Search Results for author: Sachin Grover

Found 6 papers, 2 papers with code

Inter-annotator agreement is not the ceiling of machine learning performance: Evidence from a comprehensive set of simulations

no code implementations BioNLP (ACL) 2022 Russell Richie, Sachin Grover, Fuchiang (Rich) Tsui

It is commonly claimed that inter-annotator agreement (IAA) is the ceiling of machine learning (ML) performance, i. e., that the agreement between an ML system’s predictions and an annotator can not be higher than the agreement between two annotators.

BIG-bench Machine Learning

Characterizing Human Explanation Strategies to Inform the Design of Explainable AI for Building Damage Assessment

no code implementations4 Nov 2021 Donghoon Shin, Sachin Grover, Kenneth Holstein, Adam Perer

Explainable AI (XAI) is a promising means of supporting human-AI collaborations for high-stakes visual detection tasks, such as damage detection tasks from satellite imageries, as fully-automated approaches are unlikely to be perfectly safe and reliable.

COVID-19 India Dataset: Parsing COVID-19 Data in Daily Health Bulletins from States in India

1 code implementation27 Sep 2021 Mayank Agarwal, Tathagata Chakraborti, Sachin Grover, Arunima Chaudhary

While India has been one of the hotspots of COVID-19, data about the pandemic from the country has proved to be largely inaccessible at scale.

Model Elicitation through Direct Questioning

no code implementations24 Nov 2020 Sachin Grover, David Smith, Subbarao Kambhampati

We show how to generate questions to refine the robot's understanding of the teammate's model.

Plan Explanations as Model Reconciliation -- An Empirical Study

no code implementations3 Feb 2018 Tathagata Chakraborti, Sarath Sreedharan, Sachin Grover, Subbarao Kambhampati

Recent work in explanation generation for decision making agents has looked at how unexplained behavior of autonomous systems can be understood in terms of differences in the model of the system and the human's understanding of the same, and how the explanation process as a result of this mismatch can be then seen as a process of reconciliation of these models.

Decision Making Explanation Generation

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