Stephanie Lee
Ethics of Crowdsourcing

‘Crowdsourcing’; Innovation or Exploitation?
Abstract
Crowdsourcing is the concept of mass collaboration of external outsourced tasks typically from an institution to its community (Howe, 2005). This paper explores the two sides of crowd sourcing; whether this net-enabled community collaboration is accelerating innovation or exploiting the laborers with menial tasks of little to no benefit through an analysis of Soylent, one of the latest innovations in crowd sourcing using John Rawls’ Theory of Justice, the Veil of Ignorance, I aim to demonstrate how can we all benefit from crowdsourcing and harness the true power of collective intelligence and mass collaboration just by maintaining a fine balance between the two extremes as according to the Aristotle’s Golden Mean.
Introduction
David Brent once said; “A problem shared is a problem halved, so is your problem really yours or just half of someone else’s?” In terms of an ideal crowd sourced task, the problem would be diminished by half, or even more because it is shared between dozens, hundreds, and even thousands of active community participants eager to solve it to the best of their abilities. Crowdsourcing, as first coined and defined by Jeff Howe, is the concept of mass collaboration of tasks that are outsourced externally typically through the Internet to the community at large. He says it is “the act of outsourcing tasks, traditionally performed by an employee or contractor to an undefined, large group of people or community (a "crowd"), through an open call” (Howe, 2005). Because it is an open call to an undefined group of people, a typical crowd sourced task gathers those who are most fit to solve it and are able contribute with the most relevant and pertinent ideas. On the surface, crowdsourcing seems to be a wonderful idea. Many have lauded the promise of net-enabled collaboration; they praise its ability to bring together communities for a single cause or action and revitalize democracy. The open source software movement proved that a network of passionate, geeky volunteers could write code just as well as the highly paid developers at Microsoft or Sun Microsystems. Wikipedia showed that the model could be used to create a sprawling and surprisingly comprehensive online encyclopedia. Moreover, companies like eBay and MySpace have built profitable businesses that could not exist without the contributions of users. Nevertheless, who really is the main benefiter of the crowd’s intelligence? Does the crowd actually benefit from participating in collective tasks where rewards vary according to individual input? Are we even keeping true to the principles of crowd sourcing? My paper aims to address the ethics of crowd sourcing; is it innovation or is it exploitation of the ‘crowd’ using John Rawls’s Theory of Justice, the Veil of Ignorance. I will also elaborate further and connect the idea of collective intelligence versus mob wisdom to illustrate the impact of ethical crowd sourced tasks in my analysis of a crowdsourcing project, the Microsoft Word Processor’s Soylent project. In this paper, I use Jonathan Zittrain’s definition of exploitation as a misuse of a person’s labor in a way that doesn’t benefit them and innovation as value addition and/or creation, both to a specific product and to the ‘laborers’ who worked on it (Zittrain, 2008).

History of Crowdourcing Humankind has always needed to catalogue and organize information in order to maintain organization of important data or solve a problem that benefits the many. However, the need for social collaboration and acceleration of innovation is creating more demand for crowdsourcing more than ever. Currently, Iceland is crowdsourcing its new constitution. (Thomas, 2011). During the past, transcribing and gathering information was accomplished by hand before the digital era. Now, Microsoft is crowdsourcing aspects of Windows 8 (Thomas, 2011). Now we will examine how crowdsourcing is more than just a current trend.
