specifically
Speaker 1: an artificial intelligence. You know, about the ways that tech
Speaker 1: can can hurt or help the quest to make the
Speaker 1: world more equitable. And you might be going, aren't computers
Speaker 1: essentially or even quintessentially unbiased? Um? You know, a program
Speaker 1: doesn't have feelings, It only has code that it executes,
Speaker 1: And of course that's true, but the humans who write
Speaker 1: the code do have biases, some conscious, some unconscious, and
Speaker 1: so the ways that we tell programs to work can
Speaker 1: carry those biases. One example that I always think of.
Speaker 1: And you might have seen headlines about this back in
Speaker 1: like two about how digital cameras can behave in biased ways.
Speaker 1: There was this whole thing where some webcams weren't tracking
Speaker 1: and focusing on the faces of black users, and some
Speaker 1: other cameras were flagging photos of Asian subjects because the
Speaker 1: software insisted that their eyes were closed. In both of
Speaker 1: these cases, it was clear that the programs had been
Speaker 1: trained on photos of a vast majority of white faces.
Speaker 1: The programs didn't know what to do with skin that
Speaker 1: reflected light differently or with eyes that were a different shape.
Speaker 1: And I will say, like this is not a purely
Speaker 1: digital issue. UM, this sort of thing has been an
Speaker 1: issue in photography for as long as photography has existed.
Speaker 1: UM film stocks were originally created with only white subjects
Speaker 1: in mind. And it wasn't until like furniture and chocolate
Speaker 1: companies started lodging complaints with Kodak in the nineteen sixties
Speaker 1: and seventies. The Kodak started to adjust their films to
Speaker 1: better capture different shades of brown. But on this like
Speaker 1: small example scale, you know, like that sucks for the
Speaker 1: users of these cameras. But these problems are really compounded
Speaker 1: when you start getting into big data and machine learning
Speaker 1: and artificial intelligence. Artificial intelligence has the capacity to totally
Speaker 1: change our world for the better. Um. Everything from making
Speaker 1: our energy grid more efficient and more adaptable, preventing tragic
Speaker 1: outages like we saw in Texas recently, to helping farmers
Speaker 1: make the most of their resources and getting more fresh
Speaker 1: foods to people who currently don't have good access to that,
Speaker 1: to making autonomous vehicles possible, to letting your doctor just
Speaker 1: like real quick consult every case of a disease, ever
Speaker 1: while making a decision about how to proceed with your treatment.
Speaker 1: Uh to I don't know, stopping the thing where you
Speaker 1: always get served ads for the thing you just bought. Yes,
Speaker 1: I like that T shirt, That's why I just bought
Speaker 1: it up. Okay, Uh anyway, it's it's not It's not
Speaker 1: as simple as um Asimov's laws of robotics when you
Speaker 1: start getting into the wider consequences of AI UM and
Speaker 1: you know, um, a robot may not injure a human
Speaker 1: being or through inaction, allow a human being to come
Speaker 1: to harm, which of course didn't even work in the
Speaker 1: fictional world that that asthmov was set up. And robot
Speaker 1: ethics is totally a thing that is also not simple.
Speaker 1: And of course it is ideal if you're if your
Speaker 1: roomba or your autonomous car does not kill you. UM,
Speaker 1: but we're talking about designing these AI systems that will
Speaker 1: change the way of life for whole societies. UM, it
Speaker 1: is a big deal and it could lead to some
Speaker 1: big problems. So we need to talk about how to
Speaker 1: train your algorithm. Um. These big systems start small with
Speaker 1: designers training algorithms with data sets. So right from the start, UM,
Speaker 1: you have the issue of what data is going in
Speaker 1: and within that data, what's being paid attention to and
Speaker 1: what's being ignored. And now I'm not saying that these
Speaker 1: designers are like all mustache twirling villains out there to
Speaker 1: do evil, but they are human. You know, We're each
Speaker 1: moving through the world with our own set of experiences.
Speaker 1: There are so many other experiences that were bound to
Speaker 1: fail to take some of them into consideration, or to
Speaker 1: or to misunderstand some of those circumstances. Which is why
Speaker 1: it's so important to have people from a variety of
Speaker 1: backgrounds and on these projects. And right now, diversity in
Speaker 1: tech is uh not great. UM. Back inten a whole
Speaker 1: bunch of big tech companies got together and pledged to
Speaker 1: increase diversity in their workforces UM and make their results public.
