Speaker 1: Welcome to Tech Stuff, a production of I Heart Radios
Speaker 1: How Stuff Works. Hey there, and welcome to tech Stuff.
Speaker 1: I'm your host, Jonathan Strickland. I'm an executive producer with
Speaker 1: I Heart Radio and How Stuff Works, and I love
Speaker 1: all things tech. And I'm sitting in the audience of
Speaker 1: a local theater like Stage theater not long ago. I'm
Speaker 1: waiting for the show to start, and there's a song
Speaker 1: that's playing over the sound system, and I'm really kind
Speaker 1: of digging the song, but I totally don't recognize it.
Speaker 1: And I glanced down at my phone and I see
Speaker 1: that on the phone below the time on the locked
Speaker 1: phone screen, it says that the song is danger High
Speaker 1: Voltage by Electric six. Now this is obviously a hypothetical
Speaker 1: example because I would recognize that song anywhere, but you
Speaker 1: get the point. Anyway, I'm thinking, that's so cool. My
Speaker 1: phone knows what songs are playing around me. That's so neat.
Speaker 1: I didn't even have to tell to do anything. And
Speaker 1: then a couple of hours later, as I think back
Speaker 1: on this moment, uncertainty and dreads start to see Ben,
Speaker 1: wait a minute, if my phone can identify a song
Speaker 1: that's playing around me, that means my phone is actually
Speaker 1: listening to stuff. It wouldn't be able to tell me
Speaker 1: the song title. Otherwise it has to be able to
Speaker 1: pick up the audio. I didn't activate any app. I
Speaker 1: didn't turn on shah Zam or ask my phone or anything.
Speaker 1: My phone did it by itself. So my phone is
Speaker 1: detecting the sounds around it even when it's not in
Speaker 1: an active mode. Now, on a similar note, I'm sure
Speaker 1: we all have had these personal assistant experiences out there.
Speaker 1: Whether we use one ourselves, we've been around when someone
Speaker 1: else uses them, things like Google Assistant or Alexa or
Speaker 1: Siri or Cartana. There's more of them out there. You
Speaker 1: can activate these assistants with a specific word or phrase,
Speaker 1: and then you speak to them to carry out some
Speaker 1: sort of task or to get you some sort of
Speaker 1: information or something along those lines. We've got a Google
Speaker 1: Home device in our house, so we might use it
Speaker 1: to get a quick rundown on the weather Report. We
Speaker 1: might ask it to play a track off an album
Speaker 1: by the jazz Fusion band weather Report. But wait, that
Speaker 1: means that device is listening to We didn't have to
Speaker 1: take any physical action. We didn't have to push a
Speaker 1: button to make it work. We just spoke the keyword
Speaker 1: or a key phrase, and off it goes. And then
Speaker 1: we get into stuff that seems super creepy. And I'm
Speaker 1: sure most of you have had some sort of experience
Speaker 1: like this. Say you're chatting with friends, maybe you're at
Speaker 1: a restaurant or you're just hanging out, and you're talking
Speaker 1: about this new snack food you just heard about, and
Speaker 1: this is just one part of a conversation that rambles
Speaker 1: all over the place. But then you talk a little
Speaker 1: bit about the snack food for a couple of minutes.
Speaker 1: You're like, you've heard about it, you wanted to try it,
Speaker 1: you haven't tried it yet. Later on, you pop on
Speaker 1: over to Facebook, and as you're scrolling through your feed,
Speaker 1: there it is. There's an ad for the very same
Speaker 1: snack food you mentioned to your friends just a little
Speaker 1: earlier that day. You've never purchased the snack as far
Speaker 1: as you remember, you haven't even searched for it on
Speaker 1: the web, and there's the ad. So as Facebook listening
Speaker 1: in on your conversation in an effort to serve up
Speaker 1: a laser focused targeted ad. One this episode, we're gonna
Speaker 1: take a look at the technology that allows our devices
Speaker 1: to listen in on us, and we'll explore the studies
Speaker 1: about whether or not anything hanky is going on and
Speaker 1: try to separate fact from fud FU D that's fear,
Speaker 1: uncertainty and doubt. And we'll also chat about some recent
Speaker 1: news stories about how big companies have been handing over
Speaker 1: audio messages to third party human contractors and what that
Speaker 1: means in terms of privacy and ethics. Now, first, let's
Speaker 1: address a big reason why devices aren't constantly recording or
Speaker 1: broadcasting all the sounds within an environment that's reachable by microphone.
Speaker 1: It's because that's truly enormous, Like, that's a huge amount
Speaker 1: of data. So let's just take Facebook as an example.
Speaker 1: There are more than two billion people using Facebook every month.
Speaker 1: At least one and a half billion people pop on
Speaker 1: Facebook every single day. Now that's not necessarily the same
Speaker 1: one and a half billion people every day, but every
Speaker 1: day one point five billion people check Facebook, and out
Speaker 1: of that number, nearly one billion of them are accessing
Speaker 1: Facebook on mobile devices. So, just from a data management standpoint,
Speaker 1: there's no way any company, even one as large as Facebook,
Speaker 1: could be actively monitoring, recording, or even analyzing all that
Speaker 1: audio that would be coming in from a billion mobile handsets.
