Interviewer (5:27)

I guess first, can you introduce yourself?

Michael Jeffrey Asia (5:30)

My name is Michael. Currently I'm the General Secretary of the Data Level association, having built the space for over five years now. So I'll basically take you through what data labeling is and I will use a practical example of a self driving car. For example, if you have to have this car on the road working without causing accidents on the roads, this vehicle has to be taught a lot of things. It has to be fed with a lot of information. For example, if this vehicle has to identify, let me say, a truck, an suv, a person, a dog, you know, any other animal, you know, the cabs, the horse wheels, besides the roads, the traffic sign, the traffic lights and what have you, this vehicle has to be taught, it has to be given this information like a small child. So when you have to help it understand that this is a human being, you don't take a picture of a human being, just one person, you know, you have to take millions and millions of pictures. You have people in different heights, different sizes, you know, so that by the time you're working on this, you're giving it a variety. You know what people are. Because if you give it to, let me say, pictures of grownups only if it finds a kid on the road, it's gonna hit the kid. So all these pictures are taken on the roads randomly. Then you're given this information. So you're told to label these vehicles, you know, correctly. And that is why anytime you're doing all this labeling, you have to be very accurate. You have to identify this as a bus. You have to identify this as a suv, for example. This is a truck, this is a trailer. So that it doesn't hit any of this, doesn't cause an accident. So that is why when this information is brought to us, we have to label it up correctly. Once all this is done, the information is put together, then this machine is taught through an algorithm on how to identify all these things.

Interviewer (7:12)

I know that there's data labeling businesses all over the world, but I know that there's a lot of it in Kenya. Do you know why it's such a popular job here?

Michael Jeffrey Asia (7:23)

First of all, Kenya has, I think, one of the best Internet penetrations worldwide, if not within the continent of Africa. I think every homestead, almost every homestead in Kenya has an Internet connection at the moment. Second, Kenyans are learned. Most Kenyans have gone through a very good uneducation system and most of them are aggressive. I think Kenyans, I call it a challenge nation where like all Kenyans want to challenge every system, all Kenyans want to build every system, all Kenyans want to be part of every system. That is why we are like Kenyans can sit down and decide like this is what we need. I'll give an example of the mobile money transfer which was invented here in Kenya, first used here in Kenya. I know most people have never trusted Kenyans because of all that. You go to Uganda, there's a problem. Yeah. And that was a student, not even a graduate, just a student doing such a thing. So Kenyans are tech savvy, we love challenges as Kenyans. We have the best infrastructure in terms of the Internet connectivity. Guys are educated. So like yeah, that is why everyone feels so comfortable bringing their jobs here in Kenya.

Interviewer (8:29)

How did you personally get involved in data labeling?

Michael Jeffrey Asia (8:33)

So after working as a storekeeper for quite some time, I later on joined betting which was a betting farm here in Kenya. And two months later it's closed down. But as a lady I was with in college when I was doing my diploma in a cargo who introduced me to summer. By the time we were together in Betaine she left, then went to summer. Then after Bettin shut down I was like tell me how are you surviving today? And she sent me a lingerie applied and I went to summer school, trained. Then a year later I was absorbed. That was in 2020. But prior to that I had my own challenges as a person financially and my kid had been diagnosed with cancer of the lymphatic system. This is Covid we are talking about and I'm like I don't have a job so I'm supposed to get back to my pocket because flights were not in operation globally so I couldn't afford by then I went talk to someone who borrowed me 1.7 million Kenya shillings. It was an equivalent of around 1700 US dollars. So that is what I use for medication. So when someone came in it wasn't offering a good pay to be honest. It was around US$240 that is on.

Interviewer (9:41)

Gross US$240 per month. Per month.

Michael Jeffrey Asia (9:45)

So I had to pick the job because I didn't have an option. I had stayed at the hospital for four months, so. So it was me at the hospital and me at summer at the same time until my child was actually discharged. Then, now this is the burden I have. Then. Now I need to figure out how to find solutions, you know, to the financial crisis at my place because I need to pay house rent, I need to provide for them, basically, I need to take care of the sick child and so on and so forth. So that is how I found myself at someone. Yeah.

