The ever-growing need to handle distributed data with high volume, complexity, and heterogeneity has resulted in an increased demand for augmented analytics. This demand aims to automate processes involved in understanding, analysing, and extracting knowledge from data using artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and other data science technologies. However, the ultimate measure of success in data science lies in ensuring users' trust, thereby introducing new challenges in achieving trustworthy data science.
As a researcher, my work focuses on AI-enhanced data preprocessing, analysis, information extraction, and knowledge harvesting across various applications such as information retrieval, data federation, sentiment analysis, and energy consumption. Additionally, I address the issue of trustworthiness in data science by implementing solutions with privacy and fairness awareness, thereby facilitating trustworthy AI-powered decision-making.
During this lecture, I will discuss the conflicts and synergy between augmented analytics and trustworthiness in data science. I will present studies where knowledge harvesting through augmented analytics was conducted and emphasize the identified issues related to trustworthiness, with a specific focus on privacy and fairness problems.
Associate professor at Department of Computing Science