There are a lot of tasks that are too complicated for computers to complete automatically, so they require human resources to complete them. Therefore, companies headed by amazon use network outsourcing platforms to find cheap outsourcing workers to complete these tasks. Programs and algorithms are cruel to these outsource workers, but there is also a positive aspect to these jobs is that the outsourcers gain income and experience, so the author (two senior Microsoft researchers) spent 6 years investigating and interviewing outsourced work in the United States and India. The authors have concluded that in the future, the company needs to establish a platform that is more friendly and humane to outsourcing workers, and the government should also introduce policies that are more friendly to outsourcing work and protect the rights of workers to protect work.

First, ghost work is different from other typical jobs related to the gig economy, such as Uber and Lyft drivers. You can actually communicate with someone. Ghost work refers to workers who caption photos, tag and delete inappropriate content worldwide. It is difficult to see them in real life, that is, some behind-the-scenes workers who are difficult to reach. And these jobs are difficult to directly apply to computers, have created certain employment opportunities. Thousands of people use these jobs to make money. The article mentioned that in the United States, 1.2 million to 2.5 million people participated in ghost work. A study by the J.P. Morgan Research Institute shows that between 2015 and 2016, 4.3% of the American adult did at least one online platform economy job, and this data will increase in the future. But on the contrary, in the face of inherently limited work tasks, especially when the number of high-quality jobs is very scarce, it is difficult for workers to choose high-quality jobs, so the treatment of workers really becomes a difficult problem. In the face of this situation, I think it is a challenge, and it can also impact the internet environment. And from my perspective, I think there are two aspects can be changed to improve those workers life.

1. Although ghost work is mostly done by individuals, those people are actually a network. What API requesters use to hire workers simply hides the connection between them. The API allows requesters to hire one or a group of workers without showing you who they might be connected to. But workers can gather in online forums to provide each other with social support, make a difference, and share their experience about how to find a good work. New groups will definitely give birth to new ideas and bring new business opportunities.
2. With reference to history, we find that the collective fate of workers is a moral and political issue and should not be determined by market forces. Consumers and platforms should not obtain cheap platform services at the expense of workers. We also hope that the situation of workers can be improved. This requires the platform to be transparent so that both parties at work can understand and reduce mutual friction, thus, we can increase workers’ rights and interests.

What caused the unfavorable conditions for workers in ghost work？

How can the platform be improved to make the platform more transparent？

With the development of the network, will there be more full-time staff to switch to ghost work?

Read More

Summary:
Ghost Work by Mary L. Gray and Siddharth Suri talks about the invisible human labor powering the seemingly ‘automated’ systems. It talks about the opaque world of employment and how this shadow workforce is what powers the so called intelligent systems and is largely responsible for their seamless working. There are hundreds of millions of invisible people who work online through Amazon Mechanical Turk, CrowdFlower and other crowd-sourcing platforms for a meager sum. Most of these workers have a Bachelors or a Masters degree, who might or might not be employed full time. They join these platforms hoping to make some additional money along while also hoping to get a sense of community. Most of these workers are from the US and India as it’s easier to get cheap labor in these countries. These crowd sourcing platforms offer labor as a service where the laborers are hired using an API which makes it extremely convenient to filter these crowd source workers according to their ‘qualifications’, evaluate their work, collect their answers and pay them, all in a very short amount of time. A major implication of the API is that the workers are stripped away from their identity and are only identifiable by their unique identifier. Although, this workforce is essential to solve the problem of the “last mile”, the use of APIs create a distance between the parties involved rendering these workers in between as ghost workers.