The Longitude Problem Sailors during the past, had difficulty with written information being accurate. In 1714, the British Government was stuck for a solution to what they called "The Longitude Problem" which made sailing difficult and deadly (killing 1,000s of seamen every year) (Thomas, 2011). Inaccurate Longitude charts were causing sailors to be lost at sea, and in some cases people starved. Seeking innovation, the British Government offered £20,000 for people to invent a solution (£20,000 in 1714 is around $4.7 million dollars in 2010) (Thomas, 2011). Innovative scientists and sailors came together to formulate information that is more accurate. This is possibly the first ever example of crowdsourcing; thus, solving the problems for many sailors. The contest was considered unsolvable by many. John Harrison, the son of a carpenter, won the Longitude contest. Harrison invented the 'marine chronometer' (i.e. an accurate, vacuum-sealed pocket watch) (Thomas, 2011). His knowledge and brilliance helped solve navigation in the 1700’s. The aristocrats were hesitant to award Harrison the prize but eventually paid him the £20,000 (Thomas, 2011). This example of crowdsourcing is a great one because it highlights one of the principles of crowdsourcing - innovation and creativity can come from anywhere (Thomas, 2011). In the early 18th century, being able to navigate accurately was the difference of returning home or not. Getting lost on a transoceanic voyage did not just mean that a competing nation could lay claim to a new piece of empire before you. It was also a matter of life and death (Thomas, 2011). Latitude was not as much of a problem in that it could be found from the altitude of the sun at noon with the aid of a table giving the sun’s declination for the day. 1936: Toyota Logo Contest Improvements in design standards required crowdsourcing. Contests were important for logo designs in the 1930’s. In 1936, Toyota held a logo contest to redesign their company logo. The company received 27,000 entries and the winning logo was the three Japanese katakana letters for “Toyoda” in a circle, which was later modified by Risaburo Toyoda to "Toyota” (Design Crowd, 2012). Entries during this time were hand written, and then mailed in. In some cases, mail took a long time to receive, and then there was the arduous task of narrowing down the contest entries.

The Oxford English Dictionary: Prior to the Oxford English Dictionary, most English dictionaries were inconsistent, lacking in historical context and about as far from comprehensive as it was possible to be. One of the reasons the dictionary was able to change that was because hundreds of volunteers were assigned to particular books, copying passages illustrating word usage onto quotation slips (Design World, 2012). Crowdsourcing and collaboration helped unify dictionary standards. Furthermore, there were errors here and there but the project proved the public to make labour intensive tasks quicker and cheaper.

Food Preservation – Early 1900’s In the early 1900’s, Peter Durand transferred a technique, which had recently been discovered for preserving food in glass jars into cylindrical tin or wrought iron canisters (Thomas, 2011). He sought help from many sources. The cans were suited to the Army’s needs as they were lighter, cheaper and quicker to make, and hardier than any means of preserving food which had existed previously (Thomas 2011). This was an example of how crowdsourcing helped the multitudes.
Bill Grave – Modern Databasing Bill Graves cataloged the world’s cemeteries and produced a searchable, geo-tagged database of grave markers (Brumfield, 2012). Crowdsourcing took place. Volunteers participate in two ways: either they take photos of headstones, or they helped transcribe the information on those photos. According to Brumfield (2012), “The unique thing is that the photos are taken and uploaded with a mobile app, which means that they’re geo-tagged and assigned to a cemetery based on the mobile devices GPS and some information already entered by the volunteer when he or she begins a session” ( p. 1). The photos were then submitted for transcription and categorization — by the volunteer, who took them, perhaps by a volunteer without a mobile device (Brumfield, 2012). Crowdsourcing continues to evolve using efforts of many individuals.
2010 to 2012 Currently Facebook, twitter, Google and the open source software movement proved that a network of passionate and volunteers could write code just as well as the highly paid developers at Microsoft or Sun Microsystems. These new avenues will change the way individuals or groups of people communicate or transcribe information. Since 2010, interactivity is on the increase. Interactivity means that you are no longer simply searching, reading, forwarding, or printing. Rather, you are actively producing, adding information online, and becoming a part of the content creation process (LeMay, 2012). Online interaction between users at different locations is becoming a good tool for business professionals, advertisers, and personal users. According to LeMay (2012), “Skype, http://www.skype.com, allows you interact online by making international telephone VOIP phone calls; OOVOO, http://www.oovoo.com, makes it possible for you to conduct free international videoconferencing” (p. 1). Interactivity is going to change crowdsourcing for years to come. Now let us examine the Literature Review.
