Sevvandi Kandanaarachchi

Generates (U,W) mixture graphs where U is a line graph graphon and W is a dense graphon. Graphons are graph limits and graphon U can be written as sequence of positive numbers adding to 1. Graphs are sampled from U and W and joined randomly to obtain the mixture graph. Given a mixture graph, U can be inferred. Kandanaarachchi and Ong (2025) .

The experimental validation of algorithms depends strongly on the characteristics of the test set used. Ideally, such a set should exhibit diverse characteristics that challenge an algorithm and are present in real-world problems. An approach to examine the diversity and representativeness of a test set is Instance Space Analysis, which uses a 2D projection to visualise the test set while identifying the strengths and weaknesses of competing algorithms. However, this has the limitation of discarding potentially useful information while crowding out the space as more features and algorithms are considered. This paper describes an extension of Instance Space Analysis into 3D, which retains a higher degree of information while maintaining the explainability of the visualisation by finding the rotations that maximise the linear trends. In addition to the expansion to 3D, a new algorithm for identifying portfolio footprints is introduced, offering a more robust and reliable method for identifying footprints in both 2D and 3D instance spaces. As a case study, we present a performance analysis of unsupervised anomaly detection methods, subject to changes in the normalisation technique. The results demonstrate the advantages by identifying regions of strength of algorithms previously thought to be underpowered.

As AI systems increasingly influence various aspects of human life, it is critical to ensure their development and deployment align with ethical standards and societal values. Our paper argues that integrating expertise from Humanities, Social, and Economic Sciences (HSES) into AI development is essential to achieving responsible AI. We present four compelling reasons to advocate for this integration: enhancing social legitimacy, ensuring meaningful impact, strengthening credibility and capability building. These reasons emerged from a collaborative effort involving 16 researchers from AI and HSES fields. Together, we explored the enablers and barriers to integrating our knowledge for the purpose of developing effective, responsible, and socially grounded AI products. We aim to inspire others to adopt an integrated approach to AI development, promoting innovations that are both technologically advanced and aligned with societal needs.