Machine learning and deep analytics for biocomputing: call for better explainability

The goals of this workshop are to discuss challenges in explainability of current Machine Leaning and Deep Analytics (MLDA) used in biocomputing and to start the discussion on ways to improve it. We define explainability in MLDA as easy to use information explaining why and how the MLDA approach made its decisions. We believe that much greater effort is needed to address the issue of MLDA explainability because of: 1) the ever increasing use and dependence on MLDA in biocomputing including the need for increased adoption by non-MLD experts; 2) the diversity, complexity and scale of biocomputing data and MLDA algorithms; 3) the emerging importance of MLDA-based decisions in patient care, in daily research, as well as in the development of new costly medical procedures and drugs. This workshop aims to: a) analyze and challenge the current level of explainability of MLDA methods and practices in biocomputing; b) explore benefits of improvements in this area; and c) provide useful and practical guidance to the biocomputing community on how to address these challenges and how to develop improvements. The workshop format is designed to encourage a lively discussion with panelists to first motivate and understand the problem and then to define next steps and solutions needed to improve MLDA explainability.