III Literature Review Crowd sourcing is most popularly recognized and analyzed by scholars as distributed labor networks through the Internet. For every critic of its exploitative power, there is also a campaigner for its cost efficiency and societal benefit in harnessing the spare human brainpower. The work of Lada Adamic, a professor at the University of Michigan on the intersection of crowd souring and social gaming and Jeff Howe, the author of the ‘Crowd sourcing’ will be two of the writings I will refer to most frequently. Adamic’s findings demonstrate crowd sourcing as a positive method to compliment the lack of scalability in expert driven/approach to tasks that have a vast amount of data. The community is utilized to help with analyzing and making use of large amounts of ‘highly dynamic data’. She also brings to attention the co-relation of the common problems in crowd sourcing (such as the difficulty of the task, quality of output, and incentives needed to motivate the community) with the quality of the end task result and provides suggestions on the most efficient crowd sourcing ecosystem using social gaming and interaction. Adamic states explicitly in her findings that the crowd is motivated by themselves to do the task, with or without material compensation although the harder the task is and the less the reward, the less likely they are to attempt it. Howe is a well-known campaigner for crowd sourcing; his book paints both the utopia of a crowd sourcing community and researches extensively on the negative consequences on this shift in the workforce, where all of all a sudden, crowd sourcing has become almost just another marketing fad. It also describes many examples of the early adopters to crowd sourcing first harnessed the transformative power and its’ tremendous benefits in terms of revenue and cost of production. He argues that crowd sourcing is necessary for a democratic community. Another work I will also refer to that will complement the argument that crowd sourcing aids society and accelerates innovation is ‘Here Comes Everybody, The Power Of Organization without Organizing’ by Clay Shirky. His book on the collective intelligence of the ‘unorganized’ crowd persuades the reader to pay attention to how the new forms of sharing the Internet has given us has accelerated the collective action of a small group people so much so that they can even erode existing institutional monopolies. Shirky also poses a strong case for social tools that allow us the increasingly rapid communication of information and collective decision making which in turn will make crowd sourcing even more common and powerful. He really is arguing that by crowd sourcing tasks in the age of the Internet, we are actually empowering its users. Not every crowd-sourced, organized decision making has been of benefit to the community. Jonathan Zittrain during his talk at SXSW panel on crowdsourcing-likened crowd sourcing to sweat shops in this modern day where one can be exploited to work on menial tasks for little compensation and personal satisfaction. James Surowiecki, the author of ‘The Wisdom of the Crowds’ also argues that the aggregation of information in groups resulting in a group attempt to make a decision can be fatal. His findings on the failures of crowd intelligence demonstrate that crowd sourced projects often fail because of an information cascade where a few decision makers overpower the rest and make ‘informative choices’ first, which then leads the rest of the decision makers to follow suit. This actually creates an imbalanced, fragile, almost dictator-like social outcome. In reality, one would need a select group of diverse, centralized, and open minded individuals with a common purpose, an organized division of labor, and an atmosphere of acceptance to promote an environment for successful decision-making and crowd power. He asserts that what happens when the decision making environment is not set up to accept the crowd, is that the benefits of individual judgments and private information are lost and that the crowd can only do as well as its smartest member, rather than perform better. My ethical dilemma starts here; are today’s companies truly promoting an atmosphere of collaboration and innovation through the Internet in crowd sourcing projects with their communities, or are they simply exploiting the ‘trend’ of being seen as open minded and democratic but instead, outsourcing menial tasks to save cost of production on labor while not benefiting the collective community socially and economically? Does the act of crowd sourcing itself promote innovation or generate exploitation, and if the latter, should we still employ it as a method for production? Even if we were to compensate the crowd generously for their time working on a menial task, is it worth benefiting a large institution or corporation that way but abandoning the crowd’s social and emotional well being. Not all crowdsourcing projects benefit the individual who works on them, in fact Jaron Lanier in his book ‘You Are Not a Gadget’ debated that crowd wisdom was best suited for problems that involve optimization, not innovation.

IV Methodology My methodology includes an in-depth analysis of one case study project in terms of crowd labor. The four main criteria that I will be comparing and dissecting the project will be based on Rawls’ reasoning principles; the Veil of Ignorance. I plan to use his procedure of reasoning without taking into account my personal biases on crowdsourcing. Rawls claims that rational people will unanimously adopt his principles of justice if their reasoning is based on general considerations, without knowing anything about their own personal situation (Rawls, 1971). Personal knowledge might tempt them to select principles of justice that give them unfair advantage, hence paving the path for an unjust and unethical decision. In this paper, I take into account how crowdsourcing may benefit any and all the parties participating in the project to accumulate a wide range of evidence and actions to come to a morally and ethically viable decision. Among the criteria that I will analyze Soylent with is:
1. Use of Transparency by both the employer and the ‘crowdworker’ (employee)
2. Consideration of labor law and equal opportunity employment rights
3. Benefit of the project towards all the parties involved: the employer, the crowdworker, and the Middleman (the company that is carrying out the project), and the public.