Speaker 1: And every year these reports come out and the numbers
Speaker 1: haven't changed that much in some of these categories in
Speaker 1: six years. UH. Women are better represented now UM or
Speaker 1: as rather having gone from around fift of the workforce
Speaker 1: to around of the workforce at places like Google and Facebook.
Speaker 1: But the only company that showed a comparable jump in
Speaker 1: UM black employees was Amazon, and they're including their distribution
Speaker 1: center employees. And other categories of underrepresented people like people
Speaker 1: with disabilities aren't even being reported in all of these
Speaker 1: public results UM, but studies show that they're dramatically underrepresented
Speaker 1: in the workforce, which just isn't great. You know, when
Speaker 1: we're designing technology like self driving cars that will need
Speaker 1: to take into consideration the movement of wheelchair users. And
Speaker 1: there are unfortunately instances of programs being made specifically with
Speaker 1: bias UM, like in to sixteen when the Boston Police
Speaker 1: Department used this social media surveillance system to flag posts
Speaker 1: made by regular citizens who who used certain terms, for example,
Speaker 1: colloquial Arabic Muslim words or um words like ferguson or protest,
Speaker 1: and we are all in this, whether we like it
Speaker 1: or not. UM Again, just as one example, everything that
Speaker 1: we do online, from you know, what we type into
Speaker 1: search engines and social media sites, to our location data,
Speaker 1: to how we move our mice or like tap at
Speaker 1: our smartphones, all of that has the potential to be
Speaker 1: recorded and collected and sold and referenced and cross referenced
Speaker 1: and used to track us in any number of ways.
Speaker 1: But AI systems are a huge industry worldwide. Business spending
Speaker 1: on artificial intelligence hit about fifty billion dollars in and
Speaker 1: it's expected to more than double that by retail, banking, media, governments,
Speaker 1: All kinds of industries are investing in this, and all
Speaker 1: of this is fairly new, But of course the field
Speaker 1: of ethics, and even computer ethics is not new at all.
Speaker 1: So ethics and technology have always been tied together because
Speaker 1: every time that that we humans create some new technology
Speaker 1: that changes our world and how we interact with it
Speaker 1: and each other, we have to reconsider our world and
Speaker 1: our interactions. You could even argue that in that way,
Speaker 1: like like philosophically, ethics is itself a type of technology.
Speaker 1: But I'm not going to go that deep today. I'm
Speaker 1: backing away from that precipice. Uh So, let's skip ahead
Speaker 1: from you know, the beginning of human consciousness UM to
Speaker 1: the nineteen forties, because that's when digital computers were being
Speaker 1: invented and in the field of cybernetics got started. That's
Speaker 1: the science of information feedback systems, right. UM. Cybernetics was
Speaker 1: pioneered by m T mathematician Norbert Wiener and some of
Speaker 1: his colleagues as they were working during World War Two
Speaker 1: to develop an anti aircraft cannon that could that could
Speaker 1: a detect and track a fast moving airplane and then
Speaker 1: be extrapolate the airplane's probable location in the immediate future
Speaker 1: and aim um and then see signal the firing mechanism
Speaker 1: to fire. And that internal communication that the machine was
Speaker 1: doing really got Weener thinking. In he published a book
Speaker 1: called Cybernetics, or Control and Communication in the Animal and
Speaker 1: the Machine, and in it he mused that these new
Speaker 1: computing machines could very easily become central nervous systems for
Speaker 1: processing all kinds of data from all kinds of instruments,
Speaker 1: and that that potential was huge. He compared it to
Speaker 1: nuclear weapons um. He wrote, long before Nagasaki and the
Speaker 1: public awareness of the atomic bomb, it had occurred to
Speaker 1: me that we were here in the presence of another
Speaker 1: social potentiality, of unheard of importance for good and for evil.
Speaker 1: Oh Weener expounded on this in a book he published
Speaker 1: in nineteen fifty called The Human Use of Human Beings,
Speaker 1: which which basically predicted that integrating computer technology into society
Speaker 1: was going to be another revolution, just as sweeping and
Speaker 1: messy as the Industrial Revolution, and he tried to lay
Speaker 1: out a bit of groundwork for how to not like
Speaker 1: totally bork it up. Spoiler alert, people borked it up anyway.
