Behind the Scenes of AI: What are Data Annotators Doing and Whom Do They Work For?

Behind the Scenes of AI: What are Data Annotators Doing and Whom Do They Work For?

Behind the Scenes of AI: What are Data Annotators Doing and 

Whom Do They Work For? 

Sanja Tumbas, Assistant Professor, IESE Business School, Barcelona, Spain 

Brana Savic, PhD Candidate, Universitat Politècnica de Catalunya, Barcelona, Spain 

Extended Abstract 

The massive development of artificial intelligence (AI) and its implementation immensely 

affects tasks and occupational boundaries (Faraj et al. 2018). Recent attempts to depict the 

future of work show two extreme ends of the continuum – either scenarios of optimistic views 

of workless future (e.g. more leisure time) (Avent, 2016; 2014; Bregman, 2017; Dunlop, 2016; 

Frase, 2016; Graeber, 2015; Mason, 2015; Srnicek & Williams, 2015 ) or the scary promise of 

unemployment never seen before (Kaplan, 2015; Ford, 2015; Armstrong, 2014; Leonhard, 

2016 ). 

However, AI not only relies on highly skilled, well paid engineers and scientists but also on the 

less visible contribution of low-qualified, low paid workforce (Fleming 2018). Given the wide 

range of abilities assigned to machines the skills span from performing “cognitive functions 

such as perceiving, reasoning, learning, interacting with the environment, problem solving, 

decision-making, and even demonstrating creativity.” (Rai et al 2019). Hence, it is becoming 

extremely challenging to disentangle how and why a given machine learning algorithm takes 

a decision (Burrell 2016; von Krogh 2018). 

There are multiple AI tasks relevant for organizations ranging from activities concerned with 

input (data: sound, text, images, and numbers), task processes (algorithms), and task outputs 

(solutions and decisions) (von Krogh 2018). To conduct an in-depth analysis of the types of 

tasks involved, we explore the initial process concerned with data input. To allow for the variety 

in the analysis, our study draws on the understanding of human-AI hybrids (Rai et al 2018). 

Instead of the substitution relation between AI and humans, we are open to the view where 

humans and AI augment one another (Rai et al 2018). 

The primary goal is to gain new insights about different journeys that the input data follows in 

the process of feeding complex learning algorithms. The job profile often affiliated with the 

data input activity is named “data annotator.” To span a range of scenarios, we include the 

following three vignettes: 1. data annotators recruited through digital labour platforms (for 

example see Upwork) where they act as independent contractors paid by piece-rate, with 

varying levels of activity and engagement; 2. data annotators as full time workforce for 

technology companies’ that specialize in developing datasets for machine learning and 

artificial intelligence, (see Appen) and 3. data annotators as permanent employees of 

technology companies (such as Facebook) or other sectors (car manufacturers developing 

autonomous driving vehicles). 

In our analysis we code for tasks and competencies to reach a better understanding of 

required qualifications for Data Annotator positions using information reflected in job 

announcement description collected from two online platforms – Linkedin and Glassdor. 

Following a systematic process of data collection and documentation, analysing the data using 

qualitative content analyses techniques, a profile of the position is generated, which can be 

used as a basis for further analysis of the position. We derived over 500 tasks, knowledge, 

skill and ability statements from the job announcements, coded and grouped described 

competencies that occur in each announcement. 

Our initial results suggest that both, technical and non-technical skills are highly valued for the 

data annotator position. There is a strong focus of job requirements on problem solving skills, 

management, communication and interpersonal skills. Although each single data annotation 

task may be quick-to-do and unchallenging, our results provide compelling evidence that by 

working together with a wide variety of profiles, data annotators are providing valuable outputs 

contributing significantly to improvements of processing tools and new features, efficiency and 

data quality. 

Data annotators are involved in a variety of processes in AI production ranging from small, 

fragmented, remotely performed tasks of data annotation, e.g. answering simple questions, 

identifying objects on a photograph, tagging, labelling images, categorizing audio files, 

transcribing, correcting or copying short texts, sorting items in a list to data discovering and 

collection, metrics evaluation, revision of annotation toolset, identification of bottleneck, tools 

and processes improvement. These responsibilities apply to the task descriptions for both 

identified positions, data annotator and data annotation specialist, although each group has 

its own focus: the former with more repetitive and fragmented data annotation tasks and the 

later with more responsibilities in improvement of data quality and annotation process. 

Our data analysis is indicating high level of importance of requirements for collaborative efforts 

for data annotation position. Data annotators need to interact and work closely with different 

profiles and various departments in organization. S/he cooperates with engineering and 

organizational development teams providing them feedbacks on tooling and processes; data 

science and expert teams, to support their research and to assist in scientific data 

manipulation, analysis and visualisation; project managers and project leads to ensure that 

annotation meet project requirements and to achieve project goals. 

The production of AI is a labour-intensive process and data annotation tasks are increasingly 

complex and demand higher skills. Our analysis indicates that the need for data annotation is 

a structural one, bound to go along with the future development of the sector. 

References: 

Armstrong, S. (2014). Smarter than us: The rise of machine intelligence. Berkeley, CA: 

Machine Intelligence Research Institute. 

Avent, R. (2016). The wealth of humans: Work, power, and status in the twenty-first century. 

New York: St. Martin’s Press. 

Bregman, R. (2017). Utopia for realists: And how we can get there. London: Bloomsbury. 

Burrell, J. (2016). How the machine ‘thinks’: understanding opacity in machine learning 

algorithms. Big Data & Society, 3(1) 

Dunlop, T. (2016). Why the future is workless. Sydney, Australia: NewSouth Publishing. 

Faraj, S., Pachidi S., Sayegh, K. (2018). Working and Organizing in the age of learning 

algorithm. Information and Organization, 28, page(s) 62-70 

Fleming, P. (2019). Robots and Organization Studies: Why Robots Might Not Want to Steal 

Your Job. Organization Studies, Volume: 40 issue: 1, page(s): 23-38 

Ford, M. (2015). The rise of the robots: Technology and the threat of mass unemployment. 

London: Oneworld Publications. 

Frase, P. (2016). Four futures: Life after capitalism. London: Verso. 

Graeber, D. (2015). The utopia of rules: On technology, stupidity and the secret joys of 

bureaucracy. Brooklyn, NY: Melville House. 

Kaplan, J. (2015). Humans need not apply: A guide to wealth and work in the age of artificial 

intelligence. New Haven, CT: Yale University Press. 

Leonhard, G. (2016). Technology vs. humanity: The coming clash between man and machine. 

New York: Fast Future Publishing. 

Mason, P. (2015). Postcapitalism: A guide to our future. London: Allen Lane. 

Rai, A., Constantinides, P., Sarker, S. (2019). Next-generation digital platforms: Toward 

Human-AI hybrids. MIS Quarterly, Volume 43, No 1, page(s) iii-ix 

Srnicek, N., Williams, A. (2015). Inventing the future: Postcapitalism and a world without work. 

London: Verso. 

Von Krogh, G. (2018). Artificial intelligence in organizations: New opportunities for 

phenomenon-based theorizing. Academy of Management Discoveries, Volume 4, No. 4, 

page(s) 404–409