Interviewer (10:16)

What types of pressures are you under when you're doing data labeling? I assume you have to be really highly accurate. Do you have to be very fast as well? Are you paid per task or are you paid per month, per hour?

Michael Jeffrey Asia (10:31)

At Summer, we were paid per month and that is why I took the US$240. Now, if you're given a target, for example, you're supposed to calculate and know, how many tasks do I need to submit in an hour to hit the target. Then there was what you call the occupancy. You know, how busy you are with the tool. Because most of the tools, it was a meta tool called Workplace. And at Workplace, it could deactivate your account or put you on an available mode if you go eight minutes without touching the mouse. So basically you're supposed to be cleaned on your machine for the working period. So we had KPIs and meeting billables. Also, the billable hours that the company had signed with Meta was, you know, a must. So you have to work within a certain time frame. KPIs were set and you have to meet the KPIs.

Interviewer (11:23)

So what types of work were you doing at Summer?

Michael Jeffrey Asia (11:26)

Summer was basically labeling or annotation as they call it. And mostly images and videos. Yeah, you're given a video and they're told to describe a video. For example, you're given an image and you're told to, you know, work on it in accordance, in accordance to, you know, specific guidelines. For example, if you're given, let's say, pictures of people whose, you know, the picture only contains faces that you're told to identify faces, you know, you're supposed to draw bounding boxes around their, you know, faces and label that. So that is basically what I used to do at Summer after I joined Summer. That is when through friends and colleagues, they're like, guys are talking about some other companies, some other opportunities online and so on and so forth. So the only advantage we had was we transitioned to work from home. But it was during that phase that I also came another. I came across another gig that was not a good one, for that matter, where we were, like, expected to annotate pornography. And pornography in this case was like you're presented with a video and you're supposed to put yourself in the, you know, in the minds of the 8 billion people on earth. And you like to put tags on every frame. And by frame I mean every second of that video. So you have 8 billion people in mind. I know if someone is searching for this pornography in Cuba, these are the tags they can use. Like if you're searching doggy, you know, such kind of thing. So you have to have all that in mind. Every time you're watching a video, you put tags, like 12, 15 tags on every frame. So you're supposed to work on pornography for eight hours a day, did the project for eight months. So you understand the toll is something I never want to talk about sometimes because you. It wasn't an easy thing watching pornography for eight hours. Yeah. And for eight months was already. I went for therapy for six months.

Interviewer (13:24)

It must be very sort of destabilizing.

Michael Jeffrey Asia (13:29)

To do that so much because you get to a point where your body can function. You're here even if someone's tipped naked, you don't even feel it. And you're here. You have a wife. Yeah. Who expects a lot from you. Being a young family, you know, a young woman, she also has blood flowing, you know, in her veins and she expects a lot from you intimately. But then you can't like do that. Yeah, it fractured a lot of things that time. And that is how most of the things were lost in the process.

Interviewer (14:02)

Were you specifically labeling what was happening in the video and do you know what the purpose was? Was it for searching or was it for like this pornography is actually illegal, so we can't include it or what.

Michael Jeffrey Asia (14:17)

I think for them it was basically to improve viewership on their websites, for example, because if you went to the Internet and you want to search for certain pornographic video, there are tags you're likely to use to be able to access that kind of video. So basically it was to improve their viewership. From my own assessment, because why would I do 12 to 15 tags on one frame? Is to ensure that even if someone is thinking outside the box, they would still come back to accessing this video. Even if someone was thinking so poorly, they will still come back to this very, very video. That was the main purpose of having all these tags on frames.

Interviewer (14:53)

Do you know what websites these were ultimately for or you didn't have that level of information?

Michael Jeffrey Asia (14:58)

Most of you know, the communication was done on a no reply mail. So you receive a no reply Mail, but it has a link. Then it moves you to a certain site. And what I noticed, they had like three working sites. So today you're working on this one, the next day you're working on that. So you couldn't tell who specifically was responsible, or rather who specifically was giving this work. Then once the work is done, like, your account is suspended, you do not have access to.

