Voice as Data: Learning from What People Say

Development is fundamentally about understanding people, their Motivations, behaviors and reactions. We have two primary means of understanding people— observing what they do, and what they say. As the AI4D community has noted, people’s increased use of mobile devices has led to a wealth of new data relevant to these topics. We are on the cusp of developing incredibly powerful tools that can help us understand how human beings migrate, transact and acquire wealth. This could have a large impact on how we determine policies and allocate resources. Most of this analysis has tended to focus on what people do — where they go, who they talk to, what they buy, etc. (Eagle and (Sandy) Pentland 2006). I argue that what people say is an equally rich source of development data, often containing information that cannot be obtained from people’s actions, such as their needs, hopes and aspirations. Voice is the most natural form of communication, especially for people who speak a non-mainstream language, and/or have marginal literacy skills. These are often exactly those populations who are most disenfranchised, and therefore most need their voices to be heard. Mobile phones are a natural channel for improving voice communications between development institutions and the communities they serve. However, voice communications is traditionally 1-to-1, or 1-to-many, but not many-to-many. In general, it is still difficult to process and present large amounts of heterogeneous voice content. There are several challenges to this. First, there are obvious privacy concerns. Voice content is also inherently difficult to aggregate, structure, search or scan. In this paper, I describe how my group is exploring the use of voice as a public knowledge medium. I also discuss some approaches to managing large amounts of voice content, leveraging a combination of user feedback, collective human intelligence and intelligent automation.