Reflections:
It was an interesting read because it talks about a major workforce that we hardly get a chance to interact with. As computer scientists, we work with Machine Learning models without appreciating the ‘ghost work’ that goes in to build a “gold standard” dataset. For example, the much used resource, ImageNet, developed by Fei-Fei Li of the Stanford Human-Centered AI Institute, involved 49,000 workers from 167 countries for accurately labeling 3.2 million images. ImageNet has been utilized by computer vision researchers to build state-of-the-art image recognition algorithms but hardly any of the works have acknowledged the amount of work that went into captioning the images. Companies like LeadGenius and Amara are attempting to bring about a change in how these ghost workers are treated. They deviate from the traditional business strategies as they hire workers only after a rigorous interview round and additional tests conducted by senior workers. They offer a paid video orientation session and after a 90-day trial period, the workers might also become eligible for an 8 percent hike in their hourly pay, subject to certain minimum requirements. Amara gives their employees the option to opt out of projects they find repetitive and choose from a variety of projects, unlike other platforms where the content is pre-decided and the workers have no autonomy. These companies should be appreciated for attempting to bridge the gap between mindless ghost work and the kind of that takes into account the creativity and the interests of an individual worker.

Questions:
All this makes me think about the steps we can take to better acknowledge the essential work put in by these crowd-source workers. Does this work come under employed work or volunteerism? How do we categorize this work and should we consider this form of work as formal employment? These are essential questions that need to be discussed.

Read More

The introduction talks about what is the ghost work. People who are hired by APIs to do some work which cannot be solved by artificial intelligence are called ghost work. They may determine whether some posts contain adult content or whether the person log in to the account is the account holder. These people are like ghosts since none of the APP users or the programmers do not recognize their appearance. Chapter 1 mainly discusses about the occurrence and development of ghost work. MTurk was built when amazon facing problem with correcting e-book information. Afterwards, they used this API to hire students to do the job and more companies paid them to get similar service. With years of development, the API can let workers do macro-tasks under the leading of full-time employees. However, there are also problems in such ghost work. The hired workers can hardly protect their own profits and companies can hardly find the cracker when issue occurs.

Reflection:

This kind of ghost work is beneficial for both the companies and the workers. The workers from poor areas can make profit doing such kind of job. In the example, a skilled worker can make salary of $40 per day, which is relatively high in some areas, for example, small towns in China. Also, this kind of job can be completed without time and area limitation. This means housewives or retired people can do such kind of job.

In the meanwhile, companies can make profits too. Due to there is no time and area limitations, companies can always find cheap labor.  This means companies can save expanse on hiring and make more profits. Because they can hire people all over the world, they have workers work in different hours in the day. This is similar like they are hiring 24-hour workers, which means more profits and better user-experience.

However, this kind of ghost work contains risks too. When problems occur, companies can hardly find the crackers as they are hiring too many workers without knowing who they are. Also, human make mistakes. As a result, these hired workers are likely to make mistakes when doing tasks. One more points is that the companies do not know the background of the hired people. Sometimes the hired workers may be really bad. In the worst case, they can hardly understand the words on screen and the results will be not trustworthy.

For the employees, no one can ensure their rights. When companies refused to pay their salary, they can find nowhere to ask their salary back. Also, when they hardly solve some problems, they may find someone else has solved these problems and they can get no salary. One more point is that it cannot be ensured that there are tasks whenever. There may be limited tasks in some workers’ working time, which will make these workers receive limited salary.

Questions:

Is there any policy currently to protect the users of these APIs, both companies and hired workers?

How can the workers protect themselves when the companies refuse to pay their salary?

How to deal with mistakes caused by the workers? Any remedies or punishments?

Read More

There is a lot of work going on in the present technologies with out us knowing about them like identifying hate speech or abusive speech etc. All this work is done by a computer software which we call it as Artificial Intelligence. It is hard for a software alone to classify all the possible scenarios in the world to hate speech or not. So, we train the software by providing it with some sample data and labels telling this sentence is hate speech and this is not. This data that is required by the software is provided by humans, so for a software to evolve like a human robot it requires the intervention of humans for it to develop. The chapters provided explain how these data labelling is achieved and how did jobs like crowdsourcing and Mtruck developed in the course of 10 years. It also explains how these jobs help software systems to evolve, and how these jobs are paid on contract basis. There are no legal laws that bind these jobs to provide basic necessities like health insurance, food etc., the pay for the job is decided by supply of labor for that job and in some cases the pay will be less than the minimum pay required. The chapters also explain how macro and micro jobs get crowdsources for very low payments making the jobs expand to any kind of area.