Speaker 1: We are in the age of big data, but we
Speaker 1: still have our limits. Plus you'd have to figure out
Speaker 1: that you know that that large amount of data, most
Speaker 1: of it wouldn't be useful to Facebook. Now, don't get
Speaker 1: me wrong. At the end of the day, you and
Speaker 1: I are the products being bought and sold on Facebook
Speaker 1: and Google and other providers out there. We're potential customers
Speaker 1: for all of the advertisers that use those companies like
Speaker 1: Facebook as a platform. So it benefits the advertisers and
Speaker 1: Facebook and sometimes even us as customers to match the
Speaker 1: right ads to the right people. So there's definitely an
Speaker 1: incentive to learn as much about users as possible to
Speaker 1: leverage their interests and potentially convert them into paying customers
Speaker 1: to an advertiser. Now, this is the very basic foundation
Speaker 1: of Facebook's business model. So if Facebook could do this
Speaker 1: from a technical standpoint, and if the company could get
Speaker 1: away with it from a public perception standpoint, I think
Speaker 1: there's little doubt that face Book would do it. But honestly,
Speaker 1: it's just way too much information to process and to
Speaker 1: boil down into actionable plans. We talk about a lot
Speaker 1: of stuff in our day, you know, and some of
Speaker 1: it we may not really be interested in. We're just
Speaker 1: talking about something, So it wouldn't do Facebook any good
Speaker 1: to serve up ads for stuff that we weren't actually
Speaker 1: really interested in, So it has to pick and choose
Speaker 1: its moments. Facebook has denied using phone microphones in this way.
Speaker 1: In a June second, two thousand sixteen blog post on
Speaker 1: the Facebook newsroom site, a company representative wrote this, and
Speaker 1: here's a quote. Facebook does not use your phone's microphone
Speaker 1: to inform ads or to change what you see in
Speaker 1: news feed. Some recent articles have suggested that we must
Speaker 1: be listening to people's conversations in order to show them
Speaker 1: relevant ads. This is not true. We show ads based
Speaker 1: on people's interests and other profile information, not what you're
Speaker 1: talking out loud about. We only access your microphone if
Speaker 1: you have given our app permission, and if you are
Speaker 1: actively using a specific feature that requires audio. This might
Speaker 1: include recording a video or using an optional feature we
Speaker 1: introduced two years ago to include music or other audio
Speaker 1: in your status updates. End quote. Now, it's understandable that
Speaker 1: people would be a bit skeptical regarding Facebook's claims of innocence.
Speaker 1: In this regard. The company has had several high profile
Speaker 1: scandals and issues with privacy and security. Zuckerberg himself once
Speaker 1: famously declared that privacy is dead. Also, he simultaneously does
Speaker 1: his best to preserve his own privacy. But that's commentary
Speaker 1: for another episode. So I don't blame people for thinking
Speaker 1: that Facebook might actually be listening in on conversations because
Speaker 1: the company has already proven it hasn't been the best
Speaker 1: steward of user privacy in the past. But that doesn't
Speaker 1: mean the company has actually been spying on people. It
Speaker 1: doesn't have to, at least not in that way. And
Speaker 1: this is where we get into some troubling territory because
Speaker 1: it's where we start to learn how services like Google
Speaker 1: and Facebook and others can glean information about us, whether
Speaker 1: we have consciously shared that information or not, and it
Speaker 1: helps explain how these companies can advertise to us so effectively.
Speaker 1: One way Facebook does this is with an innovation called
Speaker 1: Facebook Pixel. Now, this is a piece of code that
Speaker 1: Facebook's clients advertisers really can put on their own websites.
Speaker 1: So it's the type of code you would insert into
Speaker 1: the website for a business. So let's say you own
Speaker 1: a specialty niche marketing shop. We'll say you sell figurines
Speaker 1: based off of iconic horror movie monsters and characters, and
Speaker 1: you're going to advertise on Facebook. The pixel code is
Speaker 1: one way Facebook can optimize that experience. The code pulls
Speaker 1: information off of user behavior on your website and sends
Speaker 1: it to Facebook. If people click over to your site
Speaker 1: because of an ad on Facebook, pixel will register it.
Speaker 1: This helps you see how effective or ineffective your ads
Speaker 1: are on the site. It also can target your ads
Speaker 1: to people on Facebook who would be most likely to
Speaker 1: click on those ads. It might analyze the traits common
Speaker 1: to people who are interacting with your ads, and then
Speaker 1: extrapolate that to target people who have similar traits and
Speaker 1: behaviors but they haven't yet seen your advertisements. Facebook, meanwhile,
Speaker 1: can also use that data to serve up ads from
Speaker 1: other companies to users based on similar findings, and it
Speaker 1: can track other stuff too. Let's say you click over
Speaker 1: to an article on a blog or news site that
Speaker 1: incorporates Facebook pixel in the site's code. Facebook can see
Speaker 1: how long you were on that article, which in turn
Speaker 1: indicates your interest and investment level in that topic. Then
Speaker 1: Facebook can serve up ads related to the contents of
Speaker 1: that article to you. In the end, it's all about
Speaker 1: analyzing user behavior to get the biggest return on investment,
Speaker 1: and it doesn't require are using the microphone to do it.
