The Kaleidoscope Project investigates positionality-aware machine learning. Positionality is the social and political context that influences, and potentially biases, a person’s unique but partial understanding and outlook on the world. The scale of machine learning (ML) and artificial intelligence (AI) systems makes it easier than ever to embed positionality in society. If the embedded positionality is not aligned with the current context, it could impact different sectors of the society through the scale of AI applications.
Positionality can impact AI systems through classification systems; data collection, annotation, and sampling; model design; usage analysis; and more.
Without understanding positionality, we risk giving too much power to a future AI that is impossible in principle.
Outputs:
Presentation by Elizabeth Dubois at the MIT/Harvard Assembly 2019 Showcase
, summarizing the Kaleidoscope team's work. June 13, 2019, Academy of Arts and Sciences, Cambridge, MA .
(watch the presentation on YouTube)
Project leads:
Yewande Alade, Christine Kaeser-Chen, Elizabeth Dubois, Chintan Parmar, Friederike Schüür
Kaleidoscope is a Berkman Klein Assembly 2019 project.
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