Speaker 1: There wasn't a whole lot more work done in the
Speaker 1: field of computer ethics until the nineteen sixties and seventies,
Speaker 1: when computer based crime and information security and privacy concerns
Speaker 1: had already become a problem. So by the mid sixties,
Speaker 1: corporations and the government had both begun collecting just giant
Speaker 1: amounts of personal data about US citizens in literally massive computers.
Speaker 1: I guess, I guess all computers are literally massive in
Speaker 1: that they have mass. But okay, you know what I mean. Um,
Speaker 1: From you know, medical records to military records to legal
Speaker 1: documents to shopping habits. And this journalist by the name
Speaker 1: of Vance Packard wrote a book called The Naked Society
Speaker 1: published in nineteen sixty four about the inherent privacy issue
Speaker 1: of having that information collected and available for instantaneous reference. UH.
Speaker 1: Something of an uproar ensued UM. Focusing as Packard's book did,
Speaker 1: on the US government's use of citizens information UM, and
Speaker 1: it led to just a whole bunch of data transparency
Speaker 1: legislation over the next decade. UH. The Freedom of Information Act,
Speaker 1: the Fair Credit Reporting Act, all designed to make sure
Speaker 1: that citizens are able to know what data is being
Speaker 1: collected about them by the government and how it's being used,
Speaker 1: And to be fair, at that time, most computational power
Speaker 1: was in the hands of the government. But this legislature
Speaker 1: and conversation really ignored the activities of private corporations, and
Speaker 1: it never really questioned the ethics of collecting all of
Speaker 1: that at to in the first place. UM. And this
Speaker 1: is apparently like a very American thing, the concept that
Speaker 1: information is inherently good and like more is better UM.
Speaker 1: But that is a rabbit hole for another day. But
Speaker 1: it's not that people weren't thinking about the ethics. One
Speaker 1: important thing that happened around the same time was that
Speaker 1: a researcher by the name of Don Parker, who was
Speaker 1: looking into crime being committed via computers. Parker proposed to
Speaker 1: this leading industry professional organization, the Association for Computing Machinery,
Speaker 1: that they develop a code of ethics for their members,
Speaker 1: and they were like, yeah, cool, you you do that. UM.
Speaker 1: So he headed up a committee and the a c
Speaker 1: M adopted their first code of Ethics in ninety three.
Speaker 1: They've updated it, I think like about once a decade
Speaker 1: since then, UM, with the most recent update being in
Speaker 1: It's really thoughtful. UM. One of my favorite bits from
Speaker 1: the intro its specifies that it's quote not an algorithm
Speaker 1: for solving ethical problems. Rather, it's as a basis for
Speaker 1: ethical decision making. UM. You can read it if you're
Speaker 1: into that sort of thing by going to a c
Speaker 1: M dot org and then back to our timeline. In
Speaker 1: nineteen seventy six, Joseph Weisenbaum, who had created the psychotherapy
Speaker 1: mimicking chat, bought Eliza. A decade earlier, he published his
Speaker 1: book Computer Power and Human Reason From Judgment to Calculation,
Speaker 1: and this book was a response to the response that
Speaker 1: he had gotten from people to his chat Bought Eliza,
Speaker 1: and I know that Jonathan has talked about this chat
Speaker 1: Bought on the show before. UM. It comes up a
Speaker 1: lot and discussions about the Turing test and how convincingly
Speaker 1: computers can approximate human communication because it was one of
Speaker 1: the first that was effective. UM. The thing is, though,
Speaker 1: that Weisenbaum designed Eliza as a demonstration of how bad
Speaker 1: computers inherently are at this type of communication, but he
Speaker 1: got the opposite response from people who tried the program
Speaker 1: out in talking to I mean, you know, typing with
Speaker 1: Eliza about their psychological problems. People felt like Eliza understood them,
Speaker 1: even when they knew it was a robot. UM. So
Speaker 1: Weisenbaum was kind of like, whoe wait uh. And so
Speaker 1: he wrote this book to to really explain the differences
Speaker 1: between computation and human intelligence and to assert that ethics
Speaker 1: are imperative in the design of artificial intelligence because people
Speaker 1: will forget the computers do not have the understanding, the wisdom,
Speaker 1: the moral and emotional consideration of human beings. And this
Speaker 1: is when things really started picking up in the field
Speaker 1: of computer ethics. UM. Also in ninety this professor who
Speaker 1: was teaching medical ethics at the time. Walter Byner noticed
Speaker 1: how computers were complicating that field, and he became the
Speaker 1: first person in academia to really like decide computer ethics
Speaker 1: should be its own field of research and application UM.
