1 00:00:00,400 --> 00:00:03,680 Speaker 1: Jack Wesley works in a warehouse in eastern Pennsylvania, and 2 00:00:03,720 --> 00:00:07,080 Speaker 1: his job involves a lot of bending, lifting, twisting, and reaching. 3 00:00:07,120 --> 00:00:12,760 Speaker 1: Every day. Done wrong, all this repetitive motion can lead 4 00:00:12,760 --> 00:00:15,880 Speaker 1: to variety of back injuries that are all too common 5 00:00:15,920 --> 00:00:19,160 Speaker 1: for his profession. So when his boss has recently asked 6 00:00:19,200 --> 00:00:21,720 Speaker 1: him to try out this new piece of technology that 7 00:00:21,760 --> 00:00:25,159 Speaker 1: they said would keep him safer, he said, sure, why not. 8 00:00:25,720 --> 00:00:29,400 Speaker 1: It's a motion tracking sensor that sits on his chest. Yeah. 9 00:00:29,680 --> 00:00:31,800 Speaker 1: Come in the morning, I grab this, I grab my radio. 10 00:00:32,280 --> 00:00:35,400 Speaker 1: Um yeah, trap it right on and go to work. 11 00:00:35,440 --> 00:00:38,800 Speaker 1: It's pretty easy. The device basically warns Jack when he's 12 00:00:38,840 --> 00:00:41,960 Speaker 1: lifting boxes with bad form. He says, it's hot in 13 00:00:42,040 --> 00:00:45,199 Speaker 1: better habits. Yeah, sometimes I noticed, uh, if I was 14 00:00:45,360 --> 00:00:48,159 Speaker 1: bending forward a little more like reaching deeper into a palette, 15 00:00:48,159 --> 00:00:51,159 Speaker 1: that might have been something that would they would vibrate 16 00:00:51,159 --> 00:00:53,720 Speaker 1: on me for but uh started walking around to the 17 00:00:53,760 --> 00:00:57,080 Speaker 1: side of the paletts. Yeah, thanks to the reminder and 18 00:00:57,400 --> 00:01:04,520 Speaker 1: kind of gotten away from that now too. This same sensor, though, 19 00:01:04,760 --> 00:01:09,200 Speaker 1: is also gathering incredibly detailed information on his every movement, 20 00:01:09,880 --> 00:01:14,000 Speaker 1: and it's sending that information to his bosses. It's one 21 00:01:14,000 --> 00:01:18,039 Speaker 1: of the many technologies today allowing employers to monitor their workers, 22 00:01:18,520 --> 00:01:21,920 Speaker 1: especially in blue collar professions, and it's a trend that's 23 00:01:21,959 --> 00:01:27,840 Speaker 1: troubling worker rights advocates. Today, in the show, reporter Joshua 24 00:01:27,880 --> 00:01:31,360 Speaker 1: Bustine investigates the dilemma behind the technology Jack's been testing. 25 00:01:32,200 --> 00:01:35,039 Speaker 1: Tools like it have the potential to bring real benefits 26 00:01:35,080 --> 00:01:38,080 Speaker 1: to high risk jobs, but do workers risk giving up 27 00:01:38,120 --> 00:01:43,000 Speaker 1: too much information about themselves? Amako, you're listening to Decrypted 28 00:01:43,480 --> 00:02:01,800 Speaker 1: Stay with us, Jash. How's it going good? How are 29 00:02:01,800 --> 00:02:05,120 Speaker 1: you good things? I think your story this week is 30 00:02:05,160 --> 00:02:07,800 Speaker 1: something that hits a nerve and a lot of people. Yeah. No, 31 00:02:07,840 --> 00:02:10,520 Speaker 1: one really likes the idea of being tracked by their boss, right. 32 00:02:11,720 --> 00:02:14,640 Speaker 1: You know, this is something that's become pretty widespread, I 33 00:02:14,680 --> 00:02:17,520 Speaker 1: think in the blue collar workforce. I've been reading a 34 00:02:17,560 --> 00:02:21,000 Speaker 1: lot about the ways that workers in Amazon's fulfillment centers, 35 00:02:21,040 --> 00:02:24,360 Speaker 1: for example, have been tracked. Yeah, but you're referring to 36 00:02:24,600 --> 00:02:28,040 Speaker 1: are the production quotas that a lot of workplaces use. 37 00:02:28,360 --> 00:02:31,200 Speaker 1: Amazon certainly uses