The chapters brought about interesting topic on how crowd source can be used for very low prices. And it is also interesting to see people making living out of these kinds of jobs. It was true that the jobs are interesting and not mundane as you change your job for each different task which makes your like non mundane. The chapters didn’t explain how the crowd source work is validated, which I am interested to learn about. It is interesting to learn that we can break a big task into small micro or macro tasks and get them done at a very cheaper price using crowdsourcing. It was interesting to learn that there are internal crowd source jobs that exist in companies like Microsoft, Google etc.

1. Will the jobs like these develop more and cause full time jobs at stake as these kinds of jobs can be used for any areas like education (projects, assignments) and even macro tasks in the big projects?
2. How are people making a living out of these jobs by getting paid less than minimum wage, there are people who just do these jobs?
3. How is validation of these jobs getting done, how can you make sure that the task completed by the crowd source worker was good?
4. As the crowd source jobs are helping create automated software’s which will remove jobs of the people who are doing it as of now, it will also remove the jobs of simple tasks in crowd sources too as once the software is developed to uncover corner scenarios we need experts which only few people will be eligible. Is crowd source creating jobs or removing them?

Read More

There is a group of people who comes from a different state, even different time zone, doing some repetitive but important tasks which enable the APPs more intelligent. For example, block inappropriate photos from the website, manually compare photos of Uber drivers and so on. Their job is not full time, with a low salary, and even the work opportunity is unstable. The author defines this type of work as ghost work. According to incomplete statistics, the number of these workers are still increasing. However, this type of work has no guarantees, no bonuses, no promotions, and the number of jobs is limited. Based on the investigation, there are various reasons that this group of people would like to be a ghost worker. For example, they don’t want to leave their family, they don’t want to be bundled by a full-time job, or they need good experience to show up on their resume. This book mainly demonstrates the research result on this booming work and the standard of living of these ghost workers. The author also indicates that even though Artificial Intelligence is getting prevalence now, the last mile between what humans can do and what robots can do is still large.

This chapter reminds me of another news that I read before which revealed a scam. There are some “technology companies” claim to be able to solve ransomware malware while actually just negotiate a ransom with hackers, and then charge their customers’ far more money than the ransom. The reason I thought about this news was that the scam was deceived in the name of technology. However, this news may not have much to do with ghost work, but there is a lot of news that some AI companies actually hire cheap staff to perform manual operations to make their products look smart, and I think this is not different from what the above scam did. Nevertheless, I am only discussing a very extreme case, just because it reminds me of the news that I saw. Compared with these events, what ghost workers have been done reflects more positive. I would consider what they did was made up of the last mile between humans and AI. Regarding of the Uber driver’s case, ghost workers only add manual recognition when the driver changes significantly and the machine cannot recognize it. We can blame it as immature, even “semi-AI” technology, but we can also treat this kind of work as part of AI work once we acknowledge the insurmountable last mile problem. Besides, think about the job opportunities provided for those bunch of people, think about the convenience and efficiency provided by ghost workers. I would rather consider these as a win-win strategy. Yet this win-win situation is established on the premise that AI technology is not mature enough, the unemployment rate is high, and society has sufficient demand for this type of work.

There is a more negative effect that we have talked about during the class, however, I would prefer to discuss from the perspective of people who need these jobs. There must be a reason for these jobs, the author already introduced the original of these works and the benefits of this mode of work. However, with the progress of society and the development of science and technology, how this working model will change is still unknown. We shouldn’t just see the immediate benefits without considering long-term development. Based on these, I would like to raise the following topics that can be discussed:

• How would you predict the future development of this working model?
• Attitude expression should be based on different perspectives and from different positions. What is your perspective?
• Based on your perspective, how do you evaluate the pros and cons of ghost work?

