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Transform Your Workflow with Scry AI Automation
Get StartedLike the previous three industrial revolutions, the fourth and current one will undoubtedly upend the status quo with countless jobs lost and new ones created. This revolution is particularly worrisome because AI systems and other automation techniques are likely to render millions of people unemployed and some even unemployable. In 2013, Frey and Osborne published an extremely influential article that stated “47% of total US employment is in the high-risk category, meaning that associated occupations are potentially automatable over some unspecified number of years, perhaps in a decade or two” [1]. Although very few jobs have been destroyed by AI until now, many doomsayers have repeated the forecast regarding millions of jobs being lost to AI within the next few years. In contrast, as discussed, in the book titled, “The Fourth Industrial Revolution and 100 Years of AI (1950-2050)”, although 395 million jobs may be destroyed by AI by 2050 since the global population will have fewer workers on a percentage basis and more aging people, we may need AI and robots to help us in doing substantial work [2]. Also, very few pundits have discussed new jobs that are likely to be created because of AI. This article deals with one such emerging job category of “data annotators,” which has largely emerged due to AI and has seen a meteoric rise during the last decade.
In October 1950, Alan Turing posed the following question: can a machine imitate human intelligence? In his seminal paper, “Computing Machinery and Intelligence,” he formulated a game, called the “Imitation Game,” in which a man, a computer, and a human interrogator (“judge”) are in three different rooms. The interrogator’s goal is to distinguish the man from the computer by asking them a series of questions and reading their typewritten responses; the computer’s goal is to convince the interrogator that it is the man [3]. See Figure 1.
Later, in a 1952 BBC interview, Turing mentioned that it would be at least 100 years before any computer could convince a judge and pass the test [4]. Indeed, today many AI systems are trained in a manner like that used for educating our children, and this process is called Supervised Machine Learning, which is briefly explained below:
Supervised Machine Learning: Suppose we are given ten million pictures of the faces of dogs and cats, and we would like to partition them into two groups – one containing dogs and the other cats. Rather than doing it manually, a Machine Learning expert writes a computer program (“black box” program) by including various features that differentiate dog faces from cat faces (e.g., length of whiskers, droopy ears, angular faces, round eyes).
Also, another professional (“data annotator”) labels each picture either as having a dog face or a cat face. After enough features have been included in this “black-box program” and after all the ten thousand pictures have been labeled or annotated, these pictures are divided into two groups – a training group (with say 9,500 pictures) and a testing group (with 500 pictures).
Next, the first picture is given to this “black box” program. If this program’s output is not the same as that of the label (e.g., if its output is a dog-face but the label says that it is a cat-face), this program modifies some of its internal structure to ensure that its answer for this picture becomes the same as that of the label provided by the annotator. On the other hand, if the output of this program is the same as that of the label, then this program does not modify its internal code. See Figure 2.
After going through 9,500 such pictures and modifying itself accordingly, this black box program is tested for accuracy related to the remaining 500 pictures. If the accuracy is not sufficient then either the Machine Learning expert rewrites the program or uses more labeled data to train the current program.
On the other hand, if the trained black box program achieved the desired accuracy, then it can be used to classify the remaining (1,000,000 less 10,000) pictures into the two required groups. However, during this classification process, the accuracy could drop because the pictures may be blurry, noisy, etc. Hence, the user would need to periodically check the accuracy of the system and while doing so the user (or data annotator) will have to annotate some of the incoming pictures and create the “ground truth” against which the output of this trained black-box program can be checked.
To recap, data labeling is first required for training the Machine Learning program and then required periodically to create the ground truth to ensure the continued accuracy of the trained program.
Undoubtedly, Data Labeling is humanly the most arduous and time-consuming part of building and maintaining AI systems. In particular:
Indeed, data annotation or data labeling is critical both for training the AI system and for checking its accuracy. Keeping this in view, in 2009, Prof. Fei-Fei Lee (of Stanford University) In 2009, Fei-Fei Li created ImageNet, which became the largest database of its kind with more than 14 million URLs of images, almost all of which were hand-labeled (or annotated) to indicate as to what they contained [5]. The next section discusses in detail the importance of data annotation and ground truth checking below.