them. The idea being that you can 38 00:02:31,240 --> 00:02:34,520 Speaker 1: track exactly how fast a worker is doing something from 39 00:02:34,520 --> 00:02:36,560 Speaker 1: minutes a minute, and then make sure that they're not 40 00:02:36,600 --> 00:02:40,520 Speaker 1: doing it even the slightest bit slower. The tracking we're 41 00:02:40,520 --> 00:02:46,040 Speaker 1: talking about here is actually slightly different. The company that 42 00:02:46,080 --> 00:02:49,480 Speaker 1: makes the trackers, a startup in Brooklyn called strong Arm, 43 00:02:49,639 --> 00:02:53,679 Speaker 1: says that they are tracking only data to tell how 44 00:02:53,720 --> 00:02:56,600 Speaker 1: someone is moving in an attempt to determine when they 45 00:02:56,680 --> 00:02:59,280 Speaker 1: might be at higher risk of injury. And they're specifically 46 00:02:59,360 --> 00:03:02,120 Speaker 1: saying that they are not trying to track production data. 47 00:03:03,320 --> 00:03:06,200 Speaker 1: Tell me about them strong Arm. Sure. They're a small 48 00:03:06,200 --> 00:03:09,679 Speaker 1: company started by a guy named Sean Peterson, and he's 49 00:03:09,720 --> 00:03:12,160 Speaker 1: a pretty interesting guy. He told me that he always 50 00:03:12,160 --> 00:03:16,760 Speaker 1: wanted to be an inventor. His grandfather invented things for 51 00:03:16,840 --> 00:03:21,520 Speaker 1: the railroads, and then his dad had a construction company 52 00:03:21,520 --> 00:03:24,079 Speaker 1: out on Long Island, where Sean is from, and even 53 00:03:24,120 --> 00:03:27,200 Speaker 1: as a kid, Sean would work alongside him. Said he 54 00:03:27,240 --> 00:03:29,919 Speaker 1: always just really loved the idea of working with his hands. 55 00:03:30,200 --> 00:03:32,640 Speaker 1: He had a pretty amazing workshop, and before he passed away, 56 00:03:32,919 --> 00:03:34,600 Speaker 1: he just taught me how to kind of get into 57 00:03:34,639 --> 00:03:37,360 Speaker 1: the tools and start working at Then my father was 58 00:03:37,400 --> 00:03:42,560 Speaker 1: a rant, a construction company and that contracting company. You know, 59 00:03:42,760 --> 00:03:44,760 Speaker 1: by the time I was twelve, I could lay an 60 00:03:44,880 --> 00:03:49,640 Speaker 1: entire roof by myself. Um, the gratification and work itself 61 00:03:49,760 --> 00:03:52,160 Speaker 1: was just ingrained in me when I was a little guy. 62 00:03:52,240 --> 00:03:55,400 Speaker 1: And Sean actually started getting interested in workplace safety issues 63 00:03:55,440 --> 00:03:57,680 Speaker 1: when he was young and his father was actually killed 64 00:03:57,680 --> 00:04:02,520 Speaker 1: in a workplace accident. Uh, I don't really want to 65 00:04:02,520 --> 00:04:05,200 Speaker 1: talk too much detail about kind of stinking of it, 66 00:04:05,240 --> 00:04:09,840 Speaker 1: but he died on the drop in. They're way way 67 00:04:10,000 --> 00:04:13,680 Speaker 1: too many things that happened that could have recoided that. 68 00:04:14,560 --> 00:04:18,520 Speaker 1: And as he got older, Sean combined these two principles 69 00:04:18,520 --> 00:04:22,080 Speaker 1: of his and he wanted to invent something that would 70 00:04:22,160 --> 00:04:27,200 Speaker 1: help prevent workplace injuries um in the future. And so 71 00:04:27,200 --> 00:04:31,040 Speaker 1: Sean started a company. It's called strong Arm Technologies, and 72 00:04:31,279 --> 00:04:35,800 Speaker 1: its main product is a small device that sits on 73 00:04:35,839 --> 00:04:40,080 Speaker 1: your chest and tracks all of your movements. And the 74 00:04:40,160 --> 00:04:45,960 Speaker 1: idea is that if people in manual labor jobs warthies 75 00:04:46,040 --> 00:04:49,520 Speaker 1: all day, you