Read More

Summary of the Reading

The authors talk about a hidden or invisible power named as a Ghost Work that work side by side with software and AI systems to make sure that data is accurate when the AI system fails to do so. Users of systems like google search, YouTube, Facebook, and other applications don’t know that there are people working behind the sense to make sure that the internet is safe. There are thousands of workers that work on demand on a specific tasks in a form of projects that eventually either built a product or help to make sure that the software is working correctly. The author called these workers a shadow workforce and their work as a Ghost Work. The author suggesting that 38% of the current work will turn into ghost work in the future because of the advancement in technology and automation. Companies like Amazon are assigning people tasks to people to manually inspect some content like hate speech in Twitter or inappropriate pictures. Such tasks can’t be automatically inspected by AI systems. The human being in the loop when the AI can’t do the perfect job. The way workers can get tasks is competitive. The workers must claim their tasks using a request dashboard and it is first in first serve. The mix between human and computer computation is called crowdsourcing, microwork, crowdwork, and human computation. The ghost work doesn’t follow employment laws, or any government regulation and it is cross boarders and the countries how do the work more are US and India because of English language fluency. Work can be micro tasks like deterring if a post on Facebook is a hate speech or macro work like ImageNET project that labeled millions of pictures to be used in AI systems. Ghost work is also known as on-demand gigs economy and there are 2.5 million adults in the United States that participate in this kind of work. Amazon was one of the first companies that depends of ghost work and have built MTruck software handle its work load to give work to thousands of people to correct spelling or typos or any incorrect information. Ghost work is important to label data to make better prediction for algorithms like machine learning, machine translation, and pattern recognition. As automation advances people might start losing their jobs but the ghost work will be more requested and that can balance out the need for the workforce.

Reflections and Connections

What I found interesting about the reading was the way people around the world are working together on tasks that eventually produce a product or service and to make the internet safe. My understanding was companies uses AI systems to flag content and then employees or contractors but didn’t know that people across the globe are working together on task. I also found interesting that the interaction between human and the AI systems will continue which can decrease the concerns that the machine will take over people job in the future.

I agree with what was mentioned in the paper regarding the fusion of the code and human and the future of the workforce as they will turn more to invisible or ghost work.

As GitHub acquired by Microsoft, I think the software development will also follow the same patterns by assigning tasks to software developers across the globe to build software modules that will participate in building large scale applications.

Questions

• What things we need to put in our consideration while doing research in the AI area that might affect human in the loop?
• How do we build software systems that can automatically take the feedback of manual inspection or tagging and adjust its behavior for similar incidents?
• What is our role as graduate students or researchers to shape the future of the ghost work? 

Read More

Automation is taking over the world and a vast majority of people are losing their jobs! This might not necessarily be true. As the boom of automation and AI increases, there is a huge, silent effort from humans behind making this possible. Termed ghost workers, they are the people who “help” machines become smarter and make decisions that a machine is not capable of making. The paradox of automation’s last mile refers to humans who train AI, which ultimately goes on to function entirely on its own thereby making the human redundant. The phase in which human input is required to assist AI to become smarter is where a hidden world of ghost workers work day and night for meager pay. This human labor is often intentionally hidden from the outside world.

I agree with the author in that the current smart AI systems which are required to respond within seconds often require inputs from humans to help solve issues that are too complicated for their AI “mind”.  Human discretion is almost always required to gauge sentiments correctly, identify patterns or adapt to the latest slang. These are things that the computers would not be able to decipher on their own and would require human intervention to solve real-time problems.

The consumers, however, remain until today, unaware of the fact that a human is actually involved in the transaction, behind-the-scenes. Worse, the conditions of work and pay are not widely known as well. This came as a shock to me. The current conditions could potentially lead to the isolation of ghost workers. Treating ghost workers as nothing more than a means to get a job done strips the job of any protection whatsoever and also dehumanizes the workers in the requester’s eyes. Therefore, I resonate with the author’s thoughts about bringing this to light and feel that a call for transparency is the need of the hour.

While the author is talking about the negative impacts of this form of ghost working, he also highlights why the workers prefer to do this job and why they return to the platform to search for jobs every single day. It is because of the anonymity that this platform provides by removing from the individual any attributes that might otherwise hinder their ability to seek jobs outside. Remote access is another aspect of ghost working that attracts people to engage in this work. A surprising fact I learnt from the extract is that the quality of the ghost workers often surpasses that of full-time employees (who get paid a lot more and also get benefits). Fear of losing subsequent tasks via the platform motivates these workers to raise the bars and deliver extremely high-quality work.