The more accurate an AI system, the less human labor is required. Hence, accuracy is the most important characteristic of AI systems. Unfortunately, because of the debilitating limitations of AI models and because the incoming data changes over time, the accuracy of these AI models deteriorates, thereby forcing data annotators to label new data and AI professionals to retrain the existing AI models. Given below are a few scenarios that may require the AI system to be modified because of deterioration in accuracy:
(1) After operationalizing (i.e., putting it in production) the AI system, professionals may realize that the incoming data has a different distribution, which would require additional labeling of data and more retraining, validating, and testing of AI models. For example, for the AI system to detect vehicles on a highway, they did not include enough pictures when it was raining, snowing, foggy, or misty. Even worse could be the realization that the incoming data distribution is substantially different, which may require a reformulation of the problem and the use case. For example, while training the AI model, practitioners assumed that their model would be only used for detecting pedestrians in California, which does not have snowfall. However, later they realize that even in California there are locations that have snowy winters and hence need to collect data from those locations and then retrain on the combined dataset.
(2) After operationalizing the AI system, AI experts may recognize that the training and testing data has a bias or data governance did not include standard practices of consent management, privacy, auditability, or lineage. For example, after training the AI system, these experts realized that they were using features that included personal information (e.g., age and gender) but the data gatherers only obtained consent from males but not from females. In this case, either they must remove all data regarding females or obtain consent from them (whose data has been included), which they may not receive or receive only partially. In either case, they will need to redo the entire AI pipeline (i.e., relabeling and removing data regarding gay people, re-extraction of features, and retraining AI algorithms).
(3) While operationalizing the AI system, AI professionals may determine that their labeling was incorrect, thereby triggering re-labeling, feature engineering, and the model training pipeline. Similarly, while doing feature engineering, developers may have deprioritized some features (e.g., those related to geography or demographics), which may be incorrect and may need to be reworked. Also, sometimes, the business goal may change (e.g., conditions for approval or denial of a loan may change), which may require the decision thresholds to change appropriately. Since these thresholds are usually set manually, the corresponding AI system would need to be updated, end to end.
(4) After operationalizing the system, a change in government’s rules and regulations may be detected that would necessitate relabeling of data and redoing the entire pipeline. Alternatively, experts may realize that the AI model is not fair or interpretable, but government regulations require it to be so.
Although Generative Pretrained Transformers (GPTs) and Large Language Models (LLMs), which are a subset of Deep Learning Networks, are currently the rage, they suffer from devastating limitations – Machine Hallucinations and Machine Endearment – which are briefly discussed below by providing examples.
Machine Hallucinations: Falcon-40B is an open-source LLM that was unveiled recently. It usually provides fairly accurate and relevant answers. However, like most other LLMs of similar ilk, it occasionally provides the right answer the first time and a wrong one the second time (or vice versa), which is just one example of machine hallucination. For example, we asked the following question:
Unfortunately, the World Wide Web is now replete with examples of Machine Hallucinations for almost all GPTs and LLMs (e.g., ChatGPT, Gemini, and Claude 3) that are in operation today. Since all categories of current Deep Learning Networks including GPTs are unexplainable and uninterpretable, they become highly untrustworthy when they provide inconsistent or wrong answers. These
characteristics force ground truth checkers to spend substantial time verifying the answers provided by LLMs or they are duped into accepting wrong answers.
Machine Endearment: Regardless of the validity of their content, the output from most GPTs is confident, syntactically coherent, polite, and eloquent. Since they are trained on vast troves of human-written data, they usually produce outputs that also appear convincingly human. For example:
In June 2023, Jonas Simmerlein a theologian from the University of Vienna used ChatGPT to create a 40-minute sermon for Protestants. (See Chapter 11 in [2] for details). According to him, 98% of the content came from ChatGPT, and the entire service was conducted by two male and two female avatars on the screen. About 300 people attended this service, and some people videotaped this event eagerly. One of the attendees, Marc Jansen – a 31-year-old Lutheran pastor – was impressed and remarked, “I had actually imagined it to be worse. But I was positively surprised by how well it worked. Also, the language of the AI worked well, even though it was still a bit bumpy at times.”
The above-mentioned communication style is reminiscent of an endearing advisor, who we often turn to for direction or assistance. Over time, we begin to rely on such advisors because they seem endearing and have a stake in our well-being. We therefore call this characteristic of AI systems, “Machine Endearment”; this refers to the broad notion of people trusting AI systems due to their human-like responses and irrespective of their validity.
