Contextual Analytics: Towards a Practical Integration of Human and Data Science Approaches in the Development of Algorithms

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As algorithms play an increasingly important role in the lives of people and corporations, finding more effective, ethical, and empathetic ways of developing them has become an industry imperative. Ethnography, and the contextual understanding derived from it, has the potential to fundamentally change the way that data science is done. Reciprocally, engaging with data science can help ethnographers focus their efforts, build stronger and more precise insights, and ultimately have greater impact once their work is incorporated into the algorithms that increasingly power our society. In practice, building contextually-informed algorithms requires collaboration between human science and data science teams who are willing to extend their frame of reference beyond their core skill areas. This paper aims to first address the features of ethnography and data science that make collaboration between the two more valuable than the sum of their respective parts; second, to present a methodology that makes collaboration between the two possible in practical terms; and third, to generate critical discussion through an examination of the authors’ experiences leading and working within joint teams of ethnographers and data scientists.

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