{"id":30822,"date":"2018-11-13T00:02:00","date_gmt":"2018-11-13T05:02:00","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/lady-justice-and-the-machines-an-algorithmic-approach-to-criminal-justice-reform\/"},"modified":"2018-11-13T00:02:00","modified_gmt":"2018-11-13T05:02:00","slug":"lady-justice-and-the-machines-an-algorithmic-approach-to-criminal-justice-reform","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/lady-justice-and-the-machines-an-algorithmic-approach-to-criminal-justice-reform\/","title":{"rendered":"Lady Justice and the Machines: An Algorithmic Approach to Criminal Justice Reform"},"content":{"rendered":"

California Senate Bill 10 (SB 10), which Governor Jerry Brown signed into law in August (2018), fundamentally transforms California\u2019s approach to pretrial detention.[1]<\/a> As in most states, California previously operated a \u2018cash bail\u2019 system, which allowed individuals suspected of having committed a crime to pay a certain sum of money (bail) and await trial outside the confines of court custody.[2]<\/a> Critics of cash bail, however, point to fairness issues underlying a system that \u201cmakes justice an uneven playing field, incarcerating the poor while allowing those with money or assets to avoid jail time.\u201d[3]<\/a> This position is evinced with clarity by a co-author of SB 10, who asserted that \u201c[f]reedom and liberty should never be pay to play,\u201d and that \u201c[the existing] system has allowed the wealthy to purchase their freedom regardless of their risk, while the poor who pose no danger languish in jail.\u201d[4]<\/a><\/p>\n

In response, California state legislators developed an alternative to cash bail that draws heavily upon the first megatrend identified in the TOM Challenge: machine learning. Beginning in October 2019, criminal suspects will undergo algorithmic risk assessments whose outputs will help determine pretrial detention outcomes \u2013 these assessments will focus on the likelihood that a detained individual would, if released, commit another offense or flee before trial. Individuals will be identified as either low-, medium-, or high-risk, and pretrial detention decisions will follow from these algorithmic outputs.[5]<\/a> SB 10 therefore shifts the pretrial detention calculus on two dimensions: ability to pay is supplanted by an assessment of risk profile, and machine learning sidelines some (but not all) aspects of human discretion (the bill does allow for judges and prosecutors, at times, to diverge from the algorithm\u2019s outcome).<\/p>\n

While advocates of the change applaud improved outcomes for economic justice, others are concerned that the introduction of machine learning will create new challenges. Three specific vulnerabilities are salient given our in-class debates about machine learning. First, because these risk assessment algorithms are likely to rely on supervised learning,[6]<\/a> \u201ctraining data\u201d may contain \u201csystemic biases.\u201d[7]<\/a> In this way, example inputs and desired outputs may themselves reflect racial biases and other (implicit or explicit) discriminatory effects.[8]<\/a> Second, because California is allowing localities to choose whether to engage a third-party contractor or build their own algorithms, the fact that third-party contractors may limit transparent access to their risk-assessment-algorithm formulas (under cover of intellectual property law) could make it harder to discern whether (and how) such algorithms are biased.[9]<\/a> Finally, beyond the potential systemic biases of these algorithms, there may also be community-specific biases if the training data used, for example, is \u201cnot representative of the [specific] community that will eventually use the risk assessment.\u201d[10]<\/a><\/p>\n

Legislators have vowed to take steps, in the short- and medium-term, to mitigate at least some of these issues. A comprehensive review (to identify bias) of the system has already been planned for 2023, and one state senator has said he will draft legislation pushing for transparency in risk assessment algorithms.[11]<\/a> Yet, critics suggest that vulnerabilities remain. The Electronic Frontier Foundation has been especially vocal, arguing that public servants and individual citizens should demand greater visibility into the source code and more input as to what criteria factor into these risk assessment tools, instead of relying on third-party contractors.[12]<\/a> \u00a0Similarly, the introduction of \u201cregular independent audits\u201d could allay concerns about waiting until the planned review in 2023.[13]<\/a> Such audits, coupled with transparency, might facilitate more iterative development of the machine learning algorithms at the heart of SB 10. Specifically, they would allow for targeted improvement of the algorithms at a more rapid pace. While streamlined and transparent iteration might begin to address potential operational issues in the near term, however, it remains to be seen whether these interventions could account for more structural biases that may exist in underlying datasets. Needless to say, the stakes of this machine learning experiment are high. Individuals who are deemed risky are detained in jail \u2013 ensuring that machine learning directs appropriately proportionate pretrial outcomes is critical.<\/p>\n