Devastating Outcomes Due to Machine Hallucinations and Machine Endearment: Unfortunately, although the arguments by GPTs are seemingly persuasive, they are sometimes Machine Hallucinations. Given below is one such example:
In 2023, two lawyers, Schwartz and LoDuca, used ChatGPT to find prior legal cases to strengthen their client’s lawsuit. In response, this Transformer provided six nonexistent cases. Since these cases were fabricated, the presiding judge fined Schwartz and LoDuca five thousand Dollars. According to an affidavit filed in the court, Schwartz eventually acknowledged that ChatGPT invented the cases, but he was “unaware of the possibility that its content could be false” and therefore believed that it had produced genuine citations.
Since there is no easy way of knowing whether these GPTs and LLMs are providing correct results or those due to Machine Hallucination, “ground truth checking” is required, and this is where many data annotators are being used. Of course, some data annotators working in specific domains (e.g., healthcare) require a considerable amount of subject matter expertise whereas others may not. Finally, although GPTs and LLMs were discussed above, almost all contemporary AI systems (including other Generative AI models such as Generative Adversarial Networks, Diffusion Models, and Support Vector Machines) are brittle (i.e., break easily) and hence require ground truth checking on a regular basis.
Fifteen years have gone by since the creation of ImageNet (by Fei-Fei Lee) and today there are more than 1,000 use cases of AI that have been already operationalized. Almost all of them require Supervised Machine Learning and hence they require gigantic amounts of data to be annotated (or labeled). The original trend started around 2003-04 with Amazon, Walmart, Target, and other e-commerce companies who initially used workers in India to label their products and create catalogs. In fact, Amazon Mechanical Turk (MTurk) was the first crowdsourcing website with which businesses could hire remotely located “crowd workers” to perform discrete on-demand tasks such as labeling. And many of these “crowd workers” were based in India.
Since every organization is different than others, it also has some data sets that are different than others. For example, if two companies are using Large Language Models to answer customer queries then the answers given by each company will depend upon its own dataset. This implies each company will need annotators to cleanse and label most of the data that it owns. Of course, this is not only true about Large Language Models but about all AI systems (wherein each organization will need its datasets to be cleansed and annotated). And, since the number of organizations using AI systems is growing exponentially, the need to hire data annotators is growing accordingly. Of course, organizations may hire full-time annotators in-house or may outsource this task to a third party, but they will need such annotators in all such cases.
As an employment category, data annotation is currently being defined broadly. For example, it includes:
Hence, during the last decade, Annotation as a Service has been on a meteoric rise. Already, there are more than 400,000 annotators worldwide, and by 2050, this field is likely to have around 15 million. Currently, its market size is roughly 11.5 billion US Dollars and is expected to be around 975 billion US Dollars by 2050 (assuming a global annual inflation of 3%).
Annotation as a Service and the Crucial Role Being Played by India: More than 55% of the labor involved in creating an AI system requires cleansing and annotation of data. Hence, this task needs to be done inexpensively but accurately. Furthermore, these annotators need to work very closely with AI experts, Data Scientists, and Data Engineers. Since, India currently has more than 5.4 million software professionals and many of them are AI experts, Data Scientists, and Data Engineers, the proximity of annotators in India obviously helps.
Below are a few reasons why India has become a hotbed for providing such services:
Given the above-mentioned reasons and since India has a large workforce that is growing rapidly, our analysis shows:
China has around 100,000 data annotators and is second in this emerging area. China also has some of the same advantages as India, but Chinese are not proficient in English and hence most of their labeling is related to the Chinese language. Other low-wage countries like the Philippines, Pakistan, Bangladesh, and a few African countries are also providing some annotators but because they do not have many AI experts, Data Scientists, or Data Engineers, they have a relatively smaller number. Finally, there are a substantial number of annotators that are in expensive countries.
Annotation as a Service is a sub-segment of Business and Knowledge Process Outsourcing Sectors: As mentioned above, many data annotation tasks only require a basic proficiency in the English language and no other subject matter expertise. Hence, such tasks do not require more than a high-school education and some of them are being performed by college students working part-time, thereby making executing such tasks a part of the gig economy.
Since LLMs are likely to reduce the number of call center agents in India from approximately 1.5 million currently to around 750,000 during the next decade, many such people can be redeployed as data annotators. Hence, to some extent, Annotation as a Service belongs to the broader category of “Business Process Outsourcing.”
