We finally have the first legal ruling on algorithmic decision making. This case comes from Wisconsin, where Eric Loomis challenged the use of COMPAS for sentencing him.
While the Supreme Court denied the appeal, it made a number of interesting observations and recommendations:
- “risk scores may not be considered as the determinative factor in deciding whether the offender can be supervised safely and effectively in the community.”
- “the following warning must be given to sentencing judges: “(1) the proprietary nature of COMPAS has been invoked to prevent disclosure of information relating to how factors are weighed or how risk scores are to be determined; (2) risk assessment compares defendants to a national sample, but no cross- validation study for a Wisconsin population has yet been completed; (3) some studies of COMPAS risk assessment scores have raised questions about whether they disproportionately classify minority offenders as having a higher risk of recidivism; and (4) risk assessment tools must be constantly monitored and re-normed for accuracy due to changing populations and subpopulations.”
Like Danielle Citron (the author of the Forbes article) I’m a little skeptical that this will be enough. Warning labels on cigarette boxes didn’t really stop people smoking. But I think as part of a larger effort to increase awareness of the risks, and to make people even stop and think a little before blindly forging ahead with algorithms, this is a decent first step.
At the AINow Symposium in New York (that I’ll say more about later), one proposed extreme along the policy spectrum regarding algorithic decision-making was to place a moratorium on the use of algorithms entirely. I don’t know if that makes complete sense. But a heavy heavy dose of caution is definitely warranted, and rulings like this might lead to a patchwork of caveats and speedbumps that help us flesh out exactly where algorithmic decision making makes more or less sense.