Rediet Abebe: towards an algorithmic justice

The opposite of poverty is justice

Bryan Stevenson, Equal Justice Initiative founder

On February 27th, Dr. Rediet Abebe gave a prescient talk at Georgia Tech. During her talk, entitled “Designing Algorithms for Social Good”, she gave highlights from a research career that has already produced remarkable results at the intersection of algorithms, optimization, and social computing.

The surreal portion of the talk came at the beginning, where she detailed algorithms for the distribution of social benefits that could be optimized to improve the chances that people on the margins survive and thrive in the face of shocks. Shocks like wars, recessions, or viral pandemics.

How do we design policies of support when we know the crises will come? Dr Abebe has an algorithm for that.

The slide above — “A Model of Welfare” — is taken from her paper Subsidy Allocations in the Presence of Income Shocks and speaks so directly to the plight of the millions of gig economy workers, restaurant staff, undocumented, underemployed and others whose very ability to find adequate food and shelter are endangered as the impact of the COVID-19 pandemic compounds. In short, a humane and just welfare system has to account for the events that are likely to drive people on the margins — and millions who may consider themselves to be far from the margins — into poverty. With the increase income inequality and the looming impact of climate change and other threats, the reactive and piecemeal approaches to social welfare are clearly not up to the challenge.

The words of Bryan Stevenson, founder of the Equal Justice Initiative resonate: “The opposite of poverty is justice”. It would be an amazing world where the tools of computing were bent toward justice and equity. I hope that you’ll read Dr Abebe’s work, and ask your elected officials for justice. I hope that you contribute whatever you can to ease the burden of those around you.

Here’s a recent talk of Dr Abebe’s for inspiration

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