Issue 719 - Page 29

By Giovanni Mastrobuoni
By Giovanni Mastrobuoni

Do algorithms help to reduce crime ?

Why predicting individual crimes by using criminals ’ habits against them appears to be a good investment


We all tend to follow habits at work and in other aspects of our lives . I generally work best in the morning and hate working after dinner . Our habits usually reflect preferences , learning , or a combination of the two – or , as Charles Duhigg notes in his book `The Power of Habit ’, the mere repetition of an act might generate a routine . My own recent research shows that criminals are not all that different from law-abiding citizens when it comes to sticking to a routine , possibly as a result of their experience , their tendency to specialise , and their belief that they have developed an ideal strategy . And police departments are catching on , with the assistance of an increasingly common tool : algorithms . Because algorithms use patterns in data to predict future behavior , they can forecast the movies someone might like on Netflix or the books she might buy on Amazon . But they can also help lawenforcement agencies fight crime . Some algorithms calculate prison inmates ’ future recidivism . Others underpin predictive-policing tools , which generate crime forecasts with the aim of optimising patrols . Investments in predictive analytics lead to a reallocation of resources ( including through different policing strategies and alternative sentencing ), and thus alter the probability of arrest or detention across individuals . For this reason , it is important to understand whether these algorithmic tools reduce crime , and whether they do so without generating biases against certain groups . The most elaborate and well-known predictive-policing software has essentially evolved from hotspot maps . These programs operate on the principle that areas recently subject to high crime are more likely to have high near-term crime rates . Thus , law-enforcement agencies should focus on these areas in order to deter the largest number of criminals . While researchers have shown that these statistical algorithms have greater predictive power than simple averages , proving that they actually reduce crime is considerably more difficult . Police

departments tend to embrace predictive policing when crime is high , and subsequent reductions might reflect a natural decline that has nothing to do with that decision . Area-focused policing may also simply shift crime elsewhere . A proper evaluation therefore requires a better counterfactual scenario : What would have happened to crime without the use of predictive policing ? As for bias , it is not inconceivable that predictive policing may distort lawenforcement outcomes . More deprived areas may have higher crime rates and will be patrolled more intensively once predictive policing is introduced . If police resources remain fixed , criminals in deprived areas will have a greater chance of encountering a police patrol than criminals in more affluent neighborhoods . But while this is a fair outcome for serial offenders who contributed to the spike in crime that led to the extra patrolling , today ’ s first-time offenders did not previously increase the crime numbers . Because most predictive-policing algorithms bundle together crime incidents without separating habitual criminals from first-time offenders , they may be biased against the latter in deprived areas . To help address the questions of effectiveness and bias , I evaluated predictive-policing software used in Milan , Italy . This allowed me to establish a proper counterfactual : For historical reasons , Milan has two police departments that share the same objectives , but only one of them uses predictive policing . KeyCrime , the predictive software developed by Mario Venturi that is used in Milan , differs from common predictive-policing tools because it focuses on arresting perpetrators rather than deterring them ( thus denying criminals the opportunity simply to go somewhere else ) and distinguishes firsttime offenders from recurrent criminals . The software uses information gathered from victim reports and CCTV cameras to link criminals to commercial robberies , and then predicts when and where a particular individual or group will strike next . KeyCrime generates individual predictions , which reduces the scope for bias . The results indicate that analysing the habits of recurrent criminals more than doubles the likelihood of arresting them . Thieves tend to act in a similar way over time , targeting a specific neighborhood and type of business , as well as sticking to a certain time of day . So , for example , someone who previously robbed a jewelry shop at nine o ’ clock in the morning is likely to reoffend in the same neighborhood , at around the same time , and against another jeweler . Since there are only so many matches that fit the predictions , the software highlights the potential future targets , and the police department organises patrols to catch the thief . Micro-predictions based on individual criminal groups ’ behavior have been proven to work in combating robberies and are now being extended to other types of serial offenders , like sex offenders and terrorists . Whether predictive policing will be equally successful in bringing these criminals to justice remains to be seen , as reduced interaction with victims and availability of CCTV footage might make it more difficult to link offenders across incidents . More widespread use of predictive policing may lead criminals to change their habits and become less predictable . But the development of more powerful algorithms and data-gathering processes provides some grounds for optimism among police departments . “ Crime is terribly revealing ,” Agatha Christie wrote . “ Try and vary your methods as you will , your tastes , your habits , your attitude of mind , and your soul is revealed by your actions .” For now , building capacity to predict individual crimes by using criminals ’ habits against them appears to be a good investment .
Giovanni Mastrobuoni , Carlo Alberto Chair at Collegio Carlo Alberto , is Professor of Economics at the University of Essex .
Copyright : Project Syndicate , 2022 .
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