Interviewer (15:26)

So in your report, you talk about how you feel like you were training AI companions at some point. Can you talk to me about how you got into that? Was that the same job or a different one?

Michael Jeffrey Asia (15:40)

That was a different one. Because this now was basically chat moderation and not data labeling or data notation, let me say. So when you talk about trying to train these chatbots, you sit down and ask yourself, how does these companies resolve disputes? Because part of the things you're told from the beginning is like, you're not supposed to share your personal information, for example. So here you are, you share your personal information, the system, or rather it's flagged then. Now that is how they narrow down to you and suspend your account. So that means, because again, you still have your messages, there's a message count in your dashboard and that is what they used to pay you. How do you identify the message count without having the messages? How do you prove the message count? How do you resolve conflicts? So it means these messages are being stored for future use.

Interviewer (16:31)

Right.

Michael Jeffrey Asia (16:32)

So, and we are like, we are here and this messages, there are certain strict guidelines you're given that you must have these number of characters before a message can be sent. So that means you are not allowed to send short, very short messages. You're supposed to miss a 10, you know, a threshold of characters on every text message you're sending. And that is why they required someone with a high type and speed or at least 43 to 50, you know, words per minute. And that is where you're like, now, the speed they need is to ensure that they get the very many characters they need within the one minute that you're supposed to submit that text message. So the response has to be within one or two minutes, depending on the site you're actually on. That is where they needed the typing speed.

Interviewer (17:13)

Yeah. Do you think that you were talking to real people, to be honest?

Michael Jeffrey Asia (17:17)

Yes. Because you could feel the human aspect of their conversation. And there are times when, you know, when someone is looking for love, you can tell if this is a real human being or not. And most of the people who are on those sides were elderly people. And I really Doubt even Kenya. We can have people who are lonely there, you know, to that extent of, you know, paying for such services. And to me, I would say, I think we are raised totally different as a people and generally as Africans. And there are certain things you rarely find in Africa, for example, you know, people paying subscription fee to have such services offered to them. Like someone feels slowly talking that they can pay to get company. I haven't seen it anywhere in Africa unless, you know, it's there. But I think they target the vulnerable and mostly the aged people, let me say, the people who have, you know, gone through a lot. Like someone suffered in an accident, for example, lost their partner and what have you. They have had mental issues, maybe, and they need support. They need someone to talk to. You know, those guys are looking for love. And now all these things are integrated on one side. And that is what you're supposed to be responding to. So a target group are not in Africa, to be honest. Yeah. It must be us and Europe.

Interviewer (18:38)

Yeah, yeah. So you log into to this job where you're doing chat, you're chatting with people. I mean, what sorts of things are you, like, asked to do?

Michael Jeffrey Asia (18:49)

This job requires a lot of creativity and fast thinking. Because if I'm talking to a man, I'm supposed to act with a woman. If I'm talking to a woman, I act the man. If I'm talking to a girl, it works like one. If I'm talking to a lesbian, it works like one. So it requires a lot of creativity. In switching of all these conversations, did.

Interviewer (19:11)

You have to be, like, explicit with these people?

Michael Jeffrey Asia (19:14)

Yeah.

Interviewer (19:14)

Like, were you texting them essentially, or.

Michael Jeffrey Asia (19:16)

That was basically what we were doing. Yeah. The guys who come here and they need someone to sex, charge them. And the site provided even pornographic stickers.

Interviewer (19:26)

So you said that having to watch pornography for eight hours a day desensitized you. It was not a good job. This seems like almost another level of that because you're, like, having to participate back and forth. I mean, was. Was this difficult for you?

Michael Jeffrey Asia (19:43)

The pornographic one was a different one, Right?

Interviewer (19:45)

It was a different one, I'm saying. But you did that and then. Then you did the sexting.

Michael Jeffrey Asia (19:50)

Okay, I was doing this the same time.