Speaker 1: They can just look at who you are, where you've been,
Speaker 1: both in real life if it's tracking your location and
Speaker 1: on the Internet if it's tracking your your browsing and
Speaker 1: who your friends are. And all of this information combined
Speaker 1: gives Facebook a ton of data about what kind of
Speaker 1: ads to target towards you. Now, on top of that,
Speaker 1: Facebook can purchase information from data brokers to supplement its
Speaker 1: own guard Ganga and database. There are companies that manage
Speaker 1: stuff like loyalty programs, which also track what you buy.
Speaker 1: They have to for the loyalty programs to work, and
Speaker 1: those purchases are linked to you as a person. They know, Oh,
Speaker 1: Jonathan goes to Starbucks all the time and he always
Speaker 1: gets those Nitro cold brews, So let's put an ad
Speaker 1: that targets him based on that information. Now, that data
Speaker 1: isn't just being used to help you get the best
Speaker 1: deal on whatever it happens to be. That information is valuable.
Speaker 1: So companies that manage these loyalty programs can and do
Speaker 1: buy and sell sell that data you know are spending
Speaker 1: habits are part of this sort of encyclopedia entry about
Speaker 1: our interests, priorities, and behaviors. Now, none of this needs
Speaker 1: to use a microphone to spy on us. So in
Speaker 1: the case of seeing that snack food pop up on
Speaker 1: the Facebook feed, it could simply be that you exhibit
Speaker 1: behaviors similar to ones that people who have bought that
Speaker 1: snack food tend to have. As well. You've liked the
Speaker 1: same sort of pages. You may even have a lot
Speaker 1: of friends who have already bought this stuff. You may
Speaker 1: live in a region where it has recently been introduced.
Speaker 1: These are the kinds of points of data that Facebook
Speaker 1: might use in order to serve that add up to
Speaker 1: you that have nothing to do with your microphone. So
Speaker 1: you got the ad not because you talked about the
Speaker 1: snack food, but because Facebook has sussed out you're the
Speaker 1: type of person who would like that snack food because
Speaker 1: spoiler alert, You're not as special as you think you are,
Speaker 1: and I'm not as special as I think I am.
Speaker 1: Now you could argue, and I would agree with you
Speaker 1: on this, that what Facebook is doing is at least
Speaker 1: as creepy as listening in on a microphone, perhaps even
Speaker 1: more so. Facebook has filed patents that focus on technology
Speaker 1: is meant to predict where you're going to go next
Speaker 1: based on your history of location data. So, in other words,
Speaker 1: Facebook is trying to figure out where you're going to
Speaker 1: go before you go there. And it's not just you,
Speaker 1: it's all the people you know who are using Facebook
Speaker 1: two and so it's not just predicting where you'll go,
Speaker 1: it's also predicting which people you may be running into,
Speaker 1: because it's predicting those people are going to go to
Speaker 1: that same place and whether or not you might encounter
Speaker 1: one another. It can also use that to make suggestions
Speaker 1: to add people on Facebook who are going to those
Speaker 1: same places so that they become your friends online. Now
Speaker 1: why does Facebook care who your friends are? Because the
Speaker 1: more people who use Facebook and the more interconnected they become,
Speaker 1: the more useful the information they generate for Facebook. That
Speaker 1: that ends up becoming more valuable to the company. So
Speaker 1: it is pretty creepy and invasive, and it doesn't have
Speaker 1: to use the microphone. But when we come back, I'll
Speaker 1: talk a bit more about these sound activated features and
Speaker 1: what's actually going on, because there is some stuff we've
Speaker 1: got to be worried about. But first, let's take a
Speaker 1: quick break. When I opened this show, I talked about
Speaker 1: how my phone could listen in on music and identify
Speaker 1: the song even when the phone was in its locked mode.
Speaker 1: Now that's because I have a Pixel to xcel phone.
Speaker 1: It's an Android phone. It's actually a flagship Google phone,
Speaker 1: and there's a feature on the Pixel too that's called
Speaker 1: now playing. You have to activate this feature, you have
Speaker 1: to choose to optimize it. So I want to make
Speaker 1: that clear. I chose to activate this feature. It's not
Speaker 1: just active by default, and with it active, the phone
Speaker 1: can identify music that's playing, and it can tell me
Speaker 1: the title even when the phone is in its locked position.
Speaker 1: So what gives Well, this is not as creepy and
Speaker 1: invasive as it sounds at first glance, because his feature,
Speaker 1: this is incredible to me, is actually entirely local to
Speaker 1: the Pixel two phones. It works on the phone itself.