As applied to pretrial detention and criminal justice reform more broadly, therefore, machine learning may address certain problems while exacerbating others. Are the latter more significant than the former? Are there other considerations, unacknowledged here, that accompany the introduction of machine learning into criminal justice reform and decision-making?<\/p>\n

(711 words)<\/p>\n

<\/a>[1]<\/a> Bryan Anderson and Alexei Koseff, \u201cVacant governor\u2019s mansion + Bail measure has the votes + California Priorities summit today,\u201d November 9, 2018, https:\/\/www.sacbee.com\/news\/politics-government\/capitol-alert\/article221389490.html<\/a>.<\/p>\n

<\/a>[2]<\/a> American Bar Association, \u201cSteps in a Trial,\u201d December 2, 2013, https:\/\/www.americanbar.org\/groups\/public_education\/resources\/law_related_education_network\/how_courts_work\/bail\/<\/a>.<\/p>\n

<\/a>[3]<\/a> Dave Gershgorn, \u201cCalifornia just replaced cash bail with algorithms,\u201d Quartz<\/em>, September 4, 2018, https:\/\/qz.com\/1375820\/california-just-replaced-cash-bail-with-algorithms\/<\/a>.<\/p>\n

<\/a>[4]<\/a> \u201cGov. Brown signs bill eliminating money bail in California,\u201d The Mercury News<\/em>, August 28, 2018, https:\/\/www.mercurynews.com\/2018\/08\/28\/gov-brown-signs-bill-eliminating-money-bail-in-california\/<\/a>.<\/p>\n

<\/a>[5]<\/a> Alexei Koseff, \u201cJerry Brown signs bill eliminating money bail in California,\u201d Sacramento Bee<\/em>, August 28, 2018, https:\/\/www.sacbee.com\/news\/politics-government\/capitol-alert\/article217461380.html<\/a>.<\/p>\n

<\/a>[6]<\/a> Building Watson: Not So Elementary, My Dear! (Abridged), \u201cAppendix.\u201d<\/p>\n

<\/a>[7]<\/a> Hayley Tsukayama and Jamie Williams, \u201cIf a Pre-trial Risk Assessment Tool Does Not Satisfy These Criteria, It Needs to Stay Out of the Courtroom,\u201d Electronic Frontier Foundation<\/em>, November 6, 2018, https:\/\/www.eff.org\/deeplinks\/2018\/11\/if-pre-trial-risk-assessment-tool-does-not-satisfy-these-criteria-it-needs-stay<\/a>.<\/p>\n

<\/a>[8]<\/a> Ibid.<\/p>\n

<\/a>[9]<\/a> Dave Gershgorn, \u201cCalifornia just replaced cash bail with algorithms,\u201d Quartz<\/em>, September 4, 2018, https:\/\/qz.com\/1375820\/california-just-replaced-cash-bail-with-algorithms\/<\/a>.<\/p>\n

<\/a>[10]<\/a> Hayley Tsukayama and Jamie Williams, \u201cIf a Pre-trial Risk Assessment Tool Does Not Satisfy These Criteria, It Needs to Stay Out of the Courtroom,\u201d Electronic Frontier Foundation<\/em>, November 6, 2018, https:\/\/www.eff.org\/deeplinks\/2018\/11\/if-pre-trial-risk-assessment-tool-does-not-satisfy-these-criteria-it-needs-stay<\/a>.<\/p>\n

<\/a>[11]<\/a> Dave Gershgorn, \u201cCalifornia just replaced cash bail with algorithms,\u201d Quartz<\/em>, September 4, 2018, https:\/\/qz.com\/1375820\/california-just-replaced-cash-bail-with-algorithms\/<\/a>.<\/p>\n

<\/a>[12]<\/a> Hayley Tsukayama and Jamie Williams, \u201cIf a Pre-trial Risk Assessment Tool Does Not Satisfy These Criteria, It Needs to Stay Out of the Courtroom,\u201d Electronic Frontier Foundation<\/em>, November 6, 2018, https:\/\/www.eff.org\/deeplinks\/2018\/11\/if-pre-trial-risk-assessment-tool-does-not-satisfy-these-criteria-it-needs-stay<\/a>.<\/p>\n

<\/a>[13]<\/a> Ibid.<\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

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