Suspicion Machines

Lighthouse Reports: “Governments all over the world are experimenting with predictive algorithms in ways that are largely invisible to the public. What limited reporting there has been on this topic has largely focused on predictive policing and risk assessments in criminal justice systems. But there is an area where even more far-reaching experiments are underway on vulnerable populations with almost no scrutiny.

Fraud detection systems are widely deployed in welfare states ranging from complex machine learning models to crude spreadsheets. The scores they generate have potentially life-changing consequences for millions of people. Until now, public authorities have typically resisted calls for transparency, either by claiming that disclosure would increase the risk of fraud or to protect proprietary technology.

The sales pitch for these systems promises that they will recover millions of euros defrauded from the public purse. And the caricature of the benefit cheat is a modern take on the classic trope of the undeserving poor and much of the public debate in Europe — which has the most generous welfare states — is intensely politically charged.

The true extent of welfare fraud is routinely exaggerated by consulting firms, who are often the algorithm vendors, talking it up to near 5 percent of benefits spending while some national auditors’ offices estimate it at between 0.2 and 0.4 of spending. Distinguishing between honest mistakes and deliberate fraud in complex public systems is messy and hard.

When opaque technologies are deployed in search of political scapegoats the potential for harm among some of the poorest and most marginalised communities is significant.

Hundreds of thousands of people are being scored by these systems based on data mining operations where there has been scant public consultation. The consequences of being flagged by the “suspicion machine” can be drastic, with fraud controllers empowered to turn the lives of suspects inside out…(More)”.