Coding complicity in police violence

Occasionally people (usually my fellow white men) yell at me to “stick to science!” Well, sticking to science is a luxury that white women and scientists of color can’t afford, and pretending scientists aren’t complicit in violence toward underrepresented groups preserves inequality. At the same time, some within the broad tent of STEM (science, technology, engineering, and mathematics) actively perpetuate problems. My latest piece for SIAM News discusses one particular example — the ways in which computer scientists and other developers of code have helped increase racial profiling and police brutality — but many of the points apply more broadly to STEM. (Bonus: look for the Rage Against the Machine lyrics.)

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When Software Harms, What You Reap Is What You Sow

For SIAM News:

As of July 2020, the CalGang database contained the names and personal details of nearly 90,000 people in the state of California who are suspected of being in gangs or associating with gang members. Despite its stated purpose to provide law enforcement agencies with accurate intelligence information, audits and independent investigations revealed that the database was riddled with errors, falsified material, racial profiling, and other serious problems.

Databases and algorithms are ubiquitous parts of our interconnected world, but CalGang illustrates a major way in which they can fail people. If a streaming service suggests a movie that you do not like, no real harm is done; but if your name appears in CalGang, you may face consequences like increased police harassment or harsher sentences if charged with a crime.

“[Most of] the people creating these technologies are not affected in negative ways,” Seny Kamara, a computer scientist at Brown University, said. “But if you’re a young Black male growing up in Chicago or New York or California, you know that you may end up as a false positive in a gang database, and that affects your life in a completely different way.”

[Read the rest at SIAM News]

Ecological stability far from equilibrium

toxic algae on Lake Erie, as seen by the Landsat 8 satellite

The linked article is for SIAM News, the magazine for members of the Society for Industrial and Applied Mathematics (SIAM). The audience for this magazine, in other words, is professional mathematicians and related researchers working in a wide variety of fields. While this article contains equations, I wrote it to be understandable even if you gloss over the math.

[ This blog is dedicated to tracking my most recent publications. Subscribe to the feed to keep up with all the science stories I write! ]

Ecological Transients and the Ghost of Equilibrium Past

For SIAM News:

The sight and smell of eutrophication—in the form of a layer of stinking green algae on a lake or pond—is likely familiar to many readers. The result is detrimental, even toxic, to other species that rely on the water, ranging from tiny animals to birds and even humans. For example, eutrophication on Lake Erie affects millions (see Figure 1). But the real culprit is actually the substance that feeds the algae: excess phosphorous that is produced by human activities like fertilizer runoff and leaky septic systems.

To manage eutrophication, one must know whether the affected body of water resides in a eutrophic stable state, or if its state is a long transient. The second case mimics stability because it can last a long time but is sustained by another source of phosphorous in the lakebed sediments. According to Tessa Francis, an ecologist at the University of Washington Puget Sound Institute, the wrong management choice has major consequences in terms of costs and trade-offs.

“You’re investing all of this social, political, and economic capital into management, but you’re getting no results from it,” Francis said. “If you gave the system a bigger smack by adding an alternative management strategy to tackle the phosphorus pool at the bottom of the lake, that would be more likely to get your lake back to the state you want. This is just one consequence of long transients in terms of how they affect management decisions.”

[Read the rest at SIAM NEWS]