3 citations · 4 across the 3 of their papers we have counts for
6 papers
FASTER-CE: Fast, Sparse, Transparent, and Robust Counterfactual Explanations
Shubham Sharma, Alan H. Gee, Jette Henderson +1
Counterfactual explanations have substantially increased in popularity in the past few years as a useful human-centric way of understanding individual black-box model predictions.…
FEAMOE: Fair, Explainable and Adaptive Mixture of Experts
Shubham Sharma, Jette Henderson, Joydeep Ghosh
Three key properties that are desired of trustworthy machine learning models deployed in high-stakes environments are fairness, explainability, and an ability to account for variou…
FaiR-N: Fair and Robust Neural Networks for Structured Data
Shubham Sharma, Alan H. Gee, David Paydarfar +1
Fairness in machine learning is crucial when individuals are subject to automated decisions made by models in high-stake domains. Organizations that employ these models may also ne…
Explainable Machine Learning in Deployment
Umang Bhatt, Alice Xiang, Shubham Sharma +7
Explainable machine learning offers the potential to provide stakeholders with insights into model behavior by using various methods such as feature importance scores, counterfactu…
CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models
Shubham Sharma, Jette Henderson, Joydeep Ghosh
As artificial intelligence plays an increasingly important role in our society, there are ethical and moral obligations for both businesses and researchers to ensure that their mac…
Verity: Blockchains to Detect Insider Attacks in DBMS
Shubham S. Srivastava, Medha Atre, Shubham Sharma +2
Integrity and security of the data in database systems are typically maintained with access control policies and firewalls. However, insider attacks -- where someone with an intima…