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How an algorithm kicks small businesses out of the food stamps program on dubious fraud charges
> Last year, the USDA disqualified more than 1,600 retailers across the country from receiving SNAP payments. Over 90 percent of those businesses are convenience stores or small groceries.

> It’s impossible to pin down exactly how many retailers were banned from accepting SNAP dollars due to fraud charges that the government can’t actually prove. But court testimony by a USDA official indicates that, just last year, hundreds of retailers were permanently disqualified from the program based primarily on an algorithmic assessment of their transaction patterns

> The USDA does not bother to justify or even explain the precise sales figures or thresholds that cause retailers to be flagged for investigation. In fact, officials appear not to know how they were developed in the first place.

new system was connected to roll out of EBT cards -- easier to electronically monitor every transaction. little understanding from those relying on the algorithmic assessments of how they work. supporters of the tool say it's one data point to help identify fraud, but in practice people are relying on it without skepticism. minimal opportunities for those affected to challenge the verdicts, no opportunities to view evidence against them, they are required to "prove" their innocence. those using the tool are unaware of how it works or what flags it uses to identify fraud.
algorithmic-bias  bias  snap  food-stamps  usda 
yesterday by tarakc02
What is the Streisand effect? - The Economist explains
When the Wikipedians refused, the DRCI is alleged to have hauled a French Wikipedia editor into its offices and forced him to delete the entire article, on pain of immediate arrest. Instead of hiding the information, this made the story spread around the world—a textbook example of what internet aficionados call the Streisand Effect.
bias  psychology  http 
2 days ago by janpeuker
New Dark Age by James Bridle review – technology and the end of the future | Books | The Guardian
At the core of our thinking about new technology there lies, Bridle suggests, a dangerous fallacy: we both model our own minds on our understanding of computers, and believe they can solve all our problems – if, that is, we supply them with enough data, and make them fast enough to deliver real-time analyses. To the Panglossian prospect of Moore’s Law, which forecasts that computers’ processing power will double every two years, Bridle offers up the counterexmple of Gates’s Law, which suggests these gains are negated by the accumulation in software of redundant coding. But our miscalculations concerning the value of big data are only part of the computational fallacy; Bridle also believes it’s implicated in our simple-minded acceptance of technology as a value-neutral tool, one to be freely employed for our own betterment. He argues that in failing to adequately understand these emergent technologies, we are in fact opening ourselves up to a new dark age. He takes this resonant phrase from HP Lovecraft’s minatory short story, “The Call of Cthulhu”, rather than the dark ages of historical record, although the latter may turn out to be a better point of reference for our current era. Lovecraft’s new dark age is, paradoxically, a function of enlightenment – it’s the searchlight science shines into the heart of human darkness that brings on a crazed barbarism. Bridle’s solution is to propose “real systemic literacy”, alongside a willingness to be imprecise – cloudy, even – when it comes to our thinking about the cloud
book  innovation  society  bias 
2 days ago by janpeuker
Amazon built AI to hire people, but it discriminated against women - Business Insider
Amazon worked on building an artificial-intelligence tool to help with hiring, but the plans backfired when the company discovered the system discriminated against women, Reuters reports. via Pocket
ai  algorithms  taking  over  bias  newsletter  trustabletech 
3 days ago by thewavingcat
“Plotto”: Generating Truly Offensive Stories Since 1928.
Lynn cherny - generative or procedural story telling based on old book of plot bits, python, turned out offensive stuff, she not releasing. Long and kinda obvious but at least she thought at end.
writing  machine_learning  ethics  data  bias 
3 days ago by berran
Abilene paradox - Wikipedia
In the Abilene paradox, a group of people collectively decide on a course of action that is counter to the preferences of many or all of the individuals in the group. via Pocket
IFTTT  Pocket  bias  communications  group 
4 days ago by technogoggles
How to overcome common decision-making obstacles

Obstacle 1: Putting too much importance on your “anchor”
Obstacle 2: Overconfidence
Obstacle 3: Confirmation Bias
Obstacle 4: Group think
Obstacle 5: Present Bias
Obstacle 6: Personal patterns
decision.making  bias  lists  analysis  trobuleshooting  obstacles 
5 days ago by po
Using a reference class to gain much-needed perspective | McKinsey
ake the “outside view,” which means building a statistical view of your project based on a reference class of similar projects.
McKinsey  bias 
5 days ago by tom.reeder

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