Speaker 1: It's not consulting the cloud at all, it's not sending
Speaker 1: any information. So how can that be possible? How can
Speaker 1: all this information exists on the phone already? Well, let's
Speaker 1: boil it down first, if you've ever played with any
Speaker 1: digital sound recording software, you've likely seen sound recorded as
Speaker 1: a wave form, a visualization of sound, and typically it's
Speaker 1: pretty simple stuff like if you're using a very basic
Speaker 1: sound recording system, you're mostly looking at changes in amplitude
Speaker 1: or volume. In other words, so you see a continuous
Speaker 1: series of peaks and valleys over the course of a
Speaker 1: sound recording. Those represent the loudest and the quietest parts
Speaker 1: of the recording that changes in volume. You can also
Speaker 1: graph frequency or pitch, and you can if you zoom
Speaker 1: way in, see shapes in the wave form that indicates
Speaker 1: specific phonetics and sounds. Anyone who has worked in audio
Speaker 1: editing for a while can identify at a glance certain
Speaker 1: distinctive sounds. Tari, my producer, can probably tell you just
Speaker 1: by looking at a waveform of my recording which moments
Speaker 1: represent the irritating mouth sounds she removes before publishing an episode.
Speaker 1: It doesn't take long before you can do this yourself.
Speaker 1: It's actually pretty easy to identify, say it like a
Speaker 1: high hat symbol in a music recording, because it's very distinctive. Now,
Speaker 1: that means that songs have these distinctive features like a
Speaker 1: fingerprint that represent the sound of the song, and if
Speaker 1: you can recognize the fingerprint, you can identify the song
Speaker 1: even if you're not listening to the song at that moment.
Speaker 1: And you could look at a print out of a
Speaker 1: wave form of a song and you can try and
Speaker 1: match it against a library of print outs. That's essentially
Speaker 1: what the pixel Too is doing. The program runs in
Speaker 1: the background, It activates when the sound profile indicates that
Speaker 1: there's music present, so it then analyzes the sound that's
Speaker 1: coming in through the microphone and it creates one of
Speaker 1: these digital fingerprints that I was just saying. Then, just
Speaker 1: like you would with a crime scene fingerprint, the pixel
Speaker 1: Too will compare the digital analysis of the song that's
Speaker 1: playing against a local database on the phone of fingerprints
Speaker 1: that represent thousands of popular songs for your region. Now
Speaker 1: exactly how many hasn't really been released, but supposedly in
Speaker 1: the tens of thousands of songs range. And if the
Speaker 1: pixel Too finds a match between the song that is
Speaker 1: currently playing and the one that's in the database, it
Speaker 1: returns the result. This works even if the phone has
Speaker 1: cellular and WiFi data turned off, because again it's all local.
Speaker 1: Now the now playing feature doesn't run constantly because that
Speaker 1: would drain battery life like crazy. Instead, it samples the
Speaker 1: audio approximately every sixty seconds, and it takes time to
Speaker 1: match a song to an entry in the database. The
Speaker 1: cleaner the audio, in other words, the less background noise
Speaker 1: and less interference that's present, the faster this process tends
Speaker 1: to be. This means that when songs transition from one
Speaker 1: song to another, it can take a little bit before
Speaker 1: the phone registers the change. It all depends on the
Speaker 1: acoustic quality of the environment and where in this sampling
Speaker 1: cycle the phone is at any given time, so that's
Speaker 1: not quite as creepy because everything's local on the device.
Speaker 1: It's not sending any data out anywhere else. It's not
Speaker 1: listening to what I'm listening to and an alerting Google
Speaker 1: to let them know, hey, Jonathan's once again listening to
Speaker 1: the soundtrack to be More Chill, which would be an
Speaker 1: accurate suggestion that it would make because I do listen
Speaker 1: to that a lot. Anyway, you can use this feature
Speaker 1: to learn more about the track, the artist, the album,
Speaker 1: including potentially purchasing that music. And those features do connect
Speaker 1: to the outside world through WiFi or cellular connections, but
Speaker 1: that requires an extra step on the part of the user. Also,
Speaker 1: Google pushes out updates to this database with the most
Speaker 1: popular songs, and these are regionalized to reflect the country
Speaker 1: you're in, because you're less likely to run into, say
Speaker 1: a Peruvian pop song when you're in Scotland. The push
Speaker 1: updates do happen over WiFi or cellular local connections. But
Speaker 1: but this is just the reference data that analyze music
Speaker 1: gets compared against. An app like Shazam, on the other hand,
Speaker 1: connects to the cloud, but you also have to activate
Speaker 1: the app to have it listened to the audio, so
Speaker 1: it's a user choice to have the app listen. So
Speaker 1: this is more like a push to talk device, except
Speaker 1: it's pushed to listen. Shazam is also analyzing music to
Speaker 1: sus out a digital fingerprint for the audio, but it
Speaker 1: can compare the sampled audio against a much larger database
Speaker 1: consisting of millions of songs, rather than the tens of
Speaker 1: thousands you would find on the pixel to now playing feature.
