Science - USA (2021-07-16)

SCIENCE sciencemag.org 16 JULY 2021 • VOL 373 ISSUE 6552 287

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I

n Just Algorithms, Christopher Slobogin

develops two careful and sustained com-

parative arguments in favor of automat-

ing the nature and duration of criminal

sentencing in the United States along

with principles to govern the machinery

of this automation. The primary purpose

of the book is to counter recent arguments

against automated jurisprudence,

especially those made concerning

potential biases inherent in com-

putational risk assessment instru-

ments (RAIs) ( 1 – 5 ).

In the book’s first chapter,

“Rationale,” Slobogin argues that

attorneys, mental health profes-

sionals, judges, and parole board

members frequently impose and

enforce harmful legal decisions

and that, while computational

simulations are imperfect, they

are increasingly valid, fair, and just pre-

dictors of an individual’s future criminal

tendencies. In support of this assertion, he

summarizes his 2015 paper ( 6 ), in which he

claimed that core elements of US culture,

such as individualism, an adversarial justice

system, the “laissez-faire, winner-or-loser

ethos” of the country’s political economy,

SOCIAL SCIENCE

Better algorithms are key to reducing bias in criminal

sentencing, argues a legal scholar

By Michael Spezio

BOOKS et al.

Automating incarceration

the punitive character of “fundamental-

ist Christians,” and historic racism are so

entrenched that they will stymie all efforts

designed to reduce incarceration and end ra-

cial bias in the US criminal justice system.

The book’s central chapters—“Fit,” “Validity,”

and “Fairness”—specify and support the le-

gal principles that must govern the design,

implementation, auditing, and revision of al-

gorithms used in automated jurisprudence.

In “Fit,” Slobogin describes

the criteria an RAI must meet to

conform to the law’s specification

of risk. These include the ability

to provide a “more-likely-than-

not standard” of the probability

of risk that an individual will

commit a specific set of actions

within a given time period rela-

tive to his or her assigned group,

in the absence of any legal con-

straints such as house arrest or

incarceration. RAIs that could

meet the specificity criterion would be

those trained to predict only “conviction for

the most serious violent offenses.”

In his chapter on fairness, Slobogin con-

cedes that allegations that RAIs violate the

Equal Protection Clause of the 14th Amend-

ment have “more than a grain of truth.” He

argues, however, that RAIs that are fit and

valid will not discriminate on the basis of

race or other constitutionally protected cat-

egories, even if their use “has a disparate

racial or sex-based impact.”

To ensure that RAIs are applied fairly un-

der the 14th Amendment, Slobogin argues

that they must be used to estimate risk only

by simulating future violent crimes, not fu-

ture missed court dates, other types of mis-

demeanors, or nonviolent felonies. He also

advocates the use of “race-conscious calibra-

tion...which involves creating different al-

gorithms for different ethnicities or races.”

Slobogin’s primary argument in favor of

RAI-based sentencing appears in his pen-

ultimate chapter, “Structure.” Here, he con-

trasts the advantages of preventive justice,

wherein an individual is incarcerated on

the assumption that they would otherwise

represent a future violent threat to soci-

ety, with the many disadvantages of “just

desert” theory, in which incarceration is

intended only to punish an individual for

past crimes. The crux of his argument is

that “both desert and risk are crucial con-

siderations in fashioning sentences in indi-

vidual cases, and arguably are the principal

considerations in that context.” RAIs, he

maintains, will increase fairness in plea bar-

gaining and charging decisions and “should

trigger more oversight of a post-conviction

process that has long been ignored.”

Ultimately, however, the book does little

to allay concerns about the inherent po-

tential of algorithms to perpetuate racial

discrimination in criminal sentencing, pri-

marily because Slobogin tends to minimize

the potential effects of systemic racism on

the probability that one will be implicated

in a violent crime. Biased data will always

result in processes that are resistant to at-

tempts to achieve algorithmic fairness.

Nonetheless, Just Algorithms is one of

the first in-depth, systematic legal argu-

ments in favor of automating justice that

considers legal and scientific aspects of

criminal punishment via the simulation of

recidivism. As such, the book is necessary

reading for anyone seriously interested in

criminal justice reform and the ethical, le-

gal, and social implications of applying data

science technologies in judicial contexts. j

REFERENCES AND NOTES

1. T. L. Fass, K. Heilbrun, D. DeMatteo, R. Fretz, Crim. Justice
   Behav. 35 , 1095 (2008).


2. J. Kleinberg, H. Lakkaraju, J. Leskovec, J. Ludwig,
   S. Mullainathan, “Human Decisions and Machine
   Predictions” (NBER Working Paper W23180, Social
   Science Research Network, 2017).


3. J. Dressel, H. Farid, S c i. A d v. 4 , eaao5580 (2018).


4. J. Skeem, C. Lowenkamp, Behav. Sci. Law 38 , 259
   (2020).


5. S. Xue, M. Yurochkin, Y. Sun, Proc. Mach. Learn. Res. 108 ,
   4552 (2020).


6. C. Slobogin, Howard Law J. 58 , 317 (2015).

10.1126/science.abj3268

Algorithms are imperfect, concedes Slobogin, but

could be refined to reduce bias in the justice system.

Just Algorithms

Christopher Slobogin

Cambridge University

Press, 2021. 182 pp.

The reviewer is a faculty member of the Psychology,

Neuroscience, and Data Science programs at Scripps

College, Claremont, CA 91711, USA.

Email: [email protected]

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