Finally, as mentioned in Section 2, some of the data annotation work requires considerable subject matter expertise. For example, a data annotator who is labeling the location in X-ray pictures where Melanoma (skin cancer) is present needs to have domain expertise in this field. Similarly, a data annotator who labels different kinds of fishes and other sea creatures needs to have substantial knowledge of this area. Hence, unsurprisingly, such data annotators usually have a college degree in their area of specialization and charge substantially more than those who are labeling data that does not require any subject matter expertise.
Indeed, just like the number of call center agents is expected to decrease (due to LLMs), the number of medical transcriptionists is also likely to decrease (due to AI and automated speech recognition systems). It is quite likely that several such people will be redeployed as data annotators. Hence, some areas within Annotation as a Service belong to the category of “Knowledge Process Outsourcing” [6].
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We do not assert any ownership over your Content. You retain full ownership of all of your Content and any intellectual property rights or other proprietary rights associated with your Content. We are not liable for any statement or representations in your Content provided by you in any area in the Website. You are solely responsible for your Content related to the Website and you expressly agree to exonerate us from any and all responsibility and to refrain from any legal action against us regarding your Content. We are not responsible or liable to any third party for the content or accuracy of any User Submissions posted by you or any other user of the Website. User Submissions are not endorsed by us and do not necessarily represent our opinions or the view of any of our affiliates or partners. We do not assume liability for any User Submission or for any claims, liabilities, or losses resulting from any review.
We have the right, in our sole and absolute discretion, (1) to edit, redact, or otherwise change any Content; (2) to recategorize any Content to place them in more appropriate locations in the Website; and (3) to prescreen or delete any Content at any time and for any reason, without notice. We have no obligation to monitor your Content. Any use of the Website in violation of these Terms of Service may result in, among other things, termination or suspension of your right to use the Website.
These Submission Standards apply to any and all User Submissions. User Submissions must in their entirety comply with all the applicable federal, state, local, and international laws and regulations. Without limiting the foregoing, User Submissions must not:
We have the right, without provision of notice to:
You waive and hold harmless company and its parent, subsidiaries, affiliates, and their respective directors, officers, employees, agents, service providers, contractors, licensors, licensees, suppliers, and successors from any and all claims resulting from any action taken by the company and any of the foregoing parties relating to any, investigations by either the company or by law enforcement authorities.
For your convenience, this Website may provide links or pointers to third-party sites or third-party content. We make no representations about any other websites or third-party content that may be accessed from this Website. If you choose to access any such sites, you do so at your own risk. We have no control over the third-party content or any such third-party sites and accept no responsibility for such sites or for any loss or damage that may arise from your use of them. You are subject to any terms and conditions of such third-party sites.
This Website may provide certain social media features that enable you to:
You may use these features solely as they are provided by us and solely with respect to the content they are displayed with. Subject to the foregoing, you must not:
The Website from which you are linking, or on which you make certain content accessible, must comply in all respects with the Submission Standards set out in these Terms of Service.
You agree to cooperate with us in causing any unauthorized framing or linking immediately to stop.
We reserve the right to withdraw linking permission without notice.
We may disable all or any social media features and any links at any time without notice in our discretion.
You understand and agree that your use of the website, its content, and any goods, digital products, services, information or items found or attained through the website is at your own risk. The website, its content, and any goods, services, digital products, information or items found or attained through the website are provided on an "as is" and "as available" basis, without any warranties or conditions of any kind, either express or implied including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. The foregoing does not affect any warranties that cannot be excluded or limited under applicable law.
You acknowledge and agree that company or its respective directors, officers, employees, agents, service providers, contractors, licensors, licensees, suppliers, or successors make no warranty, representation, or endorsement with respect to the completeness, security, reliability, suitability, accuracy, currency, or availability of the website or its contents or that any goods, services, digital products, information or items found or attained through the website will be accurate, reliable, error-free, or uninterrupted, that defects will be corrected, that our website or the server that makes it available or content are free of viruses or other harmful components or destructive code.
Except where such exclusions are prohibited by law, in no event shall the company nor its respective directors, officers, employees, agents, service providers, contractors, licensors, licensees, suppliers, or successors be liable under these terms of service to you or any third-party for any consequential, indirect, incidental, exemplary, special, or punitive damages whatsoever, including any damages for business interruption, loss of use, data, revenue or profit, cost of capital, loss of business opportunity, loss of goodwill, whether arising out of breach of contract, tort (including negligence), any other theory of liability, or otherwise, regardless of whether such damages were foreseeable and whether or not the company was advised of the possibility of such damages.