Speaker 1: More importantly, I think it's fair to say this isn't
Speaker 1: a creepy use of the technology, since the listening feature
Speaker 1: only activates on the user's command rather than just being
Speaker 1: on by default. Now, this isn't that much different than
Speaker 1: what virtual assistants are doing when you use them. Clearly,
Speaker 1: the microphone on a virtual assistant like Google Home or
Speaker 1: Siri or whatever, it has to be active all the time,
Speaker 1: otherwise you wouldn't get a response when you used whatever
Speaker 1: the keyword or phrase was to activate the assistant. I'm
Speaker 1: going to try and avoid saying any of those phrases,
Speaker 1: by the way, because I don't want those of you
Speaker 1: who have those devices to deal with the frustration of
Speaker 1: them going off in response to something I say. A Now,
Speaker 1: those words or phrases have a specific sound, just like
Speaker 1: music does. In this case, we're talking about phonemes, which
Speaker 1: are recognizable sounds found in language. So in English there
Speaker 1: are forty four phonemes. The order and combination of those
Speaker 1: phonemes are the key. So if you say something that
Speaker 1: has those phonemes in the right order, or if it's
Speaker 1: close enough, if it's an a noisy environment, this can
Speaker 1: activate the virtual assistant. It's like a key fitting into
Speaker 1: a lock. Now, if you're saying other stuff, it's like
Speaker 1: the wrong key is inserted and nothing happens. It's only
Speaker 1: when you say something that fits the lock that the
Speaker 1: assistant activates. This process continues after activation. When you talk
Speaker 1: to the virtual assistant, it analyzes your speech by phonemes.
Speaker 1: Software processes those to figure out what words you are
Speaker 1: actually saying. Well for the first step, that is, because
Speaker 1: it's actually more complicated than that. So, for example, there
Speaker 1: are hominems. These are words that have a similar sound
Speaker 1: but different meanings and often different spellings. An easy example
Speaker 1: is the number eight in the past tense for to eat,
Speaker 1: such as I ate an entire bowl of cao. Mm
Speaker 1: hmm okay. So those two words eight and eight sound
Speaker 1: exactly the same, but they have different meanings. Now that
Speaker 1: means the software can't rely on just the sounds you're
Speaker 1: making when you speak to figure out what you mean,
Speaker 1: has to actually analyze syntax and context and make judgment
Speaker 1: calls about what you are actually meaning when you say
Speaker 1: these things. Sometimes it gets things right, sometimes it gets
Speaker 1: things wrong. But don't be too hard on it. Because
Speaker 1: humans misunderstand other humans all the time. Even when we
Speaker 1: are both communicating with it in the same language, we
Speaker 1: can misunderstand each other. Now, this is still just the
Speaker 1: first step you can think of. This is essentially speed
Speaker 1: each to text. From there, you have to determine what
Speaker 1: is actually being asked by the speaker, what is the
Speaker 1: intent behind the words. If someone speaks French very slowly
Speaker 1: to me, I might be able to spell out what
Speaker 1: is being said phonetically, but that doesn't mean I understand
Speaker 1: the actual content of what was spoken. And to complicate matters,
Speaker 1: there are a lot of different ways to ask for
Speaker 1: the same information. I might say what's the weather for
Speaker 1: this week? Or will I need an umbrella today, or
Speaker 1: one of a dozen other ways to inquire about the weather.
Speaker 1: The software has to be able to determine what the
Speaker 1: intent was behind my question, and then there's another step,
Speaker 1: which is matching intent with action. The assistant has to
Speaker 1: respond to my request, and hopefully it does so in
Speaker 1: a way that's relevant to whatever I was asking about
Speaker 1: in the first place. So if I ask my virtual
Speaker 1: assistant for an update on the weather, I'm not going
Speaker 1: to be impressed if it instead tells me about the
Speaker 1: track FAIC or vice versa. And as assistants get connected
Speaker 1: into more systems like security systems, lights, apps, and more,
Speaker 1: the software has to send appropriate commands to these other
Speaker 1: elements to produce the expected results. Now, this is all impressive,
Speaker 1: and because it's impressive, it could be a little scary
Speaker 1: when we think about assistance as hanging on our every word.
Speaker 1: What are are they always listening? Are they always paying attention? Now?
Speaker 1: They're always monitoring sound, but they're not doing so in
Speaker 1: an effort to broadcast or record information. They are on
Speaker 1: alert for that initiating phrase or word. They ignore everything else.
Speaker 1: More on that a little bit later. Now that being said,
Speaker 1: there are ways in which someone could hack an assistant
Speaker 1: or a phone, or really any connected device that has
Speaker 1: a microphone in order to eavesdrop using that devices microphone.
Speaker 1: Edward Snowden revealed that the n s A use such
Speaker 1: tactics in the agency's surveillance efforts. Apps that have access
Speaker 1: to your phone's camera and microphone for the purposes of
Speaker 1: sharing video, audio, and related features can do some disturbing
Speaker 1: stuff if they're compromised. They can also do some disturbing
Speaker 1: stuff if they're not compromised, but if the party behind
Speaker 1: it is malicious. Felix Krauss made such an app as
Speaker 1: a proof of concept for iOS devices. The app, like
Speaker 1: many others, asked the user for permission to access the camera.
Speaker 1: Kraus stated that once a user agreed to this, the
Speaker 1: app could access both the front and back camera anytime
Speaker 1: the app was in the foreground of the iOS device.
