Search Results for author:
Found 6 papers, 3 papers with code
FairLay-ML: Intuitive Remedies for Unfairness in Data-Driven Social-Critical Algorithms
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.
Information-Theoretic Testing and Debugging of Fairness Defects in Deep Neural Networks
Guided by the quantitative fairness, we present a causal debugging framework to localize inadequately trained layers and neurons responsible for fairness defects.
Fairness-aware Configuration of Machine Learning Libraries
This paper investigates the parameter space of machine learning (ML) algorithms in aggravating or mitigating fairness bugs.
IoTRepair: Systematically Addressing Device Faults in Commodity IoT (Extended Paper)
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
Program Analysis of Commodity IoT Applications for Security and Privacy: Challenges and Opportunities
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
Sensitive Information Tracking in Commodity IoT
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
