Keynote: The crossroads of AI fairness, accountability and transparency in the industry, healthcare, education, and government

Modern machine learning techniques contribute to the massive automation of data-driven decision-making and decision support. Multiple examples from different industries, healthcare, education, and government illustrate the challenges of developing and making use of trustworthy and human-centered AI. It becomes better understood and accepted, in particular, due to the new General Data Protection Regulation (GDPR), that employed predictive models may need to be audited. Disregarding whether we deal with so-called black-box models (e.g. deep learning) or more interpretable models (e.g. decision trees), answering even basic questions like “why is this model giving these answers?” and “how do particular features affect the model output?” is nontrivial. In reality, auditors need tools not just to explain the decision logic of an algorithm, but also to uncover and characterize undesired or unlawful biases in predictive model performance, e.g. by law hiring decisions cannot be influenced by race or gender. In this talk, I will give a brief overview of the different facets of comprehensibility of predictive analytics and reflect on the current state-of-the-art and further research needed for gaining a deeper understanding of what it means for predictive analytics to be truly transparent, fair, and accountable. I will also reflect on the necessity to study the utility of the methods for interpretable predictive analytics.

Lecturer

Dr. Mykola Pechenizkiy

His core expertise and research interests are in predictive analytics and its application to real-world problems in industry, healthcare, and education. Mykola leads Trustworthy AI interdisciplinary research studying foundations of robustness, safety, trust, reliability, scalability, interpretability, and explainability of AI; developing novel techniques for informed, accountable and transparent predictive and prescriptive analytics; and demonstrating their ecological validity in practice in collaboration with industrial partners. Over the past decade, Mykola has co-authored more than 100 peer-reviewed publications and served on the program committees of the leading data mining and AI conferences.