Speaker 1: He wrote that computer technology was creating whole new ethical
Speaker 1: concerns that needed to be taken into consideration. UM, so
Speaker 1: he popularized the term computer ethics, did speeches and workshops
Speaker 1: and everything and jumping ahead. The first major textbook on
Speaker 1: the subject was published called Computer Ethics, edited by one
Speaker 1: Deborah Johnson, and Johnson disagreed with maner Um. She argued
Speaker 1: the computers weren't creating new problems, but rather exacerbating old
Speaker 1: problems around things like privacy, ownership, power, and responsibility. We
Speaker 1: will get into what the future may hold after we
Speaker 1: get back from a quick break. Welcome back. As the
Speaker 1: computer industry group with a consumer adoption of home computing,
Speaker 1: with with internet access growing in both the capacity and
Speaker 1: the ubiquity of computers, just absolutely snowballing UM. The field
Speaker 1: of computer ethics exploded UM, and more and more groups
Speaker 1: formed to to help everyone makes sense of all of this.
Speaker 1: For example, the Electronic Frontier Foundation was founded in there,
Speaker 1: that advocacy and activism group that's dedicated to defending civil
Speaker 1: liberties as technology advances. And okay, this is kind of
Speaker 1: a side quest, but I did not notice. They actually
Speaker 1: formed up in response to the federal seizure of a
Speaker 1: bunch of computer equipment belonging to Steve Jackson Games, yep,
Speaker 1: the company that brings us Apples to Apples and Munchkin
Speaker 1: lots of other good stuff. So what happened here? What
Speaker 1: was that was that there was this Bell South digital
Speaker 1: document that explained how the emergency telephone system worked, and
Speaker 1: it leaked, and the U. S. Secret Service was concerned
Speaker 1: that having that info out there was a security concern,
Speaker 1: you know, that hackers might overwhelm the system, um, something
Speaker 1: like that. So so the Secret Service was conducting raids
Speaker 1: tracking this documents digital distribution. Steve Jackson was innocent, um,
Speaker 1: but they got this warrant, conducted this raid, took all
Speaker 1: this stuff, um, and wound up accessing and the leading
Speaker 1: a bunch of personal bulletin board messages from the company's
Speaker 1: website in the process. And now the Secret Service, you know,
Speaker 1: didn't find anything, so they gave the equipment back and
Speaker 1: didn't press charges, but Steve Jackson was like, no, no, no,
Speaker 1: y'all almost tanked my business. You violated the privacy of
Speaker 1: my bulletin board users. We are pressing charges against you.
Speaker 1: But there was really no civil rights organization that was
Speaker 1: prepared to take on the case due to the technological
Speaker 1: complexity of the issue. So the e f F formed
Speaker 1: in order to bring that suit to court. Um, and
Speaker 1: that was the first time that the court recognized the
Speaker 1: email should have equal protection to to to to phone
Speaker 1: calls or any other kind of communication. So so thanks,
Speaker 1: thanks Steve Jackson for for everything. Um, the ff has
Speaker 1: done a whole lot of important stuff. Um, they got
Speaker 1: encryption technology taken off the list of nationally regulated weapons.
Speaker 1: That's a separate episode though, anyway, So side quest over
Speaker 1: back to the main quest. Um, all of this computer
Speaker 1: stuff exploded and the field of computer ethics specialized. Um,
Speaker 1: so now you've got internet ethics, information systems ethics, robot ethics.
