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Master of Science in Computer Science

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https://hdl.handle.net/1853/73328
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Publication Search Results

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    Mitigating Racial Biases in Toxic Language Detection
    (Georgia Institute of Technology, 2022-05-05) Halevy, Matan
    Recent research has demonstrated how racial biases against users who write African American English exists in popular toxic language datasets. While previous work has focused on a single fairness criteria, we propose to use additional descriptive fairness metrics to better understand the source of these biases. We demonstrate that different benchmark classifiers, as well as two in-process bias-remediation techniques, propagate racial biases even in a larger corpus. We then propose a novel ensemble-framework that uses a specialized classifier that is fine-tuned to the African American English dialect. We show that our proposed framework substantially reduces the racial biases that the model learns from these datasets. We demonstrate how the ensemble framework improves fairness metrics across all sample datasets with minimal impact on the classification performance, and provide empirical evidence to its ability to unlearn the annotation biases towards authors who use African American English. ** Please note that this work may contain examples of offensive words and phrases.
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    Virtual Reality as a Stepping Stone to Real-World Robotic Caregiving
    (Georgia Institute of Technology, 2021-05-04) Gu, Yijun
    Versatile robotic caregivers could benefit millions of people worldwide, including older adults and people with disabilities. Recent work has explored how robotic caregivers can learn to interact with people through physics simulations, yet transferring what has been learned to real robots remains challenging. By bringing real people into the robot's virtual world, virtual reality (VR) has the potential to help bridge the gap between simulations and the real world. In this thesis, we present Assistive VR Gym (AVR Gym), which enables real people to interact with virtual assistive robots. We also provide evidence that AVR Gym can help researchers improve the performance of simulation-trained assistive robots with real people. Prior to AVR Gym, we trained robot control policies (\emph{Original Policies}) solely in simulation for four robotic caregiving tasks (robot-assisted feeding, drinking, itch scratching, and bed bathing) with two simulated robots (PR2 from Willow Garage and Jaco from Kinova). With AVR Gym, we developed \emph{Revised Policies} based on insights gained from testing the Original policies with real people. Through a formal study with eight participants in AVR Gym, we found that the Original policies performed poorly, the Revised policies performed significantly better, and that improvements to the biomechanical models used to train the Revised policies resulted in simulated people that better match real participants. Notably, participants significantly disagreed that the Original policies were successful at assistance, but significantly agreed that the Revised policies were successful at assistance. Overall, our results suggest that VR can be used to improve the performance of simulation-trained control policies with real people without putting people at risk, thereby serving as a valuable stepping stone to real robotic assistance.
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    Code-Upload AI Challenges on EvalAI
    (Georgia Institute of Technology, 2021-05-04) Jain, Rishabh
    Artificial intelligence develops techniques and systems whose performance must be evaluated on a regular basis in order to certify and foster progress in the discipline. We have developed several tools such as EvalAI which helps us in evaluating the performance of these systems and to push the frontiers of machine learning and artificial intelligence. Initially, the AI community focussed on simple and traditional methods of evaluating these systems in the form of prediction upload challenges but with the advent of deep learning, larger datasets, and complex AI agents, etc. these methods are not sufficient for evaluation. A technique to evaluate these AI agents is by uploading their code, running it on the sequestered test dataset, and reporting the results on the leaderboard. In this work, we introduced code upload evaluation of AI agents on EvalAI for all kinds of AI tasks, i.e.reinforcement learning, supervised learning, and unsupervised learning. We offer features such as scalable backend, prioritized submission evaluation, secure test environment, and running AI agents code in an isolated sanitized environment. The end-to-end pipeline is extremely flexible, modular, and portable which can later be extended to multi-agents setups and evaluation on dynamic datasets. We also proposed a procedure using GitHub for AI challenge creation to version, maintain, and reduce the friction in this conglomerate process. Finally, we focused on providing analytics to all the users of the platform along with easing the hosting of EvalAI on private servers as an internal evaluation platform.