Speaker 1: It could take videos and pictures with no indication to
Speaker 1: the user that such a thing was happening, and it
Speaker 1: could upload that data to a remote server. It could
Speaker 1: even run real time facial recognition software. Now does this
Speaker 1: mean apps like Facebook's Messenger or YouTube are doing this? Well,
Speaker 1: not necessarily, but it does mean it's at least possible
Speaker 1: to do and nothing is stopping him. More, let's say
Speaker 1: ethically unconcerned app from doing just that. So what can
Speaker 1: you do to protect yourself from bad actors? Uh, here's
Speaker 1: the bad news. Not much you could go without using
Speaker 1: such devices and apps in the first place. That's pretty
Speaker 1: darn restrictive. Crowds recommended using camera covers to obscure the
Speaker 1: phone's cameras when you weren't actively using them, or revoking
Speaker 1: camera access to the various apps on the phone. And
Speaker 1: that's about it. Yikes. Now, when we come back, I'll
Speaker 1: cover a related topic that's been in the news lately.
Speaker 1: But first let's take another quick break. Okay, so we
Speaker 1: know it's possible to use cameras and microphones against people,
Speaker 1: either with malware or what amounts to a security loophole
Speaker 1: between handset hardware and apps. But there's something us we
Speaker 1: need to chat about, and that's humans listening in on
Speaker 1: what were assumed to be private conversations and messages. Now
Speaker 1: here's the context. In August two thousand nineteen, several major
Speaker 1: media outlets reported an upsetting revelation, namely that Facebook had
Speaker 1: been sending out audio files that users were creating in
Speaker 1: Facebook Messenger, for example. And these were audio clips sent
Speaker 1: through Messenger itself, so it's akin to a private text
Speaker 1: to a friend. And Facebook was sending these audio files
Speaker 1: to a third party contractor to transcribe that audio. So
Speaker 1: imagine having a private text message thread set to a
Speaker 1: complete stranger for review. It was similar to that, except
Speaker 1: it was audio, not text. So what's actually going on? Well,
Speaker 1: Facebook said this all had to do with users who
Speaker 1: had opted into having their audio messages transcribed automatically. Essentially,
Speaker 1: it was all about using the voice to text option
Speaker 1: in Facebook. Now, according to Express Computer, this option didn't
Speaker 1: really have a warning that let you know that those
Speaker 1: audio files you were creating through this voice to text
Speaker 1: feature would go to be heard by any humans out there.
Speaker 1: In fact, they said that the warning that would pop up,
Speaker 1: or the notification that popped up said, turn on voice
Speaker 1: to text in this chat using Facebook Messenger, and above
Speaker 1: the no and yes buttons where you would choose one
Speaker 1: of these options. Facebook further would describe the option display
Speaker 1: text of voice clips you send and receive. You can
Speaker 1: control whether text is visible to you for each chat.
Speaker 1: So again it makes it sound like, oh, this is
Speaker 1: all automated. If I use voice to text, I just
Speaker 1: say a phrase, the text shows up. I might have
Speaker 1: to make some adjustments to the text, maybe it has
Speaker 1: misinterpreted one of the words or whatever. But sort of
Speaker 1: a hands free approach to sending messages in Messenger. Lots
Speaker 1: of apps use voice to text features, and in theory
Speaker 1: it's a pretty great feature. You can dictate a message
Speaker 1: to be sent to your friend without having to stare
Speaker 1: at the screen and type or swipe on a keyboard.
Speaker 1: Tons of folks use features like this if they want
Speaker 1: to interact with an app while they're driving, for example,
Speaker 1: to minimize the distractions they have as they putter around.
Speaker 1: But you'll notice those messages don't seem to indicate anywhere
Speaker 1: that the voice to text recordings could be sent to
Speaker 1: a human being for review. Express Computer further explains that
Speaker 1: even on a supplemental page explaining the voice to text feature,
Speaker 1: Facebook fails to mention that human beings will be reviewing
Speaker 1: that material. Instead. The supplemental page talks about how voice
Speaker 1: to text uses machine learning to get better at interpreting
Speaker 1: what you saying, so that it becomes more useful to
Speaker 1: you the more you actually use the feature. So the
Speaker 1: concept here was that some voice recognition software would transcribe
Speaker 1: this audio. Google Voice also used to do this for
Speaker 1: voice messages. I remember getting voicemails from my mother, who
Speaker 1: has a Southern US dialect as do I, but hers
Speaker 1: is more pronounced. The Google Voice speech to text program
Speaker 1: had problems interpreting my mother's messages, and frequently the transcription
Speaker 1: would be hilariously off track, and most of the time
Speaker 1: I wouldn't even be able to guess what the original
Speaker 1: message was based off the transcription. It meant that I
Speaker 1: would listen to the voicemail and then I would shake
Speaker 1: my head a lot as I would read the transcription
Speaker 1: at the same time and just see how far off
Speaker 1: it was. This is a big challenge for voice recognition programs.