Interviewer (19:51)

You're doing them at the same time?

Michael Jeffrey Asia (19:52)

At the same time. Because I have a shift between 4 and 9 for the church motorist job. Then I'm like, having the other one after 9 to around 3 or 4am in the morning. So that is how I used to work. So I am experiencing these two jobs having These two jobs with different experiences, but all of them are so demanding.

Interviewer (20:09)

Yeah.

Michael Jeffrey Asia (20:10)

And I have to do the summer shift during the day. So basically that is how we ended up some of us never sleeping for at least. We used to work for at least 18 hours a day. And that is why some of us are still suffering from insomnia to date. And when I told you I have gone for like three days without sleeping, basically, I mean it, because sometimes you're like, there, you switched on the lights, but you can't sleep and you realize it's already 5am in the morning and you can go to bed at 5am so you're like supposed to pick up something else and do it. So most of the guys have been in this space have a problem with sleep. Most of them I happen to go to. There is an institution called Faraja Cancer Support center in Parkhouse where, like, I engaged one of the therapists who really helped me big time. And I have always said Fariger Cancer Support center has been of immense help to me as a person because some of the things they took me through to help me, I don't think everyone would have done that, but it was one of the best services I ever got for that year because they stood with me, they were there for me. And I think I got to access them because I was a caregiver to a child with cancer. That is how I actually got to interact with them. And as I was like, no, I need a solution to this. Yeah. So I take my child, I go there for therapy as a caregiver of a cancer patient. And at the same time, I have to explain to them this is what I'm going through at a personal level now. So that is how now I got time.

Interviewer (21:41)

Did any place that you work for ever offer you any sort of like mental health support at all? Did they even talk about it ever?

Michael Jeffrey Asia (21:52)

No. At summer we had that. I wouldn't say it was in there, but sometimes you see you go through problems where like, even the therapist doesn't want to tell you. They don't even know what to tell you. Not to look like I'm looking down upon them, but like, okay, the issues you go through, when you go there explaining it to them, they also shocked. So you're like the one who doing counseling to them, not them doing it to you. So sometimes there was that gap because now they did it from a general point of view, not from a worker point of view. Because what I'm experiencing is not what they have experienced. You see, like, I'm trying to advise you on how to curb drugs, for example, and I have never used one. How the effect of, you know, cigarettes, you know, affect someone and yet I've never smoked. I can't tell you how it tastes unless I test it. So for us, I felt like those guys needed to have a space because they used to do it from a general point of view. But if they labeled data, for example, and not just labeling them, but a graphical for that matter, they would understand what we talk about. So personally I would have suggested they go through data labeling first as part of equipping them with the knowledge of what is in the pipelines or what is happening to these people for them to be able to advise people accordingly.

Interviewer (23:09)

Do you regret doing this job or did. Do you feel like it's something that you just needed to do in order to, you know, provide for your family and your child?

Michael Jeffrey Asia (23:20)

I didn't have an option. If I did, I could, I couldn't have done that, but I didn't have an option. I really had a burden of, you know, financial burden that I needed to sort at that time. So the best I could have done is just accepted the job. So I think that is the vulnerability that most people are going through and that is why most of these companies are taking advantage of, you know, some people. Because under normal circumstances, I don't think you take that job. Today I wouldn't take it. I wouldn't take it whatsoever because I know the damages that the job can actually bring to any human. And also that is the reason as to why we formed the Data Labelers Association. We need to address some of these problems and I will remind them that we're here and they will feel our impact. Unless they do things the right way, I'm going after them. And the good thing is I'm doing from a point of experience, not assumption. I have been through this. I know what I'm talking about and I know how to approach it. So I know today they might try to ignore who we are, but at one point they will call us on these tables, we will sit down with them because we will ensure that we have the right protections in place, the right policies in place, and we must be part of the policy making process. We are not going to be left behind because we understand the space better than any other person. Because I went through that mess, I understand what it is and I understand what can be done to find a solution to this problem. Yeah.

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Interviewer (30:03)

So let's talk about the Data Labelers Association. How long has it been running for? When did you start?

