Title of the project
Explainable Machine Learning
Background and summary of fellowship:
Data has all the information, and efficient processing of data for information extraction is a key to achieving the right decision. Computers help to understand the data, extract important information and then finally provide a decision. Computers use a particular tool from the engineering field of computer science, called machine learning for realizing the help. The use of machine learning is growing, from speech recognition to robots to autonomous cars to medical fields including life science data analysis. Today machine learning is at the core of many intelligent systems across all science and engineering fields. Naturally, machine learning has to be highly reliable. Thanks to the Digital Futures fellowship, I am fortunate to address a challenge in modern machine learning. The challenge is how to make the machine learning fields more trustworthy and unbiased. For example, a visual camera-based face recognition system should not discriminate against people due to skin colour or gender.
Towards the challenge, a prime concern is to develop explainable machine learning (xML) systems, closely related to explainable artificial intelligence (xAI). Preferably, users should be able to understand what can be the precise effect or outcome of the systems before their formal use. Mistakes after formal use are costly in many situations, for example, detection of infection in a clinic/hospital. We should fully understand how computers use data for information extraction, and then reach a decision using the information. In turn, how computers can explain actions to users. The development of xML/xAI requires a confluence of mathematics, computer science and real-life understanding of applications scenarios including user perspectives.
Asssociate Professor, Division of Information Science and Engineering at KTH, Co-PI of research project Data-Limited Learning of Complex Dynamical Systems at Digital Futures+46 8 790 84 78