Speaker 1: There are a lot of different dialects and accents. People
Speaker 1: from different regions within the same country can sound very
Speaker 1: different even if they're speaking the exact same language. If
Speaker 1: you get someone from Savannah, Georgia, a native of Savannah, Georgia,
Speaker 1: and a native from Boston, Massachusetts, they're going to be
Speaker 1: able to have a conversation with each other, but they
Speaker 1: will end up saying the same words very differently from
Speaker 1: one another. And that's before you even start talking about
Speaker 1: people who have a different native language, who have learned
Speaker 1: English and have a foreign accent on top of the
Speaker 1: English they speak. There's no hard and fast rule you
Speaker 1: can create for a voice recognition program to follow to
Speaker 1: interpret speech correctly throughout a language. Because there's so much
Speaker 1: variation in how the words and that language are said,
Speaker 1: training the model becomes a challenge. So one thing you
Speaker 1: can do is you have a human being transcribe spoken
Speaker 1: words and then compare the human transcription against the machine
Speaker 1: produce transcription in an effort to train your model to
Speaker 1: be more effective. Humans are pretty good, though not perfect,
Speaker 1: at figuring out what some other humans says. Assuming both
Speaker 1: parties are fluent in the same language. By comparing these
Speaker 1: two records against each other and then making corrections to
Speaker 1: the model, computer scientists can tweak their voice recognition software
Speaker 1: models to be more accurate. Now, ideally you would do
Speaker 1: this before unleashing such a system on the public, but
Speaker 1: that's not really that practical. There is no in lab
Speaker 1: project that is going to come close to generating the
Speaker 1: amount of data and the sheer variety that you will
Speaker 1: encounter out in the real world. Improving the model would
Speaker 1: happen much faster with a larger sample of subjects using
Speaker 1: the model, and a billion or so people is a
Speaker 1: pretty darn big sample size. But that means sending these
Speaker 1: audio files to humans in the first place. And Facebook
Speaker 1: has said that the files were anonymized so that there
Speaker 1: was no identifiable name or anything associated with each of
Speaker 1: the audio files being sent for human review. But hey,
Speaker 1: I hear you say. Earlier in this episode, you pointed
Speaker 1: out how it's possible to really get an idea about
Speaker 1: a person just from the other data they provide, and
Speaker 1: you'd be right. These audio files had all sorts of
Speaker 1: different types of content in them, some of it was
Speaker 1: likely upsetting disturbing or inappropriate. Contractors who had been hired
Speaker 1: to do the transcription came forward anonymously, I might add,
Speaker 1: because they didn't want to get fired from their jobs,
Speaker 1: and said they felt that the practice was an unethical one.
Speaker 1: And media outlets looked into it and their conclusions were
Speaker 1: pretty much the same. Right down the board, Facebook was
Speaker 1: not transparent about what was happening with this audio, and
Speaker 1: there were no clear indications to users that their audio
Speaker 1: files might get sent to some stranger for the purposes
Speaker 1: of transcription. Now, for its part, Facebook said it halted
Speaker 1: the practice in early August two thousand nineteen, and third
Speaker 1: party contractors have said that that is true that they
Speaker 1: no longer are doing this work for Facebook. Facebook isn't
Speaker 1: the only company to come under scrutiny for this kind
Speaker 1: of thing. Google, Apple, and Microsoft have also been under
Speaker 1: the microscope for very similar practices. Now, on the one hand,
Speaker 1: it's understandable that these companies want to improve their voice
Speaker 1: recognition capabilities. It's what makes these apps and products useful
Speaker 1: and makes it more useful to a wider variety of
Speaker 1: people by training the models on this stuff. But the
Speaker 1: privacy concerns remain and it's something that isn't just troubling
Speaker 1: to users, but to the people actually being paid to
Speaker 1: transcribe the stuff in the first place. Now, it would
Speaker 1: be another matter if the companies were transparent about this practice.
Speaker 1: If users knew that there's a chance a real, live
Speaker 1: human being would be listening in on some of those
Speaker 1: voice messages for the purposes of quality control for the
Speaker 1: voice to text feature, maybe they wouldn't opt into using
Speaker 1: the voice to text in the first place, or they
Speaker 1: might opt in and not care. In some cases, I'm
Speaker 1: sure there'd be no shortage of people who would actually
Speaker 1: say truly terrible things, hoping that some poor contractor would
Speaker 1: have to listen to it all and check the audio
Speaker 1: against the automated transcription, because some people would just play nasty.
Speaker 1: Don't be nasty. By the way, there are better ways
Speaker 1: to entertain yourself than by making some other person's life miserable.
Speaker 1: Facebook could potentially face some serious charges based on this practice.
Speaker 1: The company had settled with the Federal Trade Commission, or FTC,
Speaker 1: earlier in the summer of two thousand nineteen. The settlement
Speaker 1: was for an incredible five billion dollars, and it largely
Speaker 1: revolved around the company's rather abysmal record with privacy. The
Speaker 1: charges date all the way back to two thousand twelve,
Speaker 1: when the FTC brought eight privacy related allegations against Facebook.
Speaker 1: And again, this isn't a big surprise. Zuckerberg had already
Speaker 1: cavalierly proclaimed privacy dead a couple of years before that. Now,
Speaker 1: in the settlement, Facebook agreed to adhere to some rules.
Speaker 1: Those rules said that Facebook was prohibited from making misrepresentations
Speaker 1: about the privacy or security of consumers information, prohibited from
Speaker 1: misrepresenting the extent to which it shares personal data, and
Speaker 1: it required Facebook to implement a reasonable privacy program. Now
Speaker 1: I'm no legal expert, not by a long shot, but
Speaker 1: it seems to me that Facebook's failure to alert users
Speaker 1: that their voice to text data could be sent to
Speaker 1: non Facebook employees for review is in violation of this agreement.