Michael Jeffrey Asia (30:11)

Was through a disc, just a conversation with friends and colleagues. We started the discussion in around December 2023. So it was after one of us did our research on AI Ham that she came across all those challenges. And like, no, we need to address this because some of us never knew that we have so many companies working here in Kenya. Like, through the research, she met someone who, you know, works for Netflix. You know, as a captioner, like Netflix, as employees in Kenya, how we're doing captioning, like, how they're like, something must be very special about this space. And they're like, we need to find out. Because every other complaint or every other issue people raised was basically a violation. We are like, no, we can't continue. One thing that we believed in was like, most of the people are doing this job are young people. And the destiny of this nation depends entirely on the opinion and the contribution of its young people. So if these young people are ignored, future is lost. And we are like, we won't lose our future.

Interviewer (31:18)

So what, what sort of, like, changes and reforms are you fighting for?

Michael Jeffrey Asia (31:23)

Number one, we basically need protections around this space because our labor laws were amended in 2007 and, you know, digital jobs were not like, you know, that clearing our market as such. So there were no protections for that. And that is why I feel like the tech companies are taking advantage of that loophole. And that is where we are like, no, the process of, you know, coming up with such policies and laws, you know, takes time. And we were like, we came up with a model contract, for example, and code of conduct that we will be launching officially, should be next month, but that will be communicated in advance. And we really need to, like, find immediate solutions to long term problems. Because if we stayed here waiting for such laws to be put in place before they even take effect, it will take quite some time. And we are like, no, we need to find working measures to ensure that we do not. We stop the violations are working in a better environment. Yeah.

Interviewer (32:28)

So are you focused on signing up the people who are actually doing these jobs or are you. You're trying to get people to join your association.

Michael Jeffrey Asia (32:37)

We already have 870 members as of now. And we are like, this is the time we need to interact with our members now, this is the time we need to like let them know who we are, what we do and why we have decided to do this. And that is why we have to arrange everything strategically. We have every other thing arranged strategically for that. And that is why we're not talking about it. Because we do not also want interference from external factors. So.

Interviewer (33:07)

Do you think that this is a type of job that can be reformed? Like, do you think that if you fight companies, is this a job that if there was better mental health support, if they paid more, if these sorts of changes were made, is it a good, is it a job you think people should be doing?

Michael Jeffrey Asia (33:28)

Basically we need technology. There's no doubt about that. We need technology, but it shouldn't come at a human cost. We can have this done the best way. What is so hard with offering mental support to the people working on graphic content? You know, clearly they need it. Is it so hard to provide. Question. If this job was done in the U.S. for example, will they still do what they are doing here in Kenya? Would they still give the pay they're giving? Again, because we are, we have friends and guys, we've been working on several projects in the us, in the Philippines and so on and so on. We are like someone is paid during training. In Kenyans we self train and we are never paid. But In Philippines and US for example, someone is being paid $30 per hour for training tasks. In Kenya we are paid 0 for training tasks. Someone is paid $50 per hour. Here we are paid 0.01 per task. It doesn't make sense. Why this discrimination? If they can pay people in the US and in Philippines well, that means they can pay people in Kenya well. So it's not a hard thing. It's not that complicated. Just a matter of let's do things the right way. And I have a problem because I don't know if doing things the right way is a crime in some countries. Is it a crime for example in the US to do the right thing? No. Then why are they doing this in Kenya? Why in Africa? Why? What went wrong?

Interviewer (34:56)

Right? I mean it's taking advantage of the company or the country. It's very extractive, which is not right.

Michael Jeffrey Asia (35:03)

It doesn't have to be a win lose situation. We can have a win win situation. It's not a claim having a win win situation. The business can still thrive, the business can still take care of itself even if the win win situation.

Interviewer (35:15)

Are there any companies in Kenya that you know of? That are doing things the right way.

Michael Jeffrey Asia (35:23)

I don't. I haven't interacted with any, to be honest, for the following reasons. One, we have Summer in Kenya and Sama in San Francisco. Are you being paid the same way? Are people being compensated the same way? Are you doing the same job?