To the maximum extent permitted by applicable law, you agree to defend, indemnify, and hold harmless Company, its parent, subsidiaries, affiliates, and their respective directors, officers, employees, agents, service providers, contractors, licensors, suppliers, successors, and assigns from and against any claims, liabilities, damages, judgments, awards, losses, costs, expenses, or fees (including reasonable attorneys' fees) arising out of or relating to your breach of these Terms of Service or your use of the Website including, but not limited to, third-party sites and content, any use of the Website's content and services other than as expressly authorized in these Terms of Service or any use of any goods, digital products and information purchased from this Website.
At Company’s sole discretion, it may require you to submit any disputes arising from these Terms of Service or use of the Website, including disputes arising from or concerning their interpretation, violation, invalidity, non-performance, or termination, to final and binding arbitration under the Rules of Arbitration of the American Arbitration Association applying Ontario law. (If multiple jurisdictions, under applicable laws).
Any cause of action or claim you may have arising out of or relating to these terms of use or the website must be commenced within 1 year(s) after the cause of action accrues; otherwise, such cause of action or claim is permanently barred.
Your provision of personal information through the Website is governed by our privacy policy located at the "Privacy Policy".
The Website and these Terms of Service will be governed by and construed in accordance with the laws of the Province of Ontario and any applicable federal laws applicable therein, without giving effect to any choice or conflict of law provision, principle, or rule and notwithstanding your domicile, residence, or physical location. Any action or proceeding arising out of or relating to this Website and/or under these Terms of Service will be instituted in the courts of the Province of Ontario, and each party irrevocably submits to the exclusive jurisdiction of such courts in any such action or proceeding. You waive any and all objections to the exercise of jurisdiction over you by such courts and to the venue of such courts.
If you are a citizen of any European Union country or Switzerland, Norway or Iceland, the governing law and forum shall be the laws and courts of your usual place of residence.
The parties agree that the United Nations Convention on Contracts for the International Sale of Goods will not govern these Terms of Service or the rights and obligations of the parties under these Terms of Service.
If any provision of these Terms of Service is illegal or unenforceable under applicable law, the remainder of the provision will be amended to achieve as closely as possible the effect of the original term and all other provisions of these Terms of Service will continue in full force and effect.
These Terms of Service constitute the entire and only Terms of Service between the parties in relation to its subject matter and replaces and extinguishes all prior or simultaneous Terms of Services, undertakings, arrangements, understandings or statements of any nature made by the parties or any of them whether oral or written (and, if written, whether or not in draft form) with respect to such subject matter. Each of the parties acknowledges that they are not relying on any statements, warranties or representations given or made by any of them in relation to the subject matter of these Terms of Service, save those expressly set out in these Terms of Service, and that they shall have no rights or remedies with respect to such subject matter otherwise than under these Terms of Service save to the extent that they arise out of the fraud or fraudulent misrepresentation of another party. No variation of these Terms of Service shall be effective unless it is in writing and signed by or on behalf of Company.
No failure to exercise, and no delay in exercising, on the part of either party, any right or any power hereunder shall operate as a waiver thereof, nor shall any single or partial exercise of any right or power hereunder preclude further exercise of that or any other right hereunder.
We may provide any notice to you under these Terms of Service by: (i) sending a message to the email address you provide to us and consent to us using; or (ii) by posting to the Website. Notices sent by email will be effective when we send the email and notices we provide by posting will be effective upon posting. It is your responsibility to keep your email address current.
To give us notice under these Terms of Service, you must contact us as follows: (i) by personal delivery, overnight courier or registered or certified mail to Scry Analytics Inc. 2635 North 1st Street, Suite 200 San Jose, CA 95134, USA. We may update the address for notices to us by posting a notice on this Website. Notices provided by personal delivery will be effective immediately once personally received by an authorized representative of Company. Notices provided by overnight courier or registered or certified mail will be effective once received and where confirmation has been provided to evidence the receipt of the notice.
To request a copy for your information, unsubscribe from our email list, request for your data to be deleted, or ask a question about your data privacy, we've made the process simple: