no code implementations • 12 Aug 2021 • Nitin Gupta, Hima Patel, Shazia Afzal, Naveen Panwar, Ruhi Sharma Mittal, Shanmukha Guttula, Abhinav Jain, Lokesh Nagalapatti, Sameep Mehta, Sandeep Hans, Pranay Lohia, Aniya Aggarwal, Diptikalyan Saha
We attempt to re-look at the data quality issues in the context of building a machine learning pipeline and build a tool that can detect, explain and remediate issues in the data, and systematically and automatically capture all the changes applied to the data.
Contact Tracing has been used to identify people who were in close proximity to those infected with SARS-Cov2 coronavirus.
To preserve anonymity and obfuscate their identity on online platforms users may morph their text and portray themselves as a different gender or demographic.
8 code implementations • 3 Oct 2018 • Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, Yunfeng Zhang
Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking.
Automatic extraction of fairness policies, or in general, any specific kind of policies from large legal corpus can be very useful for the study of bias and fairness in the context of AI applications.
no code implementations • 22 Aug 2018 • Matthew Arnold, Rachel K. E. Bellamy, Michael Hind, Stephanie Houde, Sameep Mehta, Aleksandra Mojsilovic, Ravi Nair, Karthikeyan Natesan Ramamurthy, Darrell Reimer, Alexandra Olteanu, David Piorkowski, Jason Tsay, Kush R. Varshney
We envision such documents to contain purpose, performance, safety, security, and provenance information to be completed by AI service providers for examination by consumers.
The presence of gender stereotypes in many aspects of society is a well-known phenomenon.
Vast availability of text data has enabled widespread training and use of AI systems that not only learn and predict attributes from the text but also generate text automatically.
Deep neural networks (DNNs) are vulnerable to malicious inputs crafted by an adversary to produce erroneous outputs.
In this work, we present a cloud-based extraction monitor that can quantify the extraction status of models by observing the query and response streams of both individual and colluding adversarial users.
In this work, we have worked with movie data from Wikipedia plots and movie trailers from YouTube.
Machine learning based system are increasingly being used for sensitive tasks such as security surveillance, guiding autonomous vehicle, taking investment decisions, detecting and blocking network intrusion and malware etc.