Speaker 1: And I and I do want to say, all of
Speaker 1: this does coincide with work being done in in other
Speaker 1: fields engine engineering and information science. Like I don't want
Speaker 1: to imply that computer technology was the only field that
Speaker 1: had been working with and contributing to these to these
Speaker 1: ethical theories and the practical application of them. But like
Speaker 1: I was saying at the top of this episode, one
Speaker 1: of the really interesting specialties to me is in AI
Speaker 1: ethics because it does have such potentially sweeping effects, and
Speaker 1: also because it keeps coming up in the news for
Speaker 1: less than rad reasons. So you have probably seen headlines
Speaker 1: over the past few years about algorithms gone wrong. Um,
Speaker 1: there's there's the story from where Pro Publica found that
Speaker 1: the software used by some courts to determine the risk
Speaker 1: of criminal defendants committing further offenses and therefore to determine
Speaker 1: whether to detain those defendants until trial or or what
Speaker 1: kind of bail to set for them. Um. At least
Speaker 1: one of those algorithms, called Compass, regularly found black people
Speaker 1: riskier and white people less risky, even when everything else
Speaker 1: about the defendants cases were comparable. And this kind of
Speaker 1: issue crops up in discussions about hiring software as well.
Speaker 1: Because of a lack of care in their design, these
Speaker 1: programs that automatically sort through resumes have ranked applicants who
Speaker 1: have woman sounding or or black sounding names as lower
Speaker 1: in consideration. And then there's Google's search algorithms UM from
Speaker 1: eighteen there were all of these headlines. Reverse image searches
Speaker 1: using photos of black people were returning images of guerrillas
Speaker 1: because no one had taught the system how to consider
Speaker 1: dark skin tones in people. Or if you searched the
Speaker 1: seemingly innocuous term black girls, the first page results out
Speaker 1: of trillions of web indexed pages included porn. Or there
Speaker 1: was research into the search history of the white guy
Speaker 1: who killed nine black people in a church in Charleston,
Speaker 1: South Carolina. It's highly likely that he was radicalized, at
Speaker 1: least in part due to the way that Google search
Speaker 1: works UM. It's taking into account location and demographics about
Speaker 1: him and other searchers in his area. Google search returning
Speaker 1: white supremacist propaganda when he searched the term black on
Speaker 1: white crime. By the way, related to this, you can
Speaker 1: make Google give you less specialized search results. And I
Speaker 1: am looking into that and doing it as soon as
Speaker 1: I finished recording this this because it is continually infuriating
Speaker 1: to me, just just when I'm doing my reading for
Speaker 1: for podcast episodes like this and trying to find stuff
Speaker 1: that isn't like a restaurant in my area. If I'm
Speaker 1: talking about a larger concern anyway. UM. Also, remember how
Speaker 1: I was talking about the flaws in programming of digital
Speaker 1: cameras and how they sometimes have trouble discerning specific features
Speaker 1: of people of color. UM. You know, extrapolate that out
Speaker 1: to how facial recognition software is used with surveillance footage
Speaker 1: by police departments. Um, the software is more likely to
Speaker 1: make a mistake in identifying a black person's face because
Speaker 1: the software just isn't as good as seeing that face
Speaker 1: because of how it was programmed, which can lead to
Speaker 1: false identifications and thus wrongful harassments and arrests. UM. Georgetown
Speaker 1: University put out a whole report on this in called
Speaker 1: the Perpetual Lineup. That report found that half of Americans
Speaker 1: have photos and police facial recognition databases, by the way, UM,
Speaker 1: which includes just lots of people with no criminal backgrounds.
Speaker 1: And of course, even if we fix those algorithms, that
Speaker 1: that isn't going to fix the fact that communities of
Speaker 1: color are subject to more surveillance in the first place.
Speaker 1: More recently, there have been headlines and a whole discussion
Speaker 1: about this new rule that was issued in September by
Speaker 1: the U S Department of Housing and Urban Development. And
Speaker 1: this rule essentially makes it super difficult for banks or
Speaker 1: landlord or homeowners insurance companies to be sued for denying
Speaker 1: housing two people of color if an algorithm was used
Speaker 1: to make that determination. Based on the concept that algorithms
Speaker 1: cannot be racist, the rule was immediately challenged, UM, and
Speaker 1: in January, President Biden issued an executive order directing the
Speaker 1: Department of Housing and Urban Development to to examine the
Speaker 1: rules effects. But hoof, hoof, UM, And you know it's
Speaker 1: it's not it's not easy. None of this is easy, um.