4. The end goal of the project and how it affects society’s collective intelligence at large. What I hope to gain from this through analysis would be insight into what it means to be a truly successful, multi-beneficial, and socially ethical crowd sourced project. In determining these standards, I will refer to Aristotle’s Golden Mean to determine the middle ground between the two extremes in crowd sourcing. It defines moral virtue as a state of balance where one’s morals lie in moderation and temperance. Because of the social nature of crowdsourcing, there are many variances in crowdsourced projects, from the intellectual challenges of the tasks, to privacy violations and plagiarism accusations. Therefore, it is crucial that I search for the middle ground, a set of standards for crowdsourced projects that takes into account the extremes of the spectrum and define what an ethical crowdsourced project should really look like. Aristotle’s Golden Mean principle has been proven to be challenging for many of us when it comes to decisions because our personal biases come into play. I am personally working for a non-profit that crowd sources disaster relief so I am already biased towards crowdsourcing as an optimistic social movement to harness the power of the crowd on an internet platform for micro and macro tasks. Nevertheless, in order to define a clear set of ethical standards for future crowdsourcing projects, I will remove my biases of crowdsourcing and concentrate on analyzing Soylent in depth using a variety of literature from crowdsourcing scholars and move towards the Golden Mean. For this paper, I specifically selected works by Lada Adamic, Clay Shirky, and Jeff Howe to address the question of motivation. I also chose to use James Surowiecki’s book on the Wisdom of the Crowd and Jonathan Zittrain’s thoughts on Collective Intelligence versus the ‘Dumbness of the Crowd’ to present the other side of this ethical conundrum; even by motivating the crowd, what if the project was of little benefit to the community at large? I question if the project should even be carried out even if it was not financially motivating to a certain extent. At what point does one decide whom should the crowd sourced project be intended to benefit the person who requests for help or the crowd who provides it? The conclusion I hope to come up with is findings on how can we all best benefit most from crowd sourcing as a lifestyle and maintain the middle line between innovation and exploitation.

V Findings
“Collective action is different from individual action; both are harder to get going but once going, [collective action] is harder to stop.” - Clay Shirky A fun fact on the general effect of crowdsourcing American projects to different countries: for every $1.00 of work that is directed overseas, approximately $1.12 of value is returned to the U.S. economy (McKinsey 2003). Crowdsourcing campaigners promote this fact consistently as its seal of approval, but Lernier’s research also demonstrates the possibility of ‘digital serfdom‘with crowdsourcing projects. Instead of being the great democratizer, the crowdsourcing project could potentially affect the livelihood of the middle class (Lernier, 2010). As companies increasingly turn to crowdsourcing for its proven decreased cost of production and increased rate of effectiveness, the crowd has to fight harder to gain jobs and become even more dependent on menial technological tasks that computers are unable to do yet to survive. The effect of technology on distribution of wealth and opportunity does vary depending on geographical locations, employee/employer demographics, and general education levels. Studies have not shown us why the differences of technologies impact hence, I will look specifically into Soylent’s organizational structure and how it supports or exploits crowdsourcing according to industry standards as outlined by Zittrain (Zittrain, 2008). Soylent is a word processing interface that “integrates crowd contributions to aid users in complex writing tasks” ranging from error prevention and shortening paragraphs to automation of tasks like citation searches and tense changes” (Bernstein, Little, & Miller, 2010). Its founders believe that “that crowd workers with a basic knowledge of written English can support both novice and expert writers. These workers can perform tasks that the writer might not...They can also provide a valuable outside perspective on a document, pointing out errors that the word processor cannot catch and then suggesting fixes” (Bernstein, Little, & Miller, 2010). Their motivations seem to stem from a longing to make editing documents a simpler task, as Soylent is a kind of intermediary function between the writer and the editor. Soylent is in fact, a ubiquitous human computer editor that involves requests to Amazon’s Mechanical Turk workers (Turkers, as they are commonly known as) to perform all three of Soylent’s main functionalities:
1. Shorten, a text shortening service that cuts selected text down to 80% of its original length on average without changing the meaning of the text or introducing errors (Bernstein, Little, & Miller, 2010).
