Beyond the Rhetoric of Algorithmic Solutionism

["Dave Farber" <farber () gmail com>]

> Begin forwarded message:
>
> From: Dewayne Hendricks <dewayne () warpspeed com>
> Subject: [Dewayne-Net] Beyond the Rhetoric of Algorithmic Solutionism
> Date: January 11, 2018 at 4:53:51 PM EST
> To: Multiple recipients of Dewayne-Net <dewayne-net () warpspeed com>
> Reply-To: dewayne-net () warpspeed com
>
> Beyond the Rhetoric of Algorithmic Solutionism
> By danah boyd
> Jan 11 2018
> <https://points.datasociety.net/beyond-the-rhetoric-of-algorithmic-solutionism-8e0f9cdada53>
>
> If you ever hear that implementing algorithmic decision-making tools to enable social services or other high stakes 
> government decision-making will increase efficiency or reduce the cost to taxpayers, know that you’re being lied to. 
> When implemented ethically, these systems cost more. And they should.
>
> Whether we’re talking about judicial decision making (e.g., “risk assessment scoring”) or modeling who is at risk for 
> homelessness, algorithmic systems don’t simply cost money to implement. They cost money to maintain. They cost money 
> to audit. They cost money to evolve with the domain that they’re designed to serve. They cost money to train their 
> users to use the data responsibly. Above all, they make visible the brutal pain points and root causes in existing 
> systems that require an increase of services.
>
> Otherwise, all that these systems are doing is helping divert taxpayer money from direct services, to lining the 
> pockets of for-profit entities under the illusion of helping people. Worse, they’re helping usher in a diversion of 
> liability because time and time again, those in powerful positions blame the algorithms.
>
> This doesn’t mean that these tools can’t be used responsibly. They can. And they should. The insights that 
> large-scale data analysis can offer is inspiring. The opportunity to help people by understanding the complex 
> interplay of contextual information is invigorating. Any social scientist with a heart desperately wants to 
> understand how to relieve inequality and create a more fair and equitable system. So of course there’s a desire to 
> jump in and try to make sense of the data out there to make a difference in people’s lives. But to treat data 
> analysis as a savior to a broken system is woefully naive.
>
> Doing so obfuscates the financial incentives of those who are building these services, the deterministic rhetoric 
> that they use to justify their implementation, the opacity that results from having non-technical actors try to 
> understand technical jiu-jitsu, and the stark reality of how technology is used as a political bludgeoning tool. Even 
> more frustratingly, what data analysis does well is open up opportunities for experimentation and deeper exploration. 
> But in a zero-sum context, that means that the resources to do something about the information that is learned is 
> siphoned off to the technology. And, worse, because the technology is supposed to save money, there is no budget for 
> using that data to actually help people. Instead, technology becomes a mirage. Not because the technology is 
> inherently bad, but because of how it is deployed and used.
>
> Next week, a new book that shows the true cost of these systems is being published. Virginia Eubanks’ book 
> “Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor” is a deeply researched accounting 
> of how algorithmic tools are integrated into services for welfare, homelessness, and child protection. Eubanks goes 
> deep with the people and families who are targets of these systems, telling their stories and experiences in rich 
> detail. Further, drawing on interviews with social services clients and service providers alongside the information 
> provided by technology vendors and government officials, Eubanks offers a clear portrait of just how algorithmic 
> systems actually play out on the ground, despite all of the hope that goes into their implementation.
>
> Eubanks eschews the term “ethnography” because she argues that this book is immersive journalism, not ethnography. 
> Yet, from my perspective as a scholar and a reader, this is the best ethnography I’ve read in years. “Automating 
> Inequality” does exactly what a good ethnography should do — it offers a compelling account of the cultural logics 
> surrounding a particular dynamic, and invites the reader to truly grok what’s at stake through the eyes of a diverse 
> array of relevant people. Eubanks brings you into the world of technologically mediated social services and helps you 
> see what this really looks like on the ground. She showcases the frustration and anxiety that these implementations 
> produce; the ways in which both social services recipients and taxpayers are screwed by the false promises of these 
> technologies. She makes visible the politics and the stakes, the costs and the hope. Above all, she brings the reader 
> into the stark and troubling reality of what it really means to be poor in America today.
>
> [snip]
>
> Dewayne-Net RSS Feed: http://dewaynenet.wordpress.com/feed/
> Twitter: https://twitter.com/wa8dzp




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