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Collaborating Authors

Abraham, Savitha Sam


Fairness in Clustering with Multiple Sensitive Attributes

arXiv.org Artificial Intelligence

A clustering may be considered as fair on pre-specified sensitive attributes if the proportions of sensitive attribute groups in each cluster reflect that in the dataset. In this paper, we consider the task of fair clustering for scenarios involving multiple multi-valued or numeric sensitive attributes. We propose a fair clustering method, FairKM (Fair K-Means), that is inspired by the popular K-Means clustering formulation. We outline a computational notion of fairness which is used along with a cluster coherence objective, to yield the FairKM clustering method. We empirically evaluate our approach, wherein we quantify both the quality and fairness of clusters, over real-world datasets. Our experimental evaluation illustrates that the clusters generated by FairKM fare significantly better on both clustering quality and fair representation of sensitive attribute groups compared to the clusters from a state-of-the-art baseline fair clustering method.


Combining Qualitative and Quantitative Reasoning for Solving Kinematics Word Problems

AAAI Conferences

This paper describes a system that combines qualitative and quantitative reasoning to solve kinematics word problems that are expressed in a simplified form of English. Such an integrated approach is useful in identifying the equations required to solve the problem and to infer certain implicit details in the problem scenario. The system also generates self-explanatory solutions that can assist a student in mastering the concept involved. We created a dataset of 30 problems from this domain. Such word problems have not been addressed in recent times.


Abraham

AAAI Conferences

This paper describes a system that uses a hybrid of quantitative and qualitative knowledge to solve physics word problems. Such an integration of knowledge from two models is useful to come up with the correct solution for many of these problems. We have applied this hybrid model to solve word problems from projectile motion. These types of word problems have not been addressed in recent times. We have solved a set of 30 problems in this domain.


Hybrid of Qualitative and Quantitative Knowledge Models for Solving Physics Word Problems

AAAI Conferences

This paper describes a system that uses a hybrid of quantitative and qualitative knowledge to solve physics word problems. Such an integration of knowledge from two models is useful to come up with the correct solution for many of these problems. We have applied this hybrid model to solve word problems from projectile motion. These types of word problems have not been addressed in recent times. We have solved a set of 30 problems in this domain.