The nature of jobs posted is seasonal and depends on the requirements of the requesters. As mentioned in the extract, a few days bloom with requests while the rest of the days pass without a single request. Is there anything that can be done to streamline work so that the ghost workers are guaranteed work? Is there a way to provide alternate employment to the ghost workers that guarantees regular employment? This would be immensely helpful since the vast majority of ghost workers (if not all) depend on income from these sources to meet their living expenses.

Can task starvation be avoided by innovations?

While this platform indeed provides anonymity to the workers, why are the ghost workers not paid a fair amount? What can be done to ensure that a fair amount is distributed to the workers?

Read More

Summary of the Reading

This paper looks to build a model of crowd work to better understand some of the major challenges that the future of crowd work will face. The paper also collects data and makes suggestions on how to make the future of crowd work better for all those involved. 

One of the main contributions of this paper is building a model to represent crowd work. They do this by using not only theoretical data, but also empirical data that is gathered from both requesters and workers on crowd work websites. This model helps the authors to visualize some of the problems that come from crowd work and allow them to better understand possible solutions.

The paper then goes into depth on 12 research foci that are part of the model. They consider the future of the crowd work processes, crowd computation, and crowd workers. After this, they synthesize these foci into concrete recommendations for the future of the industry, including creating career ladders, improving task design and communication, and facilitating learning.

Reflections and Connections

I think that this paper looks at a very important part of crowd work. Crowd work is a very powerful tool, but if we don’t do it right, it could lead to a terrible future where work is cheepened and workers are not respected. We need to make sure that while this industry is still young that it is shaped in a way that will allow it to be sustainable well into the future. Articles like these are important so that we can know what we need to change now, when the industry is still young so that when it grows up, it can be a great part of the economy. 

I also like that the article has a very optimistic view of crowd work. The technology is very powerful and can allow people to do things that never could have been done without it, or could only have been done at a tremendous cost. If we put in the work to make this industry work well for its users, then we will be able to use crowd work to accomplish many great things and many people will be able to use it to make ends meet or to get an extra income on their own time. Crowd work unlocks new, more efficient ways to accomplish many old goals. 

I also think that the goals laid out in this article will be very good for the industry. The idea of creating career ladders would be extremely beneficial to everyone involved. Many people in traditional jobs only try their best because of the possibility of moving up in the company. So, a job where you can’t move up will make people less invested and less motivated. The idea of introducing better communication would also be great for the industry. There will always be times when a task is poorly worded and a quick email can save hours of wasted time and effort. The facilitation of learning also seems extremely beneficial to everyone involved and would also increase the quality of tasks and results. 

Questions

1. Considering this article was published in 2013, do you think the industry is moving in the right direction?
2. How practical do you think these recommendations are? Are they too ambitious?
3. Which of the recommendations do you think is most important for the industry to implement? 

Read More

SUMMARY

‘Ghost work’ is a new world of employment that encompasses the work done behind-the-scenes by various unnamed online workers that help to power mobile apps, websites, and AI systems. These workers are the ‘humans-in-the-loop’ contributing their creativity, innovation, and rational judgement to cases where machines are unable to make decisions accurately. The importance of these ‘gigs’ has increased exponentially over the past few years – especially in order to provide better training data for Machine Learning systems (such as the ImageNet challenge that involved annotating millions of images), cleaning-up social media pages (ensuring that there is little to no abusive content), as well as providing accurate descriptions for product and restaurant reviews. This work covers various online platforms like Amazon’s Mechanical Turk, Microsoft’s UHRS, LeapGenius, and Amara – each catering to slightly varied tasks ranging from micro-tasks (tasks that can be done quickly, but require many people) to macro-tasks (larger projects such as copyediting a newsletter, linking video to captions). The work also illustrates the lives of four such ghost workers, and their experiences on these platforms, giving some insight into the ‘human’ behind the seemingly interchangeable worker represented by a pre-defined worked ID.

REFLECTION

As a researcher working with Machine Learning, I think that I often forget the ‘human-side’ of Machine Learning – especially with respect to the generation of labels for supervised tasks. I found the description about the lives of Joan, Kala, Zaffar, and Karen to be particularly interesting and insightful. This was interesting to me as it helped me better understand the motivation of people engaging in crowd work and helped me better appreciate the efforts that need to be taken in order to gain a good ‘gold standard’. I also found it interesting to learn that on-demand workers produce higher-quality work when compared to the quality of work produced by full-time employees. While the reading posits that a potential reason for this is the competitive nature of on-demand jobs, I wonder if incentive-based bonuses in the case of full-time employees could have an impact on the quality of work provided by these employees.

The reading reminded me of another paper entitled ‘Dirty Jobs: The Role of Freelance Labor in Web Service Abuse’ which focusses on freelance work done via Freelancer.com. This paper discusses the abuse of various web services including solving CAPTCHAs, Online Social Networks Linking, etc. This reading talks about vetting processes done for the workers, but it made me wonder about the type of vetting done for ‘requesters’ and the moderation of the type of tasks that are posted on such ghost work platforms.

QUESTIONS

• What is the main motivation for ghost workers with a Master’s degree to work on these platforms? Is it generally due to an ailing family member? Additionally, Amara has a larger percentage of women, is there any reason behind this?
• In the case of a platform such as Amara, where workers perform tasks in teams, how do they handle cases of harassment (if any were to occur)? Are they any policies that exist to deal with such situations?
• Is there any form of moderation of the type of tasks that are posted on these sights? Are the ghost workers allowed to flag tasks that might seem to contribute to web service abuse? (For example, multiple account creation by using ghost workers to solve CAPTCHAs in real-time).

Read More

Summary

            Ghost work’s introduction and first chapter cover a birds-eye look at the new gig economy of crowd intelligence tasks. They cover several anecdotes of people working on these tasks in various situations, mostly in the United States and India. The introduction also wants to get across that these types of tasks are for problems that AI can’t solve or needs training to be able to solve. The text also tries to impart that there is nothing to fear from AI – automation has always happened through new technologies and that there will always be more work generated by the blind spots of the newer technologies. The first chapter then goes through several different scenarios for these workers, starting with the worst working conditions and then working up to the “best.” Lastly, it pointed out that there are possible moral issues with the whole setup, using a lawsuit of workers for a specific company arguing that they were essentially being paid minimum wage working full time with no benefits.

Personal Reflection

            I thought it was interesting to better understand how these gig workers came into being and why they’re needed.  However, I couldn’t stop thinking about the human element. Yes, the text seems to drive you towards that direction, but it’s not until the last 2-3 pages of the book that it ever actually asks the question “Is this right?” The first few specific anecdotes in the first chapter were chilling – these were people with not just undergraduate degrees but post-graduate degrees who were working for a paycheck that put them below the poverty line. Arguably only the last 2 companies mentioned, Upwork and Amara, were even close to acceptable living conditions. LeadGenius was close, as there were tiers and “promotions” that could be earned through working, but they still seemed to pay very little for quality work. MTurk being the worst really outshone the others.  A talented worker as the example was, she was only earning $16,000 a year working (according to the intro) 10 hours a day, and she was happy it was better than the $4,400 she earned the first year. The text even (insultingly) points out $4400 is more than earning $0. Futhermore, working as an mTurk required additional work to figure out what good HITs would open and what requesters to avoid as well as learn the tips and tricks of the trade. At least in a Starbucks you get paid the full amount during your training. This text and the whole ghost work gig-economy industry feels like share-cropping, where the workers are cheated out of their proper valuation.

Questions

1. How could the ghost-work/gig-economy be regulated? Is self-regulation as shown by the mechanical turk forums and reddits enough?
2. Now knowing what life is like for these workers, could you ethically use this service? Are the rosy-stories of “I can fill in gaps on my resume” or “I can’t work standard hours so the flexibility is nice” or “I have another source of income so this is just free money” enough to counteract the underpayment of these workers?
3. Which of the businesses that setup gig-workers seems like the best tradeoff for requesters and workers? Why?
4. What do you think about the idea of having to screen “employers” on Mechanical Turk? How can this impact the pay rate?

Read More