This article specifies and explores the hypothesis that the diversity of human languages, right now a barrier to “interoperability” in communication and trade, will become significantly less of a barrier as machine translation technologies are deployed over the next several years. We argue that machine translation will become the 2020's analogy for ideas, to what container shipping did for goods trade in the second half of the twentieth century. But as with container shipping or railroads in the nineteenth century, this new boundary-cost and transaction-cost reducing technology does not reduce all boundary and transaction costs equally, and so creates new challenges for the distribution of ideas and thus for innovation and economic growth. How we develop, license, commercialize, and deploy machine translation will be a critical determinant of its impact on trade, political coalitions, diversity of thought and culture, and the distribution of wealth.
Machine Translation (MT) has the potential to help people overcome language barriers and is widely used in high-stakes scenarios, such as in hospitals. However, in order to use MT reliably and safely, users need to understand when to trust MT outputs and how to assess the quality of often imperfect translation results. In this paper, we discuss research directions to support users to calibrate trust in MT systems. We share findings from an empirical study in which we conducted semi-structured interviews with 20 clinicians to understand how they communicate with patients across language barriers, and if and how they use MT systems. Based on our findings, we advocate for empirical research on how MT systems are used in practice as an important first step to address the challenges in building appropriate trust between users and MT tools.
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