A Capabilities Approach to Studying Bias and Harm in Language Technologies
This addresses fairness and inclusion issues in NLP for underserved language communities, though it is incremental as it adapts an existing philosophical framework.
The paper tackles the problem of bias and harm in language technologies, particularly for non-English languages, by proposing the Capabilities Approach to center community needs and intersectional contexts, resulting in a framework for meaningful collaboration in defining and measuring harms.
Mainstream Natural Language Processing (NLP) research has ignored the majority of the world's languages. In moving from excluding the majority of the world's languages to blindly adopting what we make for English, we first risk importing the same harms we have at best mitigated and at least measured for English. However, in evaluating and mitigating harms arising from adopting new technologies into such contexts, we often disregard (1) the actual community needs of Language Technologies, and (2) biases and fairness issues within the context of the communities. In this extended abstract, we consider fairness, bias, and inclusion in Language Technologies through the lens of the Capabilities Approach. The Capabilities Approach centers on what people are capable of achieving, given their intersectional social, political, and economic contexts instead of what resources are (theoretically) available to them. We detail the Capabilities Approach, its relationship to multilingual and multicultural evaluation, and how the framework affords meaningful collaboration with community members in defining and measuring the harms of Language Technologies.