The ability for machine learning to exacerbate bias has led to many algorithms centered on fairness. For example, fair clustering algorithms typically focus on balanced representation of protected attributes within clusters . Here, we develop a fair clustering variant where the input data is a hypergraph with multiple edge types , representing information about past experiences of groups of individuals . Our method is based on diversity of experience, instead ofprotected attributes , with a goal of forming groups that have both experience and diversity with respect to participation in edge types. We model this goal with a regularized edge-based  clustering objective,  design an efficient 2-approximation algorithm for optimizing the NP-hard objective, and provide bounds on hyperparameters to avoid trivial solutions . We demonstrate a potential application of this framework in online review platforms, where the goal is to curate sets of user reviews for a product type. In this context, "experience" corresponds to users familiar with the type of product, and "diversity" to users that have reviewed related products.
When forming a team or group of individuals, we often seek a balance of expertise in a particular task while at the same time maintaining diversity of skills within each group . Here, we view the problem of finding diverse and experienced groups as clustering in hypergraphs with multiple edge types. The input data is a hypergraph with multiple hyperedge types -- representing information about past experiences of groups of individuals -- and the output is groups of nodes. In contrast to related problems on fair or balanced clustering, we model diversity in terms of variety of past experience (instead of, e.g., protected attributes) , with a goal of forming groups that have both experience and diversity with respect to participation in edge types. In other words, both diversity and experience are measured from the types of the hyperedges. Our clustering model is based on a regularized version of an edge-based hypergraph clustering objective, and we also show how naive objectives actually have no diversity-experience tradeoff. Although our objective function is NP-hard to optimize, we design an efficient 2-approximation algorithm and also show how to compute bounds for the regularization hyperparameter that lead to meaningful diversity-experience tradeoffs . We demonstrate an application of this framework in online review platforms, where the goal is to curate sets of user reviews for a product type. In this context, "experience" corresponds to users familiar with the type of product, and "diversity" to users that have reviewed related products.
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The ability for machine learning to exacerbate bias has led to many algorithms centered on fairness. For example, fair clustering algorithms typically focus on balanced representation of protected attributes within clusters
When forming a team or group of individuals, we often seek a balance of expertise in a particular task while at the same time maintaining diversity of skills within each group
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develop a fair clustering variant where the
view the problem of finding diverse and experienced groups as clustering in hypergraphs with multiple edge types. The
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edge types ,
hyperedge types --
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. Our method is based on diversity of experience, instead ofprotected attributes
-- and the output is groups of nodes. In contrast to related problems on fair or balanced clustering, we model diversity in terms of variety of past experience (instead of, e.g., protected attributes)
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We model this goal with a regularized
In other words, both diversity and experience are measured from the types of the hyperedges. Our clustering model is based on a regularized version of an
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hypergraph
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and we also show how naive objectives actually have no diversity-experience tradeoff. Although our objective function is NP-hard to optimize, we
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for optimizing the NP-hard objective, and provide bounds on hyperparameters to avoid trivial solutions
and also show how to compute bounds for the regularization hyperparameter that lead to meaningful diversity-experience tradeoffs
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a potential
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