would gather enough information to tell who 76 00:04:49,680 --> 00:04:54,120 Speaker 1: was making movements that were associated with injuries and then 77 00:04:54,160 --> 00:04:57,080 Speaker 1: be able to deal with them. So, Josh, what what 78 00:04:57,160 --> 00:05:02,040 Speaker 1: does the device look like? So the device is a 79 00:05:02,160 --> 00:05:07,200 Speaker 1: small black box. Basically, it's a rectangle. It's about the 80 00:05:07,400 --> 00:05:12,400 Speaker 1: size of a small smartphone maybe, and it fits into 81 00:05:12,440 --> 00:05:14,960 Speaker 1: a little harness you wear kind of over your shoulders. 82 00:05:14,960 --> 00:05:18,200 Speaker 1: So it sits right against your chest, left shoulder, it's 83 00:05:18,200 --> 00:05:22,880 Speaker 1: gonna come around your waist. And that's Alex Teller. He's 84 00:05:22,880 --> 00:05:25,480 Speaker 1: another strong Arm executive. And what you're hearing is him 85 00:05:25,520 --> 00:05:29,000 Speaker 1: actually helping me put on the device. Feel a buzz 86 00:05:29,040 --> 00:05:34,159 Speaker 1: in your hand, Yeah, but already warning me that I'm doing. 87 00:05:35,800 --> 00:05:39,039 Speaker 1: And this device tracks your movement kind of like a fitbit. 88 00:05:39,520 --> 00:05:43,080 Speaker 1: It's sort of like a fitbit, but gathering detail at 89 00:05:43,080 --> 00:05:46,640 Speaker 1: a much greater level. A fitbit counts your steps, which 90 00:05:47,080 --> 00:05:50,640 Speaker 1: doesn't actually tell you all that much. This thing counts 91 00:05:50,680 --> 00:05:54,520 Speaker 1: exactly the way that you bend from moment to moment 92 00:05:54,600 --> 00:06:00,880 Speaker 1: for the entire day, So you know, it's taking the 93 00:06:00,880 --> 00:06:06,279 Speaker 1: fitbit and it is like massively increasing the detail that 94 00:06:06,480 --> 00:06:15,600 Speaker 1: the device gathers about your physical activity throughout the day. 95 00:06:17,520 --> 00:06:21,280 Speaker 1: And Josh, where's this thing being used? So strong Arm, 96 00:06:21,320 --> 00:06:24,240 Speaker 1: for such a small company, actually has a very impressive 97 00:06:24,320 --> 00:06:28,279 Speaker 1: list of clients. I talked to Toyota about a pilot 98 00:06:28,320 --> 00:06:31,080 Speaker 1: that they just started with strong Arm at a plant 99 00:06:31,080 --> 00:06:35,240 Speaker 1: in Indiana. They're used by some facilities that Heineken runs. 100 00:06:35,800 --> 00:06:40,320 Speaker 1: And I also visited a warehouse in eastern Pennsylvania that's 101 00:06:40,400 --> 00:06:48,440 Speaker 1: run by a company called geodas friends, and what was 102 00:06:48,480 --> 00:06:52,120 Speaker 1: it like. So it's in an area where everything is 103 00:06:52,240 --> 00:06:54,760 Speaker 1: logistics facility. It was actually right next door to an 104 00:06:54,800 --> 00:06:59,920 Speaker 1: Amazon fulfillment center, which was interesting and really just enorm 105 00:07:00,000 --> 00:07:04,560 Speaker 1: a facility. Workers picking things out of boxes, packing them 106 00:07:04,600 --> 00:07:08,320 Speaker 1: into smaller boxes. There were conveyor belts and fork cliffs, 107 00:07:08,760 --> 00:07:12,240 Speaker 1: and the workers there were wearing strong Arms device. So 108 00:07:12,360 --> 00:07:16,600 Speaker 1: strong Arm is currently in a pilot phase at this facility. Jack, 109 00:07:16,680 --> 00:07:21,080 Speaker 1: this is Josh, Josh, Jack, Are you good yourself? So 110 00:07:21,120 --> 00:07:23,760 Speaker 1: that's Jack Wesley. He's the warehouse worker we heard from 111 00:07:23,800 --> 00:07:27,320 Speaker 1: at the top of the episode. Jack works in the freezer, 112 00:07:27,680 --> 00:07:31,520 Speaker 1: which is maybe the most dangerous part of the Geodas warehouse, 113 00:07:32,080 --> 00:07:34,320 Speaker 1: and he's actually wearing the strong Arm device when I 114 00:07:34,360 --> 00:07:36,520 Speaker 1: talked to him, even in the interview, he said he 115 00:07:36,560 --> 00:07:38,520 Speaker 1: puts it on every day just as part of his routine. 