Interviewer (35:37)

Of course not.

Michael Jeffrey Asia (35:38)

And we want this nonsense code, you know, the issue of, you know, the minimum pay. This nonsense has to. Because we can be doing the same things. They're using the word minimum pay as a, you know, a tool to try and, you know, enslave these people and mistreat them. It's not right. Yeah. Secondly, we have, like, we keep asking questions. Why would someone be paid per hour, $50 per hour? And what we are saying here, fine, we do not want the 50, but can you at least move it between 5 and $8 per hour? That is what you're asking for, at least on the minimum side, Right? On the lower side, we're not asking for 50, but we are like, can you actually start from between 5 and 8 per hour? Let's start from there.

Interviewer (36:22)

Did. Did you have bosses? Human bosses?

Michael Jeffrey Asia (36:30)

Let me not call them both the puppets, if I might, to use the right one. Call them puppets because 1. And sometimes I even ask, because every time there is an issue, they want to implement some, let me say, draconian changes. You see someone telling, it's the client, it's the client, it's the client. So who is this client? So you can't talk because it's the client. The client has to kill your people just because it's the client. Now, we can't work that way. We must have the right approach to issues if there are changes that are supposed to be affected. Because there are times when today someone comes in and, like, you're supposed to be working for six hours. The next minute, you're supposed to work for 10 hours. The next minute, you'Re like, you'll be adding 15 minutes to your time because so and so is absent. Okay, how is that my business? I never signed a contract with someone. So if someone is absent, it's about the company to look for a way, you know, to find a way to actually, you know, compensate for the billable hours. Not using me because I'm being used for the. To, you know, fill the billable hours, and yet I'm not being compensated for the same. I really had that issue before I left. Summer, you're told today everyone will be adding 15 minutes for that, for the same period of time. 15 minutes on every day shift And I was like, we are 200 people. 15 minutes for 200 people. How many minutes are those for a whole month? So the company has met, you know, the billables, the clients once, but why are they not compensating people? And then you see people, you know, changing cars day in there. Today somebody's on a BMW, tomorrow somebody's in a Benz. And you're like, wow, well, can we.

Interviewer (37:59)

Talk about that about the client? Because you work for Sama. There's a bunch of companies that you work for. People in the United States have never heard of these companies ever, except the people who've done a lot of research. But you're not really working for Sam. You're not really working for these companies. You're working for the big tech companies. Ultimately, how do you think about that? Because it's almost like this level of abstraction between the two, where it's like you are doing work for some of the richest companies in the world, but you're contracting with these random companies that spin up in Kenya and then they take your labor. They probably make a lot of money from these companies. I think the whole system is one where it's like big tech is at the top. And then you have all of these weird contractors in the middle that are taking advantage of you. And really a question but like, sort.

Michael Jeffrey Asia (38:57)

Of, what do you.

Interviewer (38:58)

How do you think about that?

Michael Jeffrey Asia (39:00)

First of all, as far as I'm concerned, for the very period I worked in summer, for the three years I was working for Meta, and I can say this more than 100 times, I can prove this more than 100 times, I can even prove the projects I worked on and even whoever was in charge of that project from the Meta side, not from this side, I have, you know, that evidence with me. So. And I will not be afraid to share that kind of information. So I know I worked for Meta under some source, I worked for Mentor, and I can prove that one meta works on a platform called Workplace. There is any other platform called workplace anywhere that is not mentors. So I worked on workplace for the three years I worked on a project called GPL6. GPL6 was meta. Purely, there was nothing else we, we need to understand. And it was so strict that we got to a point where like, not everyone would attend client meetings, but at least we had access to certain information that they didn't know we accessed. Sometimes there is a leakage where like someone sent the wrong document to you. You're like, ah, this is what we are doing. This is what we are Getting, you know, interacting with. So this is the client. They're like. So he worked for mentor. Worked for mentor personally for three years under some assets. So I can prove that. Anyway, so for anyone who is working under BPO and is working on workplace, please, you're working for Sadma, you're working for Meta. Sorry, Just understand that it's as simple as that.

