Sameep Mehta

TL;DR: A new open source Python toolkit for algorithmic fairness, AI Fairness 360 (AIF360), released under an Apache v2.0 license to help facilitate the transition of fairness research algorithms to use in an industrial setting and to provide a common framework for fairness researchers to share and evaluate algorithms.

TL;DR: This paper envisiones an SDoC for AI services to contain purpose, performance, safety, security, and provenance information to be completed and voluntarily released by AI service providers for examination by consumers.

TL;DR: This paper proposes a new method of crafting adversarial text samples by modification of the original samples, which works best for the datasets which have sub-categories within each of the classes of examples.

TL;DR: These findings show that coupling the detection and anti-rumor strategy by embedding agents in the network, the authors call them beacons, is an effective means of fighting the spread of rumor, even if these beacons do not share information.

TL;DR: A model extraction monitor that quantifies the extraction status of models by continually observing the API query and response streams of users is introduced and two novel strategies that measure either the information gain or the coverage of the feature space spanned by user queries to estimate the learning rate of individual and colluding adversaries are presented.