Authors: Amari Bertagnolli and Ki Radcliffe, University of Wyoming debaters The March 2020 LD topic is: Resolved: Predictive policing is unjust. Predictive policing is “the application of analytical techniques—particularly quantitative techniques—to identify likely targets for police intervention and prevent crime or solve past crimes by making statistical predictions” (RAND, 2013). It’s less Minority Report (stopping crime before it starts) and more patrolling places where crime has been documented in the past. Right off the bat we want you to know you should spend more time prepping for being neg. Our big takeaway isn’t that you’re doomed if you’re neg, but it’s that you need to put more time into prepping to be neg because there are some structural weaknesses you’re going to have to organize your arguments around answering. Keep reading for our thoughts on the predictive policing topic and how to make sure you have a winnable argument when you’re neg. We’ve got 3 thoughts about being aff:
1. Racial bias Your aff should absolutely include an argument about racial bias. This is the heart of affirmative literature on this topic. When thinking about your value, it would be wise to dig into researching John Rawls. There are a lot of racial biases already in our criminal justice system and so developing a program to predict who will become a criminal will involve drawing on these biases and stereotypes against minorities and further entrenching them. Data is not neutral, but can be easily skewed by structural racism. There are great studies that police disproportionately stop and search people of color for drugs, despite use rates being equal or higher among the white population. The data produced from that doesn’t reflect the criminality of people of color, but the unconscious racial assumptions of those making the stops. Using historical data trends might then cause the police to concentrate in predominately non-white neighborhoods. That leads to increased enforcement which in turns skews crime statistics and as more crimes are reported in that area it creates a vicious cycle reinforcing racial biases and straining relationships between law enforcement and minority communities. 2. Privacy Can you prove that you wouldn’t commit a crime that you haven’t committed? There are repercussions to being labeled as a risk, such as being monitored. No one really wants to be stalked by the police or have it on their record that they are an at-risk individual, but it’s also nearly impossible to appeal since you haven’t done anything wrong yet. A computer program just thinks that maybe someday you might do something wrong, a computer programmed by fallible humans. This violates the basic principle of innocent until proven guilty. It also puts citizens’ privacy at risk. 3. Misunderstanding the root causes The human element is critical to the programing of the software for predictive policing. Too often people will jump to simple cause and effect conclusions without determining if the cause is actually the root cause of a crime. Take for example, many crimes are reported in the morning around 7 a.m. to 8 a.m. Does this mean that most crimes are being committed in the morning or are people just waking up around that time and realizing that they have been affected? The relationship here could be easily misinterpreted which would leave us with a flawed system. It is not unreasonable to infer that a few minor details being off would compound in a system like this and lead to bad identification. The neg side of this resolution is challenging at first glance. We’ve got 5 suggestions to help you out when you’re neg: 1. Don’t split your pre-tournament prep time equally Devote more of your time and energy to prepping your neg case because creative analysis and framing arguments will be necessary to overcome structural challenges. 2. Focus on particular scenarios It may be helpful to introduce an understanding of the potential of predictive policing as a way of centering potential victims and directing resources to those in need. Those viewed as at “high-risk” for being involved in violence based on data patterns could be presented with assistance and outreach efforts rather than being subjected to punishment. Many police forces have been testing this approach and have seen success in reducing rates of gun violence. 3. Set up a model for what predictive policing should look like Transparency and community input should be taken into account. Even if this doesn’t happen in every case of predictive policing now, you can argue that the aff’s burden of proof should be that predictive policing is inherently unjust in its own right. Some solid examples of predictive policing being used as a transparent tool for community-supported crime reduction can go a long way in countering the idea of predictive policing as always unjust. You shouldn’t be limited to defending the worst instances of predictive policing. That would be an atrocious model of debate. 4. Consider critiquing other aspects of policing that lead to injustice now. If profits and punitive models of policing dominate, perhaps they are part of what makes predictive policing (and policing in general) unjust in some instances. That could mean that predictive policing is not unjust in and of itself, but rather, that other issues are a root cause and shifting to predictive policing can help to hedge back against other problematic aspects of the criminal justice system. 5. Impact calculus Perhaps it is unjust to have the technological resources to prevent mass violence and refuse to use them based on more ethereal concerns and values. If lives can be saved by predictive policing tactics that alone warrants consideration. Examples that would be useful here are instances in which predictive policing (often based in social media analytics) allowed law enforcement to know ahead of time that someone was planning a violent attack and to stop it. Predictive policing can draw on the use of data in other fields, like medicine or politics. There is an ongoing and widespread turn to the use of data in decision-making for all sorts of sectors. It could be worthwhile to look at how those uses of data are making positive change and dealing with correction of biases in algorithms. We want to hear from you! Disagree with something we said? Have a question? Feel free to jump in in the comments, we'll be sure to respond! Do you have a topic you’d like us to address in a future post? Email us at [email protected] Go Pokes!
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