This document discusses the potential for artificial intelligence and machine learning in the criminal justice system, as well as some of the challenges and risks. It notes that while technologies like predictive policing and tools to assist prosecutors are now technically feasible, there are concerns about biases in the data and algorithms that could negatively impact certain groups. It emphasizes that for AI to be implemented responsibly in criminal justice, policy controls need to be incorporated and data quality must be ensured to avoid amplifying existing social inequities. The future of computational criminal justice requires a focus on "computational policy" alongside the technologies.

1. AI & Criminal Justice Reform

2. 1937 Ronald Coase: transaction costs are a central determinant
of how economic activity is organized.
1997 Ronald Gilson: Imperfect markets give rise to
intermediaries to lift the wedge between parties. “Lawyers are
transaction cost engineers.”
2015 Nicole Shanahan (at Stanford CodeX): Technology
supplements lawyers as transaction cost engineers. Technology
is the ultimate transaction cost economizer.
Origins: I wanted to understand what my job as a lawyer was

4. What the article actually says is this:
When we shift focus from thinking about legal
technology in terms of a lawyer’s efficiency, to
viewing these advancements within the context
of socioeconomic organization, we can begin to
realize its true significance.

5. Borrowing from transaction cost theory,
there should be 3 core tenets of legal technology:
 1. Optimizing for the exchange of information.
 2. Setting consistent expectations between parties.
 3. Mitigating risks.

6. Our job as modern legal technologists is to build
software that mimics the cognitive processes of
lawyers. We expect that we can produce faster,
cheaper and more accurate legal work products.

7. In the context of Criminal Justice
 “Predictive Policing”
 Prosecutor Discretion Tools

11. FOR THE FIRST TIME EVER
THIS IS ALL TECHNICALLY FEASIBLE
SO, WHAT DO YOU NEED TO
UNDERSTAND ABOUT CRIMINAL JUSTICE AI?

12. General AI
Machine
Learning
Logic/Rules
Automation

13. General AI
Machine
Learning
Logic/Rules
Automation
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA

14. “Bad” AI
Machine
Bias
Harmful
Automation
UNDER WEIGHTED
DATA
OVER WEIGHTE
D DATA
DATA WITHOUT
POLICY
OBJECTIVES
NOISY
DATABAD
DATA
MISSING
DATA

15. Beliefs:
A hungry person is allowed to steal.
I have never felt sad about things in my life.
Life Status:
How often to do you feel bored?
How often do you have barely enough money to get by?
How often have you moved in the past 12 months?
How old were you when your parents separated?
Have you ever been suspended or expelled from school?

16. If your score was high/positive in these categories, you
were are more likely to be predicted to reoffend.
However, black defendants who don’t reoffend are
predicted to be riskier than white defendants who
don’t reoffend – this where the algorithm breaks
down.
This is because attributes that predict reoffending vary
by race.

18. General AI
Machine
Learning
Logic/Rules
Automation
Computational
Logic
(1) the representation of facts and
regulations as formal logic and
(2) the use of mechanical reasoning
techniques to derive consequences of
the facts and laws so represented.
Computational
Law

19. General AI
Machine
Learning
Logic/Rules
Automation
Supervised
Learning
Unsupervise
d Learning
 Training Data
 Hand-Labels “These e-mails
exemplify willful infringement”
 Clustering “these e-mails have
similar expressions of willfulnes
s”
 Dimensionality Reduction

20. General AI
Machine
Learning
Logic/Rules
Automation
Supervised
Learning
Unsupervise
d Learning
(Deep) Neural Networks

21. Supervised
Learning
Unsupervise
d Learning
 30 Million Positions from previously playe
d Go matches used as training data
 It then began to play
itself, creating more da
ta for “reinforcement”
learning.

22. General AI
Machine
Learning
Logic/Rules
Automation
Painful and Slo
w

23. General AI
Machine
Learning
Logic/Rules
Automation
SUDDEN

24. The Future of Computational Criminal Justice
 Making a bad system is easier and more likely
than making a good system.
 Making a good system requires us to incorporate
“policy controls” on each and every algorithm.
Think “computational policy”
 Policy is more important today than it has ever
been because of the reach of modern day
computing.