116 00:07:39,240 --> 00:07:41,680 Speaker 1: Jack and all the other workers you spoke with, did 117 00:07:41,680 --> 00:07:45,680 Speaker 1: they seem a little skeptical or worried about this idea 118 00:07:45,720 --> 00:07:48,880 Speaker 1: of being tracked all day by this device. I talked 119 00:07:48,880 --> 00:07:52,000 Speaker 1: to a handful of workers and all of them were 120 00:07:52,000 --> 00:07:54,640 Speaker 1: a little bit worried at first. They had some misconceptions. 121 00:07:54,960 --> 00:07:58,240 Speaker 1: Here's Jack again. When it first came out. You know, 122 00:07:58,280 --> 00:08:01,160 Speaker 1: I heard from some of the other people myself included. 123 00:08:01,200 --> 00:08:04,440 Speaker 1: It had a video camera, you know, microphone things like that, 124 00:08:04,520 --> 00:08:07,920 Speaker 1: but it's just monitoring your movements, make sure you're bending properly, 125 00:08:07,960 --> 00:08:10,680 Speaker 1: and yeah, that's all. That's all it does. So once 126 00:08:10,720 --> 00:08:12,880 Speaker 1: we figured that out, everybody was kind of they were 127 00:08:12,920 --> 00:08:15,160 Speaker 1: more relaxed about it. And you're comfortable with that because 128 00:08:15,160 --> 00:08:18,920 Speaker 1: you feel like that is a benefit to you opposed 129 00:08:18,920 --> 00:08:22,040 Speaker 1: to if they're taking video of your then like if 130 00:08:22,080 --> 00:08:24,520 Speaker 1: you're rolling your eyes or something, they'll figure that. Yeah, 131 00:08:24,560 --> 00:08:26,840 Speaker 1: oh yeah, like, uh, it's not like a chest cam 132 00:08:26,920 --> 00:08:29,160 Speaker 1: at a traffic stop or anything like that. It's uh, 133 00:08:29,400 --> 00:08:31,440 Speaker 1: just making sure you're you're bending properly. We don't want 134 00:08:31,440 --> 00:08:40,679 Speaker 1: any injuries at the workplace. It's worth noting that the 135 00:08:40,720 --> 00:08:44,079 Speaker 1: workers are tracked in other ways as well. The warehouse 136 00:08:44,240 --> 00:08:48,040 Speaker 1: is full of cameras and Geodas also uses a production 137 00:08:48,080 --> 00:08:52,760 Speaker 1: tracking system where workers are measured on how fast they're working. 138 00:08:53,720 --> 00:08:56,800 Speaker 1: People at Geodas make between twelve fifty and fifteen dollars 139 00:08:56,840 --> 00:08:59,280 Speaker 1: an hour to start, but if they work fast enough 140 00:08:59,320 --> 00:09:01,439 Speaker 1: they can get bone to say, up to five dollars 141 00:09:01,480 --> 00:09:04,240 Speaker 1: extra per hour. Wow, So that's a lot of ways 142 00:09:04,360 --> 00:09:07,880 Speaker 1: that they're being tracked. Yeah, and as I understand it, 143 00:09:08,040 --> 00:09:11,880 Speaker 1: this isn't an unusual level of monitoring for the industry. 144 00:09:12,320 --> 00:09:16,079 Speaker 1: The businesses who work in logistics and transportation are really 145 00:09:16,160 --> 00:09:19,000 Speaker 1: data hungry. They're trying to gather as much information about 146 00:09:19,000 --> 00:09:22,920 Speaker 1: their facilities as possible. Here's Mike Conius, he's the chief 147 00:09:22,960 --> 00:09:26,040 Speaker 1: operating officer at GEODAS, and he explained to me how 148 00:09:26,080 --> 00:09:28,520 Speaker 1: he wants to eventually be able to take all these 149 00:09:28,559 --> 00:09:31,679 Speaker 1: different data streams and put them together to learn new 150 00:09:31,720 --> 00:09:34,959 Speaker 1: things about his operations. So I pulled my productivity data. 151 00:09:35,120 --> 00:09:39,160 Speaker 1: It shows exactly the type of speed and productivity this 152 00:09:39,200 --> 00:09:41,720 Speaker 1: person was doing by the minute. We can actually break 153 00:09:41,720 --> 00:09:44,920 Speaker 1: it down by the second. Then you apply the ergonomics 154 00:09:45,000 --> 00:09:48,920 Speaker 1: data to it on the exact same timeline, and you 155 00:09:48,920 --> 00:09:52,080 Speaker 1: can see the motions of the person bending and what 156 00:09:52,200 --> 00:09:54,400 Speaker 1: they're doing, and you can see that right along the 157 00:09:54,440 --> 00:09:57,679 Speaker 1: same timeline as the productivity. And then you can put 158 00:09:57,679 --> 00:09:59,880 Speaker 1: the robotic side on it and you can see all 159 00:10:00,040 --> 00:10:02,120 Speaker 1: that play out. And then all of a sudden, from 160 00:10:02,160 --> 00:10:04,559 Speaker 1: there you can look at, Okay, is there an opportunity 161 00:10:04,640 --> 00:10:06,839 Speaker 1: we have a little bit idle time? Was there some 162 00:10:06,920 --> 00:10:10,199 Speaker 1: lifting there? That created the operator, you know, our teammate 163 00:10:10,240 --> 00:10:13,199 Speaker 1: to slow down and can we correct that. In the past, 164 00:10:13,200 --> 00:10:16,080 Speaker 1: traditionally we haven't been able to see that, And in 165 00:10:16,120 --> 00:10:19,840 Speaker 1: the meantime, Geodas is actually already making some changes. There's 166 00:10:19,840 --> 00:10:21,839 Speaker 1: one place in the warehouse that I saw, and it's 167 00:10:21,840 --> 00:10:25,040 Speaker 1: a conveyor belt. Workers stand alongside it and they're picking 168 00:10:25,040 --> 00:10:27,840 Speaker 1: boxes up and moving them off to the side. And 169 00:10:27,840 --> 00:10:30,160 Speaker 1: what the company noticed was that there were certain parts 170 00:10:30,200 --> 00:10:32,880 Speaker 1: along the conveyor belt where people were just twisting too far. 171 00:10:33,679 --> 00:10:35,840 Speaker 1: So they had a manager go talk to people who 172 00:10:35,840 --> 00:10:39,040 Speaker 1: work on this part of the operation and say, hey, 173 00:10:39,040 --> 00:10:42,880 Speaker 1: watch out for this right now, it's just a coaching opportunity, 174 00:10:42,960 --> 00:10:46,360 Speaker 1: But Geodas says it may also use this information to 175 00:10:46,520 --> 00:10:48,760 Speaker 1: change the layouts of the conveyor belts in the future 176 00:10:49,080 --> 00:10:53,440 Speaker 1: so that people don't end up making these motions inadvertently. So, Josh, 177 00:10:53,480 --> 00:10:55,719 Speaker 1: it sounds like people are pretty happy about this, but 178 00:10:56,520 --> 00:11:01,480 Speaker 1: I don't know. I'm worried that this could go very wrong. Yeah, So, 179 00:11:01,840 --> 00:11:04,120 Speaker 1: both strong Arm and the clients I talked to put 180 00:11:04,120 --> 00:11:07,160 Speaker 1: a very cheery face on this, but other people who 181 00:11:07,240 --> 00:11:11,840 Speaker 1: I've spoken with are really concerned about worker surveillance and 182 00:11:11,920 --> 00:11:15,200 Speaker 1: put this into that bucket, and I think they're worried 183 00:11:15,240 --> 00:11:18,080 Speaker 1: first of all, that just any sort of data you're 184 00:11:18,080 --> 00:11:21,080 Speaker 1: gathering about workers in this situation is going to be 185 00:11:21,200 --> 00:11:23,480 Speaker 1: used to just squeeze a little bit more productivity out 186 00:11:23,480 --> 00:11:26,840 Speaker 1: of them, And workers don't like that because they're already 187 00:11:26,880 --> 00:11:30,120 Speaker 1: working super hard. Yeah. I who wants