Speaker 1: That Facebook agreed to these terms in July two thousand nineteen,
Speaker 1: and then continued the practice into August is a big problem.
Speaker 1: Whether or not it will result in further legal action
Speaker 1: against this company is unknown as I record this episode,
Speaker 1: but it seems like it's at least possible, So I'm
Speaker 1: gonna wrap this up. We know that microphones can sit
Speaker 1: in on us without our knowledge. The n s A
Speaker 1: worked on programs in the United States that did exactly that.
Speaker 1: And while companies with virtual personal assistants tell us that
Speaker 1: those assistants only activate when certain phrases are spoken, it's
Speaker 1: also possible that that list of phrases could go well
Speaker 1: beyond the ones published by the company. So, in other words,
Speaker 1: I might know that to wake up my hypothetical virtual assistant,
Speaker 1: I would have to say the alert phrase sky net awaken,
Speaker 1: and then it pays attention. But what if there's a
Speaker 1: whole laundry list of other words or phrases that could
Speaker 1: wake it up so that it records or transcribes whatever
Speaker 1: audio follows. What if, for example, the phrase shopping or
Speaker 1: going shopping activates it so that whatever follows gets registered
Speaker 1: by the device. So if I tell a friend tomorrow,
Speaker 1: I'm going shopping for some new sneakers, the device has
Speaker 1: registered the phrase new speakers because it paid attention once
Speaker 1: I said the words going shopping, and then I starting
Speaker 1: ads pop up everywhere I go online for sneakers. Now,
Speaker 1: is that something that's possible, Well, yeah, it's possible. That
Speaker 1: doesn't mean it's happening, but it could be It's also
Speaker 1: possible that my other behaviors have indicated that I'm on
Speaker 1: the lookout for some new kicks. Coincidence is a thing,
Speaker 1: and it's frustrating because without seeing behind the scenes, it's
Speaker 1: hard to draw any firm conclusions. Most of us, myself included,
Speaker 1: have a limited understanding of exactly how much data we're
Speaker 1: generating in our day to day lives and how that
Speaker 1: data can be analyzed for patterns and predictions. We may
Speaker 1: not even be aware that we're heading toward a particular
Speaker 1: decision before an algorithm draws that conclusion, and it's spooky
Speaker 1: and disturbing. But it doesn't necessarily mean that we're being
Speaker 1: spied on by a microphone. It may mean we're just
Speaker 1: broadcasting our decisions before we've known that we've made a decision,
Speaker 1: and it does indicate that there is some sort of
Speaker 1: eaves dropping going on, just not necessarily audio eaves dropping.
Speaker 1: It's more about all of our other behaviors that humans
Speaker 1: don't pick up on, so we've never had to worry
Speaker 1: about it before, but machines can analyze it at a
Speaker 1: level that is disturbing. In fact, an actual study at
Speaker 1: Northeastern University looked into the possibility of whether or not
Speaker 1: phones were getting activated by clandestine phrases and listening in
Speaker 1: on conversations, and it found that there was no evidence
Speaker 1: that this was happening. They did find that a lot
Speaker 1: of apps were taking screenshots of stuff on phones and
Speaker 1: sending those screenshots to third parties, though, so you know,
Speaker 1: that's also disturbing, But it doesn't appear that these devices
Speaker 1: are actively listening to you all the time and recording
Speaker 1: or transcribing or broadcasting that information anywhere. There's a lot
Speaker 1: to lose from doing that approach. The problem is it
Speaker 1: is something that is possible, and the other problem is
Speaker 1: that there are other behaviors were doing that are just
Speaker 1: as revealing, if not more so, than recording what it
Speaker 1: is we're saying, and that without being aware of that,
Speaker 1: we are just giving away more and more information about
Speaker 1: ourselves and more and more control over our own lives.
Speaker 1: And we're going to see more and more targeted ads
Speaker 1: that seem super creepy because there's mentioning things that we
Speaker 1: didn't think anyone knew about, because most people wouldn't pick
Speaker 1: up on it fun times, So I don't think this
Speaker 1: was a particularly you know, um, I don't think this
Speaker 1: show really helps allay any fears. It may just switch
Speaker 1: fears from microphones to everything else. But I did want
Speaker 1: to cover this because a lot of people have been
Speaker 1: talking about it for the last few years, and with
Speaker 1: these transcription services that has brought the whole conversation back
Speaker 1: into you the forefront. So I wanted to take an
Speaker 1: opportunity to really tackle it here on the show. If
Speaker 1: you have a suggestion for a future episode of tech Stuff,
Speaker 1: send me an email the addresses tech Stuff at how
Speaker 1: stuff works dot com, or drop me a line. By
Speaker 1: going to tech stuff podcast dot com. You will find
Speaker 1: there a link to all of our archived episodes, as
Speaker 1: well as links to our presence on social media where
Speaker 1: you can get in touch with us, and also a
Speaker 1: link to our online store, where every purchase you make
Speaker 1: goes to help the show. We greatly appreciate your support
Speaker 1: and I will talk to you again really soon. Text
Speaker 1: Stuff is a production of I Heart Radio's How Stuff Works.
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