Detecting Stereotypes in Human Computational Tasks (DESCANT)

Research / Projects / Detecting Stereotypes in Human Computational Tasks (DESCANT)

Follow our updates:


Zenodo: Collection of publications

CYENS Webpage, Facebook, and Twitter

CyCAT Webpage, Facebook, and Twitter

DESCANT shall contribute to the smart growth of R&D in Cyprus, as its objectives are in line with the Smart Specialization Strategy, which designates ICT as a horizontal priority, as well as Cyprus’ Digital Strategy Goals and specifically, Digital Entrepreneurship.  

Crowdsourcing has enabled the development of novel “hybrid human-machine information systems,” which benefit from including humans in the loop when facing computational tasks that are still better solved by humans than machines. However, hybrid systems are only as good as the data with which they are built and ensuring the quality of workers’ contributions is often non-trivial. DESCANT addresses a particular concern for crowdsourced data quality: the expression of social stereotypes in the data collected.  

There is no shortage of media coverage on popular hybrid systems (e.g., search engines, machine translators, chat bots) that have been observed exhibiting sexist or racist behaviours. At the same time, the research community is making serious attempts to better understand how social biases end up in these systems and what can and should be done to redress this. Furthermore, recent research has made it clear that systems and algorithms trained on human-produced data exhibit the same implicit biases – such as the expression of racial and gender stereotypes – that humans do. Although the source of the training data for these systems varies, DESCANT focuses specifically on systems that leverage paid micro-tasks crowdsourcing.  

The project extends upon previous work, which focused on developing conceptual and computational models to detect stereotypes in search engine results. In DESCANT, the approach, carrying out experiments on micro-tasks involving a number of different media (i.e., judgments of a variety of characteristics of images, sound files, and video) will be generalised. Based on the results, a set of guidelines for researchers and entrepreneurs who use micro-task crowdsourcing will be developed, which will help to understand how the design of human intelligence tasks might result in stereotyped data and will offer alternative solutions depending on their goals. 

The project is coordinated by Dr. Jahna Otterbacher, PI of the Transparency in Algorithms Group (TAG), within the CYENS Centre of Excellence (formerly RISE), and Associate Professor at the Open University of Cyprus (OUC), where she leads the Cyprus Center for Algorithmic Transparency (CyCAT). The DESCANT project pairs up the local CYENS/CyCAT team with researchers from the University of Queensland (UQ), which is consistently ranked as one of the world’s top 50 universities.  

Contact Person: 

Associate Professor Jahna Otterbacher



Project Information:





Project Funding:  

€ 149,980 


24 months 

Starting Date:  


End Date:  




Open University of Cyprus

University of Queensland 

More Projects
Poultry Farm Intelligence
PoultryFI aims to advance the proof-of-concept technology for poultry farm monitoring previously...
09 September. 2022
Hospital Transformation through Artificial Intelligence
HOSPITAIL aims to expand an existing platform for monitoring patients in ICUs
09 September. 2022
Atherorisk Project
“Identification of unstable carotid plaques associated with symptoms using ultrasonic image...
01 September. 2022
EU Horizon - RIA
SHARESPACE will demonstrate a radically new technology for promoting social interaction in...
10 August. 2022
EU Horizon - XR4ED
The project will boost the deployment of innovative XR applications for learning, training, and...
10 August. 2022
CY RIF SEED- SmartCyclo Platform
SmartCyclo Platform aims to offer a ground-breaking solution that will revolutionise the way...
10 August. 2022