2. Crowdproof, an augmentation of Microsoft Word’s spelling and grammar checker that finds problems that Word cannot, explains the problem and suggests fixes (Bernstein, Little & Miller, 2010).
3. The Human Macro, an interface for offloading and automating arbitrary word processing tasks such as formatting citations or finding appropriate figures (Bernstein, Little & Miller, 2010). Soylent’s main functionalities are also known as ‘Find, Fix, and Verify.’ Soylent is essentially an aggregator of human content, embedding human workers into a desktop application add on (Microsoft plans to embed it as a plug in for all the users of Microsoft Word) and they edit a document in real- time. Essentially, as an estimated 76% of the Turkers are comprised of workers outside of the US, mostly foreign Turkers work on editing the document. Soylent only measures their skills and proficiencies through number of words and paragraphs edited and error rates per unit measure of time. Already we see a grievance in Soylent’s key performance indicator measurements of their workers; instead of rewarding based on quality of the document edited, they reward based on quantity. Between the ‘lazy Turks’ who underperform and the ‘Eager Beaver’ Turkers who are over ambitious and aim for perfection, the level of these open ended tasks average out to a 30% error rate (Bernstein, Little, & Miller, 2010). This is considerably high considering that 69% of the foreign Turkers have obtained their Bachelors and 98% have completed high school (Bernstein, Little, & Miller, 2010). This error rate demonstrates that mass human computational power is not as effective or error free as a crowd sourcing enthusiast would hope; although the Turkers in a Soylent project appear to be working together, they really are still isolated beings each in stuck frantically trying to edit as many words as quickly as possible. While the technical nature of the Soylent project benefits a certain population of individuals who are gifted in language or one who has spare time on their hands, Soylent promotes an atmosphere of isolated and aggressive competition between its workers instead of encouraging them to truly work as a team. Ethically, this inherent measurement process does not seem beneficial to the Soylent workers; not only do they have to compete for projects instead of working as a team, they also have to edge out their colleagues in order to benefit financially. Another strong possible detrimental effect that Soylent has is how its exploitation of labor laws can lead to homogenization and decreased diversity in their Turk participant pool. Soylent engages about 10 to 30 workers to perform a reasonably menial task in a small amount of time and rewards about $1.00 - $3.00 per paragraph depending on the complexity of the document. Is it ethical to pay them so far below minimal wage? The lower the pay, the lower the quality of their talent pool will be especially since the intellectual challenge of the task per payment gained already ostracizes a large population of Turk workers (Adamic, 2011). Because of the minimal monetary gains, Soylent is indirectly keeping its participant pool homogenized to a certain demographic. A study by the Department of Informatics from the University of California, Irvine showed that in Mar 2008, Indians were at 5% of the Turkers population. By Nov 2009, it increased to 36%. In the same period, the median income of the Turkers ranged from $ 40 000- $ 60 000 USD. By late 2009, nearly a third of Turk workers reported making less than $10 000 USD annually (Ross, Zaldivar, Irani, Tomlison, & Silberman 2010). This conclusively shows that there is a disproportionate amount of low-income Indian workers in Soylent’s possible participant pool. Keeping the monetary gains so low is mostly likely harmful for all parties involved. Not only does Soylent unethically and indirectly force its workers to accept such little pay thus setting a standard of ‘race to the bottom’, they also marginalize workers of a higher intellect level who would do a better job but refuse to on the premise that the financial gain of the task is not worth their time. This homogenization of workers is directly against Howe and Shirky’s principles of crowdsourcing. They prioritize diversity as a top need for successful crowdsourced projects as a more diverse crowd from various intellectual, social, and economic backgrounds tend to produce the best crowdsourced projects (Shirky, 2008). Such pay practices are causing an undesirable demographic shift in the Turker community that indirectly harms the mission of crowdsourcing, especially since many foreign workers have come to rely on cognitive labor with micro tasks like these as a means of livelihood. In terms of addressing, the ethics of crowdsourcing from a transparency stand point. Soylent raises both the issue of alienation and exploitation of labor laws from an employee’s standpoint and privacy violations from the employer’s viewpoint. As a Soylent worker, one is not given full disclosure nor has any previous knowledge of the owner or the owner’s intentions