Speech Presented at the National Association of Criminal Defense Lawyers 57th Annual Meeting and 13th State Criminal Justice Network Conference, Philadelphia, PA

Holder, Eric

doi:10.1525/fsr.2015.27.4.252

Cited by 24 publications

(24 citation statements)

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“…They should not be based on unchangeable factors that a person cannot control, or on the possibility of a future crime that has not taken place (Holder 2014). …”

Section: Reinforcing or Undercutting Ethical Principlesmentioning

confidence: 99%

Ethical Implications and Accountability of Algorithms

2018

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Algorithms silently structure our lives. Algorithms can determine whether someone is hired, promoted, offered a loan, or provided housing as well as determine which political ads and news articles consumers see. Yet, the responsibility for algorithms in these important decisions is not clear. This article identifies whether developers have a responsibility for their algorithms later in use, what those firms are responsible for, and the normative grounding for that responsibility. I conceptualize algorithms as value-laden, rather than neutral, in that algorithms create moral consequences, reinforce or undercut ethical principles, and enable or diminish stakeholder rights and dignity. In addition, algorithms are an important actor in ethical decisions and influence the delegation of roles and responsibilities within these decisions. As such, firms should be responsible not only for the value-laden-ness of an algorithm but also for designing who-does-what within the algorithmic decision. As such, firms developing algorithms are accountable for designing how large a role individual will be permitted to take in the subsequent algorithmic decision. Counter to current arguments, I find that if an algorithm is designed to preclude individuals from taking responsibility within a decision, then the designer of the algorithm should be held accountable for the ethical implications of the algorithm in use. Keywords Algorithms · AI · Ethics · Fairness · Artificial intelligence · Big Data · Accountability · STS · TechnologyRodríguez was just sixteen at the time of his arrest, and was convicted of second-degree murder for his role in an armed robbery of a car dealership that left an employee dead. Now, twenty-six years later, he was a model of rehabilitation. He had requested a transfer to Eastern, a maximum-security prison, in order to take college classes. He had spent four and a half years training service dogs for wounded veterans and eleven volunteering for a youth program. A job and a place to stay were waiting for him outside. And he had not had a single disciplinary infraction for the past decade… Yet, last July, the parole board hit him with a denial. It might have turned out differently but, the board explained, a computer system called COM-PAS had ranked him "high risk." Neither he nor the board had any idea how this risk score was calculated; Northpointe, the for-profit company that sells COM-

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“…They should not be based on unchangeable factors that a person cannot control, or on the possibility of a future crime that has not taken place (Holder 2014). …”

Section: Reinforcing or Undercutting Ethical Principlesmentioning

confidence: 99%

Ethical Implications and Accountability of Algorithms

2018

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“…We sought to provide a comprehensive summary of the state of science and practice in the United States, acknowledging that, by design, our review focused on the context of the American penal system. Differences in the characteristics of offenders in the United States compared to those of offenders in other countries, combined with the remarkably high rate of incarceration in the United States, suggested the need for a review of the American empirical evidence and reflected recent calls for such data from clinicians tasked with conducting risk assessments in U.S. correctional settings and policymakers alike (Holder, 2014). Our specific aims were to: (1) identify and describe the characteristics and content of risk assessment instruments designed to predict general recidivism that have been validated in the United States; (2) summarize the characteristics of the studies that have been conducted in U.S. correctional settings; and (3) synthesize the findings regarding the inter-rater reliability and predictive validity of risk assessments completed using these instruments on adult offenders in the United States.…”

Section: The Current Reviewmentioning

confidence: 99%

Performance of recidivism risk assessment instruments in U.S. correctional settings.

Desmarais

Johnson

Singh³

2016

Psychological Services

103

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With the population of adults under correctional supervision in the United States at an all-time high, psychologists and other professionals working in U.S. correctional agencies face mounting pressures to identify offenders at greater risk of recidivism and to guide treatment and supervision recommendations. Risk assessment instruments are increasingly being used to assist with these tasks; however, relatively little is known regarding the performance of these tools in U.S. correctional settings. In this review, we synthesize the findings of studies examining the predictive validity of assessments completed using instruments designed to predict general recidivism risk, including committing a new crime and violating conditions of probation or parole, among adult offenders in the United States. We searched for studies conducted in the United States and published between January 1970 and December 2012 in peer-reviewed journals, government reports, master's theses, and doctoral dissertations using PsycINFO, the U.S. National Criminal Justice Reference Service Abstracts, and Google. We identified 53 studies (72 samples) conducted in U.S. correctional settings examining the predictive validity of 19 risk assessment instruments. The instruments varied widely in the number, type, and content of their items. For most instruments, predictive validity had been examined in 1 or 2 studies conducted in the United States that were published during the reference period. Only 2 studies reported on interrater reliability. No instrument emerged as producing the "most" reliable and valid risk assessments. Findings suggest the need for continued evaluation of the performance of instruments used to predict recidivism risk in U.S. correctional agencies. (PsycINFO Database Record

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“…Although race is not included in risk assessments, many factors included in risk assessments correlate heavily with race. In a 2014 speech to the National Association of Criminal Defense Lawyers, former Attorney General Eric Holder warned that sentencing decisions based on "immutable characteristics may exacerbate unwarranted and unjust disparities that are already far too common in our criminal justice system and in our society" (Holder, 2014). Recent court rulings have further highlighted the unfairness of the use of proprietary scoring algorithms that do not allow one to see how the score was calculated, and thus assess its accuracy or contest the score (State v. Loomis, 2016;Kehl et al, 2017).…”

Section: Risk Assessmentmentioning

confidence: 99%

Assessing Risk Among Correctional Community Probation Populations: Predicting Reoffense With Mobile Neurocognitive Assessment Software

Haarsma¹,

Davenport²,

White³

et al. 2020

Front. Psychol.

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We seek to address current limitations of forensic risk assessments by introducing the first mobile, self-scoring, risk assessment software that relies on neurocognitive testing to predict reoffense. This assessment, run entirely on a tablet, measures decision-making via a suite of neurocognitive tests in less than 30 minutes. The software measures several cognitive and decision-making traits of the user, including impulsivity, empathy, aggression, and several other traits linked to reoffending. Our analysis measured whether this assessment successfully predicted recidivism by testing probationers in a large urban city (Houston, TX, United States) from 2017 to 2019. To determine predictive validity, we used machine learning to yield cross-validated receiveroperator characteristics. Results gave a recidivism prediction value of 0.70, making it comparable to commonly used risk assessments. This novel approach diverges from traditional self-reporting, interview-based, and criminal-records-based approaches, and can also add a protective layer against bias, while strengthening model accuracy in predicting reoffense. In addition, subjectivity is eliminated and time-consuming administrative efforts are reduced. With continued data collection, this approach opens the possibility of identifying different levels of recidivism risk, by crime type, for any age, or gender, and seeks to steer individuals appropriately toward rehabilitative programs. Suggestions for future research directions are provided.

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Speech Presented at the National Association of Criminal Defense Lawyers 57th Annual Meeting and 13th State Criminal Justice Network Conference, Philadelphia, PA

Cited by 24 publications

References 0 publications

Ethical Implications and Accountability of Algorithms

Ethical Implications and Accountability of Algorithms

Performance of recidivism risk assessment instruments in U.S. correctional settings.

Assessing Risk Among Correctional Community Probation Populations: Predicting Reoffense With Mobile Neurocognitive Assessment Software

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