Interviewer (40:29)

Yeah, yeah. But Meta doesn't hire you directly because Meta doesn't want to get its hands dirty.

Michael Jeffrey Asia (40:37)

It's because they want to run away from the legal responsibilities.

Interviewer (40:40)

Exactly.

Michael Jeffrey Asia (40:40)

And you know, they will never run away.

Interviewer (40:43)

Yeah. So in the Uber ride over here, I was talking to the driver and she's also a data labeler. How many people do you think are doing this right now? Like, this is a pretty common job here.

Michael Jeffrey Asia (40:55)

15,000. Because let's start from where, like our group has several trainers. For example, we have guys who had like a database of 30,000 trainees. That is one person dealing with 30,000 training. 30,000 people. And we have like eight of them. So even on, you know, on a ratio of, let me say 1 to 10,000, that is 80,000 already. So we have a database of close to 200,000 people that were trained specifically for RemoteTask. Not even any other place. You're not talking about BPOs specifically for RemoteTask. Those are the ones we can access on our end as the 10 people. What of the others? Other people are trainers. And yet you've not even reached out to them.

Interviewer (41:39)

What are BPUs?

Michael Jeffrey Asia (41:41)

You know, BP is a physical setup here. Physical setup here, let's say in catal.

Interviewer (41:46)

Okay.

Michael Jeffrey Asia (41:46)

That is a bp. Yeah, yeah. Business processing zone.

Interviewer (41:50)

Did you ever have to look at violent content?

Michael Jeffrey Asia (41:56)

Pornograph was one of it. Because I viewed a 13 year old ankle session and it has never left my mind. 13 year old on a hardcore session.

Interviewer (42:10)

It's really bad.

Michael Jeffrey Asia (42:11)

Yeah. And we're like.

Interviewer (42:14)

And so you have to report that.

Michael Jeffrey Asia (42:15)

And say you can't report that. You're supposed to put tags on that. Yeah, yeah, yeah. So you don't know who to report to. You don't know the channel you're supposed to. And you have another slave tool called, you know, there's some monster called NDA. The NDA is a slave tool used to enslave people not to speak about what they are going through and going forward. We have a feeling that these NDAs should be drafted with everyone present, not the companies alone. You can't tell us that you're not supposed to speak about the violations. We will Speak about them. Unless we sit down. Like, this one is not right. This is right. I have a sample of an NDA, if I'm not wrong.

Interviewer (42:59)

When did you decide that you would talk even though you were subject to.

Michael Jeffrey Asia (43:02)

These NDAs this time? I'm very much ready for any legal battle anyway. But we are like, no, we are not going to keep quiet. This is us suffering, and we can't suffer in silence. This is not the colonial period where, like, now you're supposed to submit your. No, no, no, no, no, no. I don't have a right to speak against any violation anyway, and that is what I'm doing. And I said, I don't care if they, you know, they suspend all my accounts on, you know, all these platforms. I don't care. After all, I survived until I came across these jobs. I had survived for all those years without these jobs. I would rather speak, let them suspend all the accounts.

Interviewer (43:43)

What did it feel like? Like you were kind of training your replacement in some way. Like, that's. That's what the job is. You are training this robot to be more human, and you're training this robot.

Michael Jeffrey Asia (43:58)

To.

Interviewer (44:00)

Like, be a companion for other people. In the case of the, like, AI companion stuff must be weird.

Michael Jeffrey Asia (44:10)

No, it's kind of funny because I really suspected this when I noticed that some of these messages are being stored, or most of these messages are being stored. Why? The reason I said it earlier on is, like, how do you handle a conflict? Because these messages have codes. So they use the codes to access the messages. So if today I shared a screen where I'm, like, chatting with the user on the other side, and they can use the very code just next to the message to know who sent the message. Because the system generates the codes. Yeah. So now these messages are being stored because how do they resolve conflicts? How do they pay you?