with the project. Unknowingly, a worker might be helping to edit information that does not align with his or her moral principles. If we do not have transparency on who is commissioning these tasks, is it ethical then to contribute to something that may be out of line with our moral compasses? The affect of this alienation and moral valance caused by Soylent towards their workers is ethically negative and morally wrong. This becomes a doubly weak position for the workers because the employer has all the power and the employees have little to no knowledge of who they are working for – the employees are not able to agree or disagree with the projects given. The lack of transparency also leads to further exploitation in terms of labor laws, which would negatively affect the Turk workers. Employers who engage Soylent’s services do not have to observe the basic employee rights. I looked at their Terms and Conditions page more thoroughly to as well as their umbrella company, Amazon’s Mechanical Turk and nowhere does it imply that employers have to comply with minimum wage laws, 15-minute mandatory breaks, bonus systems, or the Equal Opportunity Act. At a glance, it seems rather ludicrous that the Turkers would put themselves at such risk for such little monetary gains and that Soylent is legally allowed to waive their basic employee rights. One might say that Soylent is not an employer, that they are just a mediator between the employer and the employee and are therefore exempt from labor laws. However, to benefit all parties involved, Soylent needs to follow best business practices, even if it is just a desktop application that functions on the internet. By allowing employers to be legally exempt from complying with the US labor laws, they are also harming the quality of talent in the participant pool. Technology has yet to come up with a clearly defined set of legal rules to prevent the exploitation of crowdworkers (Zittrain, 2008). I wonder, as ubiquitous human computing advances, one wonders, what will happen to honesty, trust and ethics on the internet? The debate on transparency also affects the employer further in terms of potential privacy violations. The employer does not know the Soylent worker nor have any information on their previous work background and potential current contractors (Bernstein, Little, & Miller, 2010). Hence, the privacy of their documents are extremely compromised by Soylent. By hiding workers in the cloud, embedding so deeply and invisibly, the employers have no way of finding out who the workers are and if they are trustworthy. A Soylent Turker could easily gleam off confidential information off a corporate document and sell it to its rival, given Soylent’s lack of transparency and policing. Soylent also does not seem to be encouraging a community self policing practices or environment; other Soylent workers who are working on the same project have little motivation to make sure that their colleagues attire to a strict set of best business practices and code of ethics. Soylent’s lack of transparency between both parties clearly is not benefiting anyone, including the crowdsourcing community. Another ethical issue that arises for the users of Soylent that is those who employ its services are issues of plagiarism accusations. Firstly, I investigated whether this splitting of the editing task into Soylent’s ‘Find, Fix, Verify’ functions could possibly change the emotional and language content of the document. In determining this, I looked at research by Michael Bernstein, Greg Little, and Robert Miller, the co-founders of Soylent from MIT Research Center. The Turkers who are behind this cloud crowd embedded plug in are not evaluated based on their language proficiency; Soylent does not take into account their syntax abilities and command of the language they are editing in, just mechanical abilities (Bernstein, Little, & Miller, 2010). This does pose an inherent threat to errors but more so, the increased possibility of the latent content of the document being altered through slight but many grammatical edits and arbitrary citation processing. If the splitting of this editing task into micro Soylent functions directly makes significant and dramatic changes to the document, be it improvements or errors, at what point does this edited document stop being an original work? Imagine if someone’s document was so poorly written that one could just repair it by sending it to Soylent. Ethically, that would be considered an infringement upon plagiarism. However, these editing tasks also seem more likely to fall under the category of ‘optimization’ tasks rather than innovation tasks. As Soylent was intended by its founders to simply the lives of many novice and expert writers, they did only formulated basic word processing functions to relieve editors of the menial tasks of grammar and overlooked word edits. If they had intended for Soylent to be a ‘plagiarism machine’ they would have surely built in more functions that enabled users to pull documents from the internet and synthesized it to their personal writing style.
