no code implementations • 11 Jul 2023 • Normen Yu, Gang Tan, Saeid Tizpaz-Niari
This thesis explores open-sourced machine learning (ML) model explanation tools to understand whether these tools can allow a layman to visualize, understand, and suggest intuitive remedies to unfairness in ML-based decision-support systems.
no code implementations • 9 Apr 2023 • Verya Monjezi, Ashutosh Trivedi, Gang Tan, Saeid Tizpaz-Niari
Guided by the quantitative fairness, we present a causal debugging framework to localize inadequately trained layers and neurons responsible for fairness defects.
2 code implementations • 13 Feb 2022 • Saeid Tizpaz-Niari, Ashish Kumar, Gang Tan, Ashutosh Trivedi
This paper investigates the parameter space of machine learning (ML) algorithms in aggravating or mitigating fairness bugs.
no code implementations • 17 Feb 2020 • Michael Norris, Berkay Celik, Patrick McDaniel, Gang Tan, Prasanna Venkatesh, Shulin Zhao, Anand Sivasubramaniam
IoT devices are decentralized and deployed in un-stable environments, which causes them to be prone to various kinds of faults, such as device failure and network disruption.
Software Engineering Performance
1 code implementation • 18 Sep 2018 • Z. Berkay Celik, Earlence Fernandes, Eric Pauley, Gang Tan, Patrick McDaniel
Based on a study of five IoT programming platforms, we identify the key insights resulting from works in both the program analysis and security communities and relate the efficacy of program-analysis techniques to security and privacy issues.
Cryptography and Security Programming Languages
1 code implementation • 22 Feb 2018 • Z. Berkay Celik, Leonardo Babun, Amit K. Sikder, Hidayet Aksu, Gang Tan, Patrick McDaniel, A. Selcuk Uluagac
Through this effort, we introduce a rigorously grounded framework for evaluating the use of sensitive information in IoT apps---and therein provide developers, markets, and consumers a means of identifying potential threats to security and privacy.
Cryptography and Security Programming Languages