Acknowledgement for research promoting explainable AI

Tools developed by computer scientists make it possible to understand why machine learning methods function as they do. 

Computer scientists from the University of Helsinki won the best paper award with their study entitled (Björklund et al. 2024) at the held in Stockholm in April.  

The award-winning study describes a method that can be used to explain how complex machine learning and artificial intelligence methods work. The method makes it possible to understand how the machine learning methods make predictions for individual data points. 

The SLIPMAP method creates a visualisation of the data as a non-linear projection, such that the machine learning method uses the same simple models for data points projected close to one another.  

“We designed a machine learning algorithm, studied its computational properties and verified the behaviour by experimenting with the algorithm in various situations. We used publicly available datasets, open-source software, and the University’s high-performance computing environment,” says Doctoral Researcher .

Use as a predictive model  

The researchers have the software under an open source license.  

According to Professor , the study improved a . 

“The new method is considerably faster and works better with noisy data. It can also be used as a predictive machine learning model,” says Doctoral Researcher . 

The same methods are used by the , a Centre of Excellence funded by the Research Council of Finland. Among other things, the methods are used to study atmospheric molecules.  

The award-winning study is a continuation of Anton Björklund’s recently completed doctoral thesis entitled ‘Interpretable and explainable machine learning for natural sciences’, for which the Faculty has granted permission for public defence.

Publication: 

Björklund, A., Seppäläinen, L., Puolamäki, K., 2024. SLIPMAP: Fast and Robust Manifold Visualisation for Explainable AI, in: Miliou, I., Piatkowski, N., Papapetrou, P. (Eds.), Advances in Intelligent Data Analysis XXII, Lecture Notes in Computer Science. Springer Nature Switzerland, Cham, pp. 223–235.