VI Conclusion My findings on Soylent have led me to a conclusion that Soylent leans more heavily into the unethical side of crowdsourcing. Although Soylent’s technology is extremely beneficial to those who employ its services lacks a sense of transparency that opens up the potential for the exploitation of labor law, plagiarism, and privacy violations. Soylent’s participant pool model is also not self-sustaining due to the minimal pay structure and how that contributes to homogenization of the workers and a general lack of diversity. The Soylent project also does not give their workers a sense of empowerment or community, which is completely against crowdsourcing as it was originally intended. I applied the Golden Mean principle on further analysis on how Soylent can help improve the collective wisdom of the crowd, also referring frequently to Zittrain’s and Surowiecki’s proposed crowdsourcing models. As of now, the Soylent project tilts more so on the favor of improving a writer’s life and work by taking away preliminary editing needs. To ensure that the workers are not left out of this equation, Soylent could take it upon themselves to provide basic training, free of charge to all their Turkers. In that way, Turkers who help out on Soylent’s language editing needs could also increase their proficiencies at that specific language and build up a valuable skill set as a job asset. Soylent could then be a mutually beneficial launch pad for foreign workers who want to increase their language proficiencies and for users who want to progress faster in their work without having the cumbersome worry about small grammatical edits. Increasing the transparency between the employers and the Turkers would also be a great step. Soylent needs to disclose, at the very least, basic information about who is commissioning the various tasks that are being crowd sourced. Both employers and Turkers should be provided enough information about each other to make a moral choice for themselves without trespassing on each other’s privacy. Besides determining basic information requirements and making them mandatory for the business transaction, Soylent should allow both parties to set up profiles to describe themselves in further details and a rating system to allow community policing. The rating system would foster a greater sense of transparency and community; a Turker who has had a bad experience with one employer can now rate him or her for his fellow Turkers and vice versa. Besides opening up the platform to be more transparent, I would suggest a reevaluation of the pay structure and setting up labor standards for the employers. Any contracted worker should at least receive basic employee rights depending on their country’s labor laws; this includes mandatory breaks, bonus system, and age of consent. I strongly believe that Soylent should not contribute to child labor; they should make it mandatory for their workers to at least be 15 years old of age and for these minors to have their parents or guardians’ consent. Setting up a reward system such as bonuses to workers who have completed tasks with above average results would also attract more talent into their participant pool and increase the quality of Soylent’s functions as a whole. For a crowd-sourced project to truly contribute to increasing the collective intelligence of society, it needs to offer mutual benefit to the main participatory parties from all angles and meet the standards of transparency, labor law, and employee rights. To me, crowdsourcing seems to be a method better suited towards tasks that require optimization, not innovation. As the adage goes, too many cooks can spoil the broth. Nevertheless, if a large number of people were helping too critically analyze, dissect errors, and improve a project that would dramatically increase the quality of the project. To increase their social impact, crowdsourcing projects should first embark to research extensively and plan strategically on the long-term advantages and disadvantages of harnessing cognitive labor, especially the global crowdsourcing workforce. After determining a set of ethical standards to minimize exploitation, crowdsourcing projects would do well also to continuously provide opportunities for community and synergistic relationships. I chose this topic because I believe that the future of social media lies in crowdsourcing. We take the many opportunities of communication and relationships one-step higher, putting those networks to work and centering them on a task that would benefit them as a collective community. Our inherent human nature though is to exploit for personal gain. Granted the Internet adds a global scale to this and increases the potential for hidden exploitation and motivations. However, also with technological advances, communities are becoming more aware of their social responsibility and stepping up to more vigilant in our preserving basic human rights. The bottom line is that in opening up a crowdsourcing marketplace, where we can offer our minds for sale, any number of parties can and will gain value. It is critical that we practice a fine balance between the extremes to accelerate the future of beneficial and responsible crowdsourcing.

Reference

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This is a really interesting essay, but you missed a key component in the Findings and lost a number of points. There was evidence that you used your methodology, but you didn't make any direct connections to either the Veil or to the Golden Mean. Both would have helped you to make a more detailed and in-depth analysis. There were grammar and citation errors through (citations were incomplete for APA style). This is a really interesting bit of research - with some finessing it could really be top notch. Title/Abstract 9/10 Introduction: 10/10 Literature Review 12/15 RQs 5/5 Methodology 13/15 Findings 12/20 Conclusion/Implications 18/20 APA Style 4/5 TOTAL 83/100