your boss to 188 00:11:30,200 --> 00:11:33,160 Speaker 1: tell you, oh, you have to work at your absolute 189 00:11:33,160 --> 00:11:36,199 Speaker 1: capacity every time? And also I know what your absolute 190 00:11:36,240 --> 00:11:39,960 Speaker 1: capacity is. And once you're determining just how fast everybody 191 00:11:39,960 --> 00:11:44,160 Speaker 1: can work, you could see employers wanting to punish those 192 00:11:44,160 --> 00:11:46,839 Speaker 1: people who don't work quite as fast as everybody else. 193 00:11:47,320 --> 00:11:50,000 Speaker 1: And why are workers so worried about that? So a 194 00:11:50,000 --> 00:11:52,640 Speaker 1: lot of workers feel like their bosses have plenty of 195 00:11:52,640 --> 00:11:57,400 Speaker 1: power over them already, and giving them another tool that 196 00:11:57,480 --> 00:12:00,320 Speaker 1: they might use to punish them is just not need Yeah, 197 00:12:00,320 --> 00:12:04,080 Speaker 1: it just feels like it's further tipping the balance. And 198 00:12:04,120 --> 00:12:06,880 Speaker 1: the final thing that I heard when talking to people 199 00:12:06,880 --> 00:12:11,079 Speaker 1: about this was that if you're gathering information about workers health, 200 00:12:11,840 --> 00:12:15,440 Speaker 1: there is bound to be a tie in to workers 201 00:12:15,480 --> 00:12:19,960 Speaker 1: compensation claims. Yeah. I can see this being freely contentious, 202 00:12:21,000 --> 00:12:23,320 Speaker 1: especially in a field of work where the rate of 203 00:12:23,400 --> 00:12:26,200 Speaker 1: injury is so high. Yeah, I think this comes down 204 00:12:26,240 --> 00:12:29,360 Speaker 1: again to whether or not workers trust their employers. And 205 00:12:30,160 --> 00:12:32,280 Speaker 1: you know, the people I talked to at geodas said 206 00:12:32,320 --> 00:12:35,320 Speaker 1: they did, but that's not universally true. So what does 207 00:12:35,360 --> 00:12:39,400 Speaker 1: strong arms say about all this? These are not criticisms 208 00:12:39,400 --> 00:12:43,800 Speaker 1: that strong harm has not heard before. The first thing 209 00:12:43,840 --> 00:12:46,600 Speaker 1: they say is that they do not measure productivity data, 210 00:12:47,280 --> 00:12:49,160 Speaker 1: and they have discussions with their clients in which they 211 00:12:49,160 --> 00:12:51,880 Speaker 1: say they do not want the trackers to be used 212 00:12:51,880 --> 00:12:55,280 Speaker 1: to punish individuals. But I also talked to Sean about it. 213 00:12:55,840 --> 00:12:58,160 Speaker 1: He said he hadn't seen clients do anything that even 214 00:12:58,200 --> 00:13:02,760 Speaker 1: got close to problematic, but that if they did, he 215 00:13:02,800 --> 00:13:05,840 Speaker 1: would be willing to take the technology back or just 216 00:13:05,880 --> 00:13:08,880 Speaker 1: cut off certain types of data. And we trust the 217 00:13:08,880 --> 00:13:10,960 Speaker 1: clients to do the right thing with that right now, 218 00:13:11,400 --> 00:13:13,240 Speaker 1: and if we start to see clients not doing the 219 00:13:13,320 --> 00:13:16,640 Speaker 1: right thing, we can further adjust the dissemination of some 220 00:13:16,679 --> 00:13:20,079 Speaker 1: of that information. So it sounds like these concerns haven't 221 00:13:20,200 --> 00:13:23,800 Speaker 1: materialized yet, but it does sound like this would be 222 00:13:23,840 --> 00:13:26,400 Speaker 1: something that labor unions would have a lot of problems with. 223 00:13:27,480 --> 00:13:29,520 Speaker 1: One thing that made my ears really perk up in 224 00:13:29,600 --> 00:13:34,400 Speaker 1: my first conversation with strong Arm was there, claims that 225 00:13:34,440 --> 00:13:42,360 Speaker 1: they worked closely with