The Need for Transparency in the Age of Predictive Sentencing Algorithms

• Author: Alyssa M. Carlson
• Position: J.D. Candidate, The University of Iowa College of Law, 2018; B.A., The University of Iowa, 2013
• Pages: 304-329

The Need for Transparency in the Age of Predictive Sentencing Algorithms Alyssa M. Carlson  ABSTRACT: Criminal law scholars devote substantial research to sociological and behavioral studies to determine characteristics common among reoffenders. This research aligns with a massive effort to reform the criminal justice system by reducing recidivism as a means to cure high crime rates and overcrowded prisons. Many scholars believe that by focusing resources on the criminal population that will likely commit future crimes, overall crime rates will decrease. The effort to reduce recidivism has led to the creation of objective risk assessment tools. These are essentially algorithms that purport to predict the likelihood that an individual will commit crime in the future. While these predictive algorithms were first implemented to determine parole conditions, they have become increasingly popular among courts and are now routinely used in all phases of a criminal proceeding. As the demand for predictive risk assessment formulas increases, many state governments now look to private companies to develop these methods. However, the move towards privatization raises issues of transparency, as companies are able to maintain the secrecy of their algorithms by claiming trade secret protection. As a result, defendants are unable to ensure the accuracy of the risk score results. This Note argues that private companies who benefit by providing a public service should be held to the same transparency requirements as public agencies, and freedom of information disclosure requirements should be extended to include proprietary predictive algorithms to achieve this result. I. INTRODUCTION ............................................................................. 304 II. BACKGROUND ............................................................................... 307 A. R ISK A SSESSMENT AS A R ESPONSE TO AN O VERBURDENED J USTICE S YSTEM ...................................................................... 307  J.D. Candidate, The University of Iowa College of Law, 2018; B.A., The University of Iowa, 2013. I would like to thank Professor Sarah Seo for encouraging me to write on this topic. Thank you also to the members of the Iowa Law Review for their hard work during the editing process, especially Courtney Brokloff, Nicholas Huffmon, and Lindsay Moulton. Finally, a special thanks to Rich and Mary Jo Parrino, who have been a tremendous source of advice and support throughout law school. 303 304 IOWA LAW REVIEW [Vol. 103:303 B. T HE B ASICS OF P REDICTIVE R ISK A SSESSMENT T OOLS ............... 309 C. E XPANSION OF A CTUARIAL A SSESSMENTS IN C RIMINAL S ENTENCING ........................................................................... 313 D. G OVERNMENT T RANSPARENCY AND F REEDOM OF I NFORMATION ......................................................................... 316 III. HOW THE PRIVATE SECTOR IS CAPITALIZING ON TRADE SECRET PROTECTION TO EVADE PUBLIC DISCLOSURE OF ITS SENTENCING ALGORITHMS ...................................................... 318 A. A L OOK AT P REDICTIVE A LGORITHMS IN THE C OURTS .............. 319 B. I NVOKING T RADE S ECRET P ROTECTION TO A VOID D ISCLOSURE ............................................................................ 321 C. T HE V ALIDATION P ROBLEM ..................................................... 322 IV. REQUIRING TRANSPARENCY FOR RISK ASSESSMENT FORMULAS DEVELOPED BY PRIVATE, FOR-PROFIT COMPANIES ........................ 324 A. A CCESS TO I NFORMATION ........................................................ 324 B. T HE B ENEFITS OF P UBLIC A CCESS TO R ISK A SSESSMENT ............ 326 C. E XTENDING D ISCLOSURE R EQUIREMENTS TO P ROPRIETARY R ISK A SSESSMENT I NSTRUMENTS .............................................. 328 V. CONCLUSION ................................................................................ 329 I. INTRODUCTION As the United States faces unprecedented rates of incarceration, 1 criminal law experts seek to decrease recidivism, believing that a small percentage of the population is responsible for a majority of crime. 2 This theory, known as “selective incapacitation,” is based on the idea “that a small subset of repeat offenders is responsible for the majority of crime and that incapacitating that small group would have exponential benefits for the overall crime rate.” 3 Researchers have started developing strategies that use objective evidence to identify criminals representing the most serious risk to 1 . See Claire Botnick, Note, Evidence-Based Practice and Sentencing in State Courts: A Critique of the Missouri System , 49 WASH. U. J.L. & POL’Y 159, 159 (2015) (“The number of adults under some form of correctional supervision in the United States has increased by 270 percent since 1980.”). 