6 citations · 10 across the 6 of their papers we have counts for
8 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…
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…
Auditing a Dutch Public Sector Risk Profiling Algorithm Using an Unsupervised Bias Detection Tool
Floris Holstege, Mackenzie Jorgensen, Kirtan Padh +5
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…
Toward Responsible AI Use: Considerations for Sustainability Impact Assessment
Eva Thelisson, Grzegorz Mika, Quentin Schneiter +2
As AI/ML models, including Large Language Models, continue to scale with massive datasets, so does their consumption of undeniably limited natural resources, and impact on society.…
Stochastic Causal Programming for Bounding Treatment Effects
Kirtan Padh, Jakob Zeitler, David Watson +3
Causal effect estimation is important for many tasks in the natural and social sciences. We design algorithms for the continuous partial identification problem: bounding the effect…
Addressing Fairness in Classification with a Model-Agnostic Multi-Objective Algorithm
Kirtan Padh, Diego Antognini, Emma Lejal Glaude +2
The goal of fairness in classification is to learn a classifier that does not discriminate against groups of individuals based on sensitive attributes, such as race and gender. One…