organized labor. Because I'm like you, Aki, 226 00:13:42,440 --> 00:13:46,520 Speaker 1: I've had conversations with organized labor about technology before, and 227 00:13:46,520 --> 00:13:48,200 Speaker 1: this just didn't seem like something that they would be 228 00:13:48,240 --> 00:13:51,640 Speaker 1: particularly enthusiastic about. So I was eager to talk to 229 00:13:51,640 --> 00:13:54,760 Speaker 1: those unions. And when I asked strong Arm to put 230 00:13:54,760 --> 00:14:00,400 Speaker 1: me in touch with some of them, they balked, So 231 00:14:00,480 --> 00:14:04,680 Speaker 1: what did you do? Well? I tracked down union local 232 00:14:04,720 --> 00:14:08,840 Speaker 1: in Lynn, Massachusetts that took part in a strong Arm pilot. 233 00:14:09,559 --> 00:14:12,200 Speaker 1: The guy I spoke to was Adam Kazinski. He's the 234 00:14:12,200 --> 00:14:15,559 Speaker 1: current president of i U e c W, a local 235 00:14:15,600 --> 00:14:18,560 Speaker 1: two oh one. Adam was a shop steward at the 236 00:14:18,600 --> 00:14:21,600 Speaker 1: time that strong Arm showed up at his workplace, and 237 00:14:21,680 --> 00:14:24,200 Speaker 1: he did say that there were lots of injuries amongst 238 00:14:24,240 --> 00:14:26,880 Speaker 1: the folks who worked there. I was in an area 239 00:14:26,960 --> 00:14:29,560 Speaker 1: that requires a lot of twisting and turning and pushing 240 00:14:29,600 --> 00:14:34,000 Speaker 1: and pulling bench work, sheet metal spot well, and we 241 00:14:34,080 --> 00:14:37,520 Speaker 1: had had high rates of ergonomic injuries in the area. 242 00:14:37,720 --> 00:14:39,720 Speaker 1: And even though Adam acknowledged that there was a real 243 00:14:39,840 --> 00:14:44,480 Speaker 1: problem where he worked, he was really skeptical of this idea. 244 00:14:44,720 --> 00:14:46,680 Speaker 1: He thought that this was giving his employer a lot 245 00:14:46,720 --> 00:14:49,520 Speaker 1: of information that he wasn't sure it needed, and he 246 00:14:49,600 --> 00:14:52,120 Speaker 1: told me that he actually advised other members of the 247 00:14:52,240 --> 00:14:56,840 Speaker 1: union not to participate in the program, which was voluntary. Well, 248 00:14:56,840 --> 00:14:59,880 Speaker 1: when you work with your body, ergonomic data is production. 249 00:15:00,640 --> 00:15:03,320 Speaker 1: By the way, when Adam says armstrong, he means strong arm. 250 00:15:03,600 --> 00:15:07,160 Speaker 1: So you know what, whether Armstrong says that's their intention 251 00:15:07,280 --> 00:15:10,400 Speaker 1: or not, there are clients or potential clients to see 252 00:15:10,400 --> 00:15:15,760 Speaker 1: your value in tracking productions through monitoring people's bodies. He 253 00:15:15,840 --> 00:15:18,680 Speaker 1: just didn't believe that this sort of tracking would end 254 00:15:18,720 --> 00:15:22,920 Speaker 1: up benefiting workers, no matter how was pitched initially. You 255 00:15:22,920 --> 00:15:26,640 Speaker 1: know that I don't have to speculate about why people 256 00:15:26,680 --> 00:15:28,520 Speaker 1: are interested in it. It's because they want to do 257 00:15:28,560 --> 00:15:31,880 Speaker 1: more with less. They want workers to work faster, longer, 258 00:15:32,040 --> 00:15:39,040 Speaker 1: and increase production while mitigating workers. Comps claims, that's I 259 00:15:39,040 --> 00:15:41,880 Speaker 1: mean clearly what this is about to me. You know 260 00:15:41,920 --> 00:15:45,000 Speaker 1: that's surprising given what strong arm told you. Yeah, I 261 00:15:45,040 --> 00:15:49,240 Speaker 1: was certainly surprised. So what ended up happening. Adams says 262 00:15:49,320 --> 00:15:51,800 Speaker 1: that a lot of people didn't want to wear