Algorithmic Equity is a project produced and designed by Josie Williams in order to establish a communal knowledge base of NYPD police officer behavior to cultivate and support community-led accountability and autonomy.  Currently, when using Algorithmic Equity, the public is able to see the amount of times a precinct used force (ie. firearm, physical force, oc spray), a count of subject injuries, and logistical information about the precinct (ie. address, commanding officer). In it’s final form, Algorithmic Equity will include a precinct’s arrest history, a section for community comments and feedback, and precinct demographic breakdowns like age, sex, race, household type, educational attainment,  citizenship status, healthcare coverage, and more.

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As machine learning (ML) models, trained on real-world datasets, become common practice, it is critical to measure and quantify their potential biases. In this paper, we focus on renal failure and compare a commonly used traditional risk score, Tangri, with a more powerful machine learning model, which has access to a larger variable set and trained on 1.6 million patients' EHR data. We will compare and discuss the generalization and applicability of these two models, in an attempt to quantify biases of status quo clinical practice, compared to ML-driven models.

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Read the paper here.