Understanding Public Perceptions for Applied Data Science

Research on data science is often approached from the angle of making AI more reliable, accurate, explainable and actionable. These outcomes contribute towards the broader objectives of making AI more trustworthy to facilitate its adoption and use. However, trust is a multi-faceted issue which is influenced by a variety of factors, including understanding, familiarity, perceptions of risk and credibility.

In the development of AI technologies, the public can be a resource and play a positive role in contributing their data for the advancement of data science, or be a barrier to deployment if they do not accept the technology or solution due to their misperceptions of risk. The role of the public is particularly important in industries such as healthcare and consumer products, where the public as end-users have
closer proximity to the AI application and significant implications on its adoption. Apart from making the science more understandable to the public, there is increasing need for the science to better understand the public as well. When and why is it important for experts to understand the public perceptions of risk? How can data scientists work with implementation partners to better understand end-users and address concerns which may inhibit adoption? These questions were explored in the inaugural UPP4DS workshop organised in-conjunction with the prestigious 2021 SIG-KDD conference.

The workshop was held on 13 August 2021. Over 30 researchers, practitioners and civil society representatives joined the workshop to examine the role of society in the development of acceptable technologies. Download the post-workshop report here.