MILLIE ARORA

Contributed Articles

Ethnography for the AI Age: How to Get Started

By MARIA CURY, MIKKEL KRENCHEL and MILLIE P. ARORA, ReD Associates To influence the development of artificial intelligence, ethnographers must build more partnerships and new kinds of outputs. Artificial intelligence (AI) has made huge strides recently in areas like natural language processing and computer-generated images – every other week seems to bring another breathtaking headline. Engineers, developers, and policymakers in the AI community are more seriously grappling with the fundamental risks that AI poses to society, like perpetuating unfair biases, putting privacy and security at risk, harming mental health, or automating tasks that provide livelihoods for people. As people flock to the fields of 'responsible AI,’ ‘AI ethics,’ and ‘AI governance’ that are all about shaping AI towards what is helpful for humanity, it is time we ask: where are the ethnographers and applied anthropologists? Many are doing ground-breaking work in AI, and reporting back to the EPIC community (see here, here, here, also here for just...

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

MILLIE P. ARORA MIKKEL KRENCHEL JACOB MCAULIFFE ReD Associates POORNIMA RAMASWAMY Cognizant 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...