2. BERNARD E. HARCOURT, AGAINST PREDICTION: PROFILING, POLICING, AND PUNISHING IN AN ACTUARIAL AGE 88–89 (2007). 3 . Id. at 88; see also PAMELA M. CASEY ET AL., NAT’L CTR. FOR STATE COURTS, USING OFFENDER RISK AND NEEDS ASSESSMENT INFORMATION AT SENTENCING: GUIDANCE FOR COURTS FROM A NATIONAL WORKING GROUP 2 (2011) (“A sample of felony defendants from the nation’s 75 most populous counties during [2004] revealed that more than 75 percent had a prior arrest history, and 53 percent had at least five prior arrest charges. Another study of nearly 275,000 prisoners released in 1994 found that two-thirds were rearrested for a new offense within three years.” (citation omitted)). 2017] THE NEED FOR TRANSPARENCY 305 the community based on their likelihood to reoffend. 4 The process, known as “risk assessment,” has led to the creation of actuarial instruments, or statistical models that predict risk of recidivism by studying the common traits of paroled inmates responsible for committing multiple crimes. 5 Prior to the use of actuarial instruments, predicting an offender’s risk of recidivism had been done by clinical assessment—“‘an informal, “in the head,” [and] impressionistic, subjective conclusion’ about the offender’s future dangerousness.” 6 In the clinical model, assessments to evaluate a defendant are either made by mental health experts or other actors in the criminal justice system, such as judges or parole boards. 7 The movement in criminal law has been away from these clinical methods based on the decision-makers’ subjective judgments, observations, and experiences, and toward the use of objective application of statistical models derived from large datasets of criminal offenders. 8 This trend of objective risk assessment has led to the development of actuarial studies that attempt to isolate specific factors in estimating risk. 9 Sometimes called “mechanical prediction,” actuarial methods consist of “the mechanical combining of information for classification purposes, and the resultant probability figure which is an empirically determined relative frequency.” 10 The scores generated by actuarial risk assessment are now routinely used in all stages of the criminal 4. HARCOURT, supra note 2, at 88. 5. Bernard E. Harcourt, Risk as a Proxy for Race 2 (John M. Olin Law & Econ. Working Paper No. 535 (2d Series) & Pub. Law & Legal Theory Working Paper No. 323, 2010), http:// chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1265&context=public_law_and_legal_ theory. 6. Dawinder S. Sidhu, Moneyball Sentencing , 56 B.C. L. REV. 671, 687 (2015) (alteration in original) (quoting William M. Grove & Paul E. Meehl, Comparative Efficiency of Informal (Subjective, Impressionistic) and Formal (Mechanical, Algorithmic) Prediction Procedures: The Clinical–Statistical Controversy , 2 PSYCHOL. PUB. POL’Y, & L. 293, 294 (1996)). 7. HARCOURT, supra note 2, at 16–17. 8 . Id. at 2, 17–18; see also Scott VanBenschoten, Risk/Needs Assessment: Is This the Best We Can Do? , 72 FED. PROB. 38, 38–39 (2008) (stating that the increased use of actuarial risk assessment instruments demonstrates a trend over the last 30 years away from the clinical judgment of judicial officers); Joe Palazzolo, Wisconsin Supreme Court to Rule on Predictive Algorithms Used in Sentencing , WALL ST. J. (June 5, 2016, 5:30 AM), http://www.wsj.com/articles/wisconsin-supreme-court-to-rule-on-predictive-algorithms-used-in-sentencing-1465119008 (“‘Evidence has a better track record for assessing risks and needs than intuition alone,’ wrote Christine Remington, an assistant attorney general in Wisconsin, . . . defending the state’s use of the evaluations.”); Harcourt, supra note 5, at 2 (“There are, to be sure, political advantages to using technical instruments such as actuarial tools to justify prison releases. Risk-assessment tools protect political actors and serve to de-responsibilize decision-makers.”). 9. HARCOURT, supra note 2, at 2 (“Today, the actuarial permeates the field of criminal law and its enforcement.”). 10 . Id. at 16–17 (quoting PAUL E. MEEHL, CLINICAL VERSUS STATISTICAL PREDICTION: A THEORETICAL ANALYSIS AND A REVIEW OF THE EVIDENCE 3 (1954)). 306 IOWA LAW REVIEW [Vol. 103:303 proceeding, including parole determinations, prison classification, and sentencing. 11 As the use of predictive risk assessment has increased, several states have turned to private companies to supply the algorithms needed to generate a defendant’s risk score. 12 Although proponents of such methods claim the practice is efficient and effective, 13 these predictive algorithms can be problematic in practice. 14 Defendants have attempted to challenge the use of such algorithmic risk scores cited by judges in sentencing decisions since the defendants have no way of validating the accuracy of the formulas. 15 So far, such attempts have been unsuccessful because the companies behind the formulas assert trade secret protection, ensuring that the formulas used to calculate risk score remain unknown. 16 The problem is further complicated by the fact that most states themselves have taken no steps to ensure the accuracy of these...