5 papers
Fairness under uncertainty in sequential decisions
Michelle Seng Ah Lee, Kirtan Padh, David Watson +2
Fair machine learning (ML) methods help identify and mitigate the risk that algorithms encode or automate social injustices. Algorithmic approaches alone cannot resolve structural…
Auditing a Dutch Public Sector Risk Profiling Algorithm Using an Unsupervised Bias Detection Tool
Floris Holstege, Mackenzie Jorgensen, Kirtan Padh +4
Algorithms are increasingly used to automate or aid human decisions, yet recent research shows that these algorithms may exhibit bias across legally protected demographic groups. H…
Cluster-Dags as Powerful Background Knowledge For Causal Discovery
Jan Marco Ruiz de Vargas, Kirtan Padh, Niki Kilbertus
Finding cause-effect relationships is of key importance in science. Causal discovery aims to recover a graph from data that succinctly describes these cause-effect relationships. H…
Can AI be Auditable?
Himanshu Verma, Kirtan Padh, Eva Thelisson
Auditability is defined as the capacity of AI systems to be independently assessed for compliance with ethical, legal, and technical standards throughout their lifecycle. The chapt…
Your Assumed DAG is Wrong and Here's How To Deal With It
Kirtan Padh, Zhufeng Li, Cecilia Casolo +1
Assuming a directed acyclic graph (DAG) that represents prior knowledge of causal relationships between variables is a common starting point for cause-effect estimation. Existing l…