Interviewer (44:50)

Right.

Michael Jeffrey Asia (44:50)

Because if they pay after two weeks, then that means they can refer to the number of messages you sent. Yeah, yeah. And they can certify that you send these messages. Then if you share a personal information, they still can know you. You shared your personal information. How do they know? Yeah. Then there are issues, like, let me say where you're. Like, there are times when, if you log into any platform and you start chatting them, there are those immediate responses that just come from the bots question, where did they get that.

Interviewer (45:21)

From you?

Michael Jeffrey Asia (45:21)

So that is how it started. AI can never be AI without humans. And for me, I've always said this, and I will say it repeatedly, it's not artificial intelligence. It's Africa intelligence. Most of these dirty jobs and most of these jobs have been done here in Africa. Guys in Africa. I have been there trying to like, I would say I've been, you know, software developer without even going through school for the following reasons. One, when you're doing piloting, for example, you're given a dummy website that you need to work on. But you're like, the clan brings the job, post it on that site. And you're like, now what do you think should be added on this? What do you think should be removed? What do you think should be like included? And you know, so you're like, here you're given the tool, you're like going through it and you're like, please add escape button here, Please add this here, please add this here. And like, once the tool is functional, that is when communication stops. Question, what happened?

Interviewer (46:16)

So I read an article also written by a Kenyan that was titled I don't write like ChatGPT. ChatGPT writes like me. Did you see that article?

Michael Jeffrey Asia (46:27)

Yeah, I came across it, but I never went through it fully.

Interviewer (46:30)

Yeah, I mean basically his point was that now when he writes online, when he like writes on LinkedIn and places like that, he is accused, he's accused of using AI. But that's because ChatGPT is trained on Kenyan, the way to set Kenyans right.

Michael Jeffrey Asia (46:46)

And this is Kenyan English. So I think it's a problem most people are facing currently. And you see now that's when you say we train our own death. Now there is someone training his own death. So we trained ChatGPT, it's fine, but now it's killing us slowly because everything you're going to write will be AI generated and we can never run away from that. And the reason for this we don't know.

Interviewer (47:13)

Is there anything else that we haven't talked about that you think people should know?

Michael Jeffrey Asia (47:19)

The most important thing is about Data Labelers Association. It is one of the organizations in the country and we are not just planning to remain here in Kenya. We are going global. That is our target and I believe we are the first one also to come as such an organization that protects the welfare of the data labelers worldwide. We've seen so many people try to copy that and we're like, we must go global because we need to address these are global problem. We don't have this locally, we have to address it globally because of global problem. We don't want our friends and colleagues in Brazil, for example, to go through the same. If this change has to be effected. It has to be across the globe, everywhere. And that is why we are here as DLA and that is like our main objective as a people and as an organization. So DLA is global. Feel free to register as a member anywhere in the world. You would love to meet our global members any day. You would love to go to Brazil and meet our members in Brazil, in Ecuador and Venezuela, so to speak.

Interviewer (48:31)

Thank you so much. Thank you for your time.

Michael Jeffrey Asia (48:33)

Thank you for your time.

Jason Kebler (48:38)

Thank you for listening to the 404 Media Podcast. If you enjoyed this episode, please like and subscribe. Leave a comment, tell your friends about us, etc. We'll be back in a few days. This episode was mixed edited by Alyssa Midcalf from Kaleidoscope this time of year everyone talks about going dry, but at Athletic Brewing Co. We're skipping that because we prefer going athletic, which isn't dry at all. From crisp goldens to hoppy IPAs and limited releases in between, you'll find something.

Michael Jeffrey Asia (49:10)

That fits your style. Every single non alcoholic brew is packed.

Jason Kebler (49:13)

With flavor and the same craft experience you love.

Michael Jeffrey Asia (49:16)

So yeah, you could call it dry.

Jason Kebler (49:18)

But there's really nothing dry about it. Find your new favorite near beer@athleticalbrewing.com Athletic Brewing Co.

Michael Jeffrey Asia (49:24)

Fit for All Times.