A lot of recent research attempts to solve—or at least mitigate—the training data scarcity problem from a technical standpoint. But there hasn’t been as much focus on the potential social consequences of actually implementing MT systems where they’re assumed to be useful.
This limited focus reflects a common critique of mainstream global development—namely, the singular reliance on technical solutions and the disregard for their social impacts. In fact, research has shown that development initiatives that introduce new information and communication technologies (ICTs) may actually worsen inequalities for marginalized communities when they are designed without consideration of social impacts.
My presentation contemplated what conditions would be latvia mobile database necessary for low-resource MT systems to avoid exacerbating social inequalities, and suggested some ways these systems could help local communities take control of their own well-being.
I adopted that term from Chipidza and Leidner, who define power parity as “equality in the control of resources and information”. According to them, the implementation of ICTs—like machine translation—into development settings can boost power parity by satisfying two conditions. First, ICTs must strengthen local communities’ ability to define their own views, needs, and goals. And second, local communities’ use of ICTs must not require a long-term reliance on resources, funding, or expertise provided by external actors.
Technologies that fail these criteria will lead to an imbalance of power. Low-resource MT in humanitarian settings is particularly susceptible to these risks, given the enormous gap between language technologies for high-resource and low-resource languages, as well as the humanitarian sector’s strong bias toward lingua francas like English.