those 263 00:15:51,880 --> 00:15:55,080 Speaker 1: trackers and that those who did soon stopped wearing them, 264 00:15:55,520 --> 00:15:58,360 Speaker 1: and the pilot kind of fizzled well, that they didn't 265 00:15:58,360 --> 00:16:00,400 Speaker 1: collect an up data to tell him anything, and that 266 00:16:00,520 --> 00:16:03,400 Speaker 1: people kind of dropped off before they were able to 267 00:16:03,400 --> 00:16:07,360 Speaker 1: get any good data, and that no one was impressed 268 00:16:07,360 --> 00:16:10,560 Speaker 1: with the program and the way it went down, so 269 00:16:10,680 --> 00:16:18,640 Speaker 1: basically just flopped. I a strong arm about this, and 270 00:16:18,800 --> 00:16:21,840 Speaker 1: they took issue with the idea that it flopped. But 271 00:16:22,800 --> 00:16:27,840 Speaker 1: they did say that they didn't finish the pilot program, 272 00:16:27,840 --> 00:16:31,600 Speaker 1: that they weren't able to kind of gather all the 273 00:16:31,720 --> 00:16:34,920 Speaker 1: data and implement any of the sort of changes that 274 00:16:34,960 --> 00:16:38,600 Speaker 1: they've done in other places, and that basically this was 275 00:16:39,520 --> 00:16:47,440 Speaker 1: um a learning experience for the company. Josh. At the 276 00:16:47,440 --> 00:16:51,880 Speaker 1: start of this episode, we talked about how widespread workplace 277 00:16:51,920 --> 00:16:56,320 Speaker 1: surveillance has become in blue collar industries, but the reality 278 00:16:56,400 --> 00:16:58,880 Speaker 1: is that this is everywhere right and in white color 279 00:16:58,920 --> 00:17:02,680 Speaker 1: work too. Yeah, I think the tension is really across 280 00:17:02,720 --> 00:17:06,760 Speaker 1: the working world in both blue collar and white collar workplaces. 281 00:17:07,040 --> 00:17:09,600 Speaker 1: It's just easier to gather more and more information about 282 00:17:09,640 --> 00:17:13,520 Speaker 1: what your workers are doing and then do something with it. 283 00:17:14,560 --> 00:17:17,639 Speaker 1: The company makes a good case that it can gather 284 00:17:18,080 --> 00:17:20,920 Speaker 1: more data about how you move at work and use 285 00:17:20,960 --> 00:17:24,320 Speaker 1: it to reduce the rates of injuries, but it can't 286 00:17:24,359 --> 00:17:27,040 Speaker 1: get away from the fact that when it does that, 287 00:17:27,560 --> 00:17:31,080 Speaker 1: it's also creating all this information that employers might use 288 00:17:31,600 --> 00:17:36,920 Speaker 1: for other things as well. And I think that fuels 289 00:17:37,160 --> 00:17:40,560 Speaker 1: a real broader vulnerability going on here, which is that 290 00:17:41,000 --> 00:17:44,400 Speaker 1: everyone's aware that there's more and more data being collected 291 00:17:44,440 --> 00:17:50,639 Speaker 1: about us in the workplace, other places online, and we 292 00:17:50,720 --> 00:17:52,919 Speaker 1: know that there's a lot of power in this information 293 00:17:53,080 --> 00:17:56,360 Speaker 1: being gathered and analyzed, but we don't have a mechanism 294 00:17:56,440 --> 00:17:58,760 Speaker 1: to say, hey, let's stop and figure this out before 295 00:17:58,800 --> 00:18:01,679 Speaker 1: we go forward, and so it just kind of progresses 296 00:18:02,200 --> 00:18:11,040 Speaker 1: whether we like it or not. Joshua Bristine, thanks for 297 00:18:11,080 --> 00:18:16,560 Speaker 1: coming on the show today. Thank you. Decrypted is hosted 298 00:18:16,560 --> 00:18:20,520 Speaker 1: by me as Sean When is Our Executive producer, Ethan 299 00:18:20,560 --> 00:18:23,240 Speaker 1: Brooks mikes a show today and Francesco Levie is ahead 300 00:18:23,280 --> 00:18:25,440 Speaker 1: of Bloomberg podcast. We'll see next week.