Speaker 1: In order to build a non biased artificial intelligence system,
Speaker 1: we um, I mean like like humans, not like you know,
Speaker 1: you and me, dear listener. We need to change the
Speaker 1: systems that lead to the building of artificial intelligence. We
Speaker 1: need to examine how the design and programming is taught, um,
Speaker 1: how companies conduct their business, how policy is written, and
Speaker 1: and who has access to seats at all of those tables.
Speaker 1: I mean it's also not technically easy, Like when you
Speaker 1: build and train these systems, just adding more diverse data
Speaker 1: isn't magically going to make the system create better, less
Speaker 1: biased rules. It might create conflicting rules. UM. You know
Speaker 1: it is expensive in terms of time and effort and
Speaker 1: and just pure physical energy to to do this work.
Speaker 1: The pitfalls of not doing this work are tremendous. You know,
Speaker 1: it can cause measurable hurt in people's lives. And as
Speaker 1: one doctor Debi Chatra, material science engineer, has said, any
Speaker 1: sufficiently advanced neglect is indistinguishable from malice. But the benefits
Speaker 1: to doing this work are equally measurable and tremendous. One
Speaker 1: consideration moving into the future is how to square the
Speaker 1: very concept of ethics with an increasingly multicultural digital world. Um.
Speaker 1: You know, not not everyone on the planet grew up
Speaker 1: with European philosophy, going back to the ancient Greek as
Speaker 1: the basis of their ethical conception. And we also have
Speaker 1: to acknowledge that, um, you know, whatever a culture's philosophical basis,
Speaker 1: it's probably rooted in some biases of its own. Um.
Speaker 1: Just for example, just throwing it out there, if your
Speaker 1: society has coded emotions as feminine and feminine as bad, um,
Speaker 1: then you're probably not giving emotional harm as much weight,
Speaker 1: if any weight, as physical harm in your considerations of justice. Um.
Speaker 1: And you can see the effects of this in things
Speaker 1: like the care that we give our veterans with physical
Speaker 1: injury versus veterans with PTSD UM or just the general
Speaker 1: ways that our society handles mental health versus physical health,
Speaker 1: or or any kind of neurodivergence. All of this work
Speaker 1: in artificial intelligence and the ethics thereof is really requiring
Speaker 1: us to redefine intelligence, to fully consider what we mean
Speaker 1: by human intelligence, what logic and emotion and experience go
Speaker 1: into that, and the ways in which machine intelligence might differ.
Speaker 1: The Stanford Encyclopedia Philosophy references Minsky's book The Society of
Speaker 1: the Mind, saying we do not wish to restrict intelligence
Speaker 1: to what would require intelligence if done by humans. And
Speaker 1: and of course that's true, um A, I can do
Speaker 1: stuff that we can't that that's arguably the whole point, um.
Speaker 1: But it all has to be done with the best
Speaker 1: of what human intelligence can be in mind, and I'm
Speaker 1: just now realizing that, like I might have written a
Speaker 1: forward thinking episode instead of a text stuff episode. But
Speaker 1: but that's what I've got for you today. So if
Speaker 1: you have enjoyed this episode and would like to hear
Speaker 1: more from me, um, you can find me podcasts like
Speaker 1: brain Stuff. It's a It's a daily short form general
Speaker 1: science and culture show. UM or Savor which is a
Speaker 1: food science and history show. Or American Shadows, which is
Speaker 1: produced with Aaron Minky's company Grim and Mild UM It's
Speaker 1: it's a show about some of the darker bits of
Speaker 1: American history UH and ways in which even those dire
Speaker 1: situations UM had light brought to them. I would like
Speaker 1: to give a quick shout out to my friend Damien
Speaker 1: Patrick Williams. He works in this field and has made
Speaker 1: me more familiar with a lot of the concepts that
Speaker 1: I talked about today. UM. You can find lots more
Speaker 1: from him at a Future Worth thinking About dot com.
Speaker 1: This podcast is produced by Tari Harrison and Severe. Thanks
Speaker 1: to her for being so kind and accommodating UM and
Speaker 1: helping me with this episode. The executive producer is Jonathan Strickland,
Speaker 1: and thanks to him for trusting me with his podcast
Speaker 1: for a day. Thanks to you for listening, and He'll
Speaker 1: talk to you again. End really soon. Yeah. Text Stuff
Speaker 1: is an I Heart Radio production. For more podcasts from
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