Introduction
Conceptually, Artificial
Intelligence is the ability of a machine to perceive and respond to its
environment independently and perform tasks that would typically require human
intelligence and decision-making processes, but without direct human intervention.
One facet of human intelligence is
the ability to learn from experience. Machine learning is an application of AI
that allows a programme to analyse a set of data and then learn how to make
predictions, or take decisions based on what was learned from previous trials
or experiences. The possibilities of artificial intelligence presently is at
the level of weak artificial intelligence, wherein the algorithm is able to
perform only specific tasks and does not have a general learning capacity.
Although they are not at the same level of a broad intelligence, as is the case
of human beings, such programs can create opportunities for diverse
applications.
The use of
intelligent agents has been developing in the field of criminal justice and has
a broad scope for further development. The current use of the and scope of the
same is differentiated according to the four general pillars of the criminal
justice system, i.e, law enforcement, prosecution, the courts and criminal
correctional systems.
AI in Law Enforcement
“The man who pulls the
lever that breaks your neck, will be a dispassionate man. And that dispassion,
is the very essence of justice. For justice delivered without dispassion, is
always in danger of not being justice.”
-Quentin Tarantino,
The Hateful Eight
Though in common
discourse AI is interpreted as a futuristic phenomenon that can mostly manifest
through sci-fi movies, it has been actively explored since John McCarthy
introduced the area of Artificial Intelligence in 1956. That very year, Philip
K Dick published Minority Report, a book about future technology that makes it
possible to predict crimes and catch criminals before the occurrence, which
later was made into a movie starring Tom Cruise. Though we do not possess
psychic 'precogs' akin to the ones featured in the movie, data mining and tools
such as predictive analytics can indeed do wonders that fall under the ambit of
artificial intelligence.
Artificial
Intelligence is a set of methods and systems used to solve super-complex
problems that cannot be solved by direct application of mathematical procedures
and hence need a certain level of abstraction or thinking, similar to cognition
in humans. Though the human neural network is much more complex and capable of
diverse thought, the AI jumps over the logistical barrier of labour and time,
accessing databases and identifying patterns exponentially faster. In the
context of Law Enforcement, this means collating information about the nature
and circumstances of human behaviour can be used for investigating, and maybe,
more controversially, predicting crime.
The versatility of AI
in this field remains such that it can include recording of behaviour include
facial and vocal recognition, generating
new information through data mining that reveals patterns of organized crime,
influence decision making through search of relevant information to possibly
recording the whereabouts, timings, and profiles of crimes to reveal target
areas and vulnerabilities, and maybe even infer from collected data in order to
'forecast' where and when crime may most probably take place.
Though we still aren't
quite sure of a virtual Robocop or sentient unmanned sky patrol, cameras can
easily recognise faces and detect suspicious behaviour, whether in a shopping
aisle or for planting a bomb. Though more abstract than the visual presentation
of AI that the masses usually prefer, the best use that AI has found is through
software algorithms that mine data and/or influence decisions. This could be
the mathematical reduction and description of an entity (modeling), the
determination of the methods by which limited resources will be organized
(queuing) or gaining insight through reproduction of the dynamics of the system
(simulation).
Information has no
real utility in itself. But once information is made actionable, it becomes
knowledge, and holds innate value and
acts as a resource. Knowledge can be discovered through data mining, or
Knowledge Discovery in Databases, which essentially generates knowledge through
search of patterns occurring across large batches of data, often collected for
different purposes.This often brings forth evidence in criminal cases,
especially financial scams as anomalies and patterns stand out distinctively.
But researchers have further argued that data surrounding the offender and
nature of crimes committed would yield high benefit by throwing light on
geographic, temporal and individual probabilities of crime occuring. Predictive
Analytics provide the risks and opportunities in data, making law enforcement
proactive rather than reactive. This means monitoring of high risk situations,
environments and people and prevention of crimes that would probably occur. We
encounter several frustrating ethical and logical dilemmas here.The building
and training of such predictive tools may very well be imbibed with the bias of
the source of training and show that bias in the results and decisions. The
2016 ProPublics investigation of one such tool named COMPAS revealed bias
against minorities in the process, hence failing to actually keep up the
objective and neutral facade of AI. Furthermore, there are no actual policies
governing the use and implementation of the information by police on ground,
making the probability of exploitation and subsequent encroachment upon civil
liberties seem undeniably high. But if we could, hypothetically, do away with
bias and concealment of information,then we would have, the much desired,
foreknowledge. As Sun Tzu said in the Art of War, 'foreknowledge’ is “the
reason the enlightened prince and the wise general conquer the enemy whenever
they move and their achievements surpass those of ordinary men”.
AI in Prosecution
A prosecutor is an attorney who
represents the federal or state government in court proceedings. They are the
principal representative of the state in every matter related to the
adjudication of criminal offences. The role of the prosecutor can broadly be
divided into two: the investigation process and commencing the proceedings of a
trial which occurs if there is substantial evidence on hand. They investigate
crimes with the police and have contact with the accused, the victim, and
witnesses. Once the preliminary investigations have been completed, they judge
whether there is sufficient evidence to bring the case to court.They question
the suspect, witnesses and experts in order to establish the suspect’s guilt.
The prosecution is carried out if
there are reasonable prospects of securing a conviction and if it serves public
interest. Once the charges are laid, the defendant is notified. The hearing and
trial take place soon after, followed by the sentencing. Prosecutors are
authorised to offer plea bargains and also conduct the trial on behalf of the
state and recommend the accused’s sentence.
In the case of investigations, they
provide advice and make sure that the evidence required for conviction is
present. Artificial intelligence plays a huge role in collecting evidence. They
do this in multiple ways, the first being the AI’s propensity for detecting
patterns. To do this, AI systems are fed with multiple images found at crime scene over the years, and are made to
recognise patterns and even possible connections between criminal cases. This
in turn will alert the police that there are crime patterns and evidence to be
collected. The above was an example of an AI software being developed in the
University of Leon in Spain, the prototype of which will soon be trialled by
the Spanish police force. This is increasingly important due to the vast amount
of time required to carry out a proper investigation coupled with the number of
cases to be taken care of as well the cost of carrying it all out. Because
budgets are insufficient and the police are understaffed, the AI can greatly
aid the police force in this capacity. They are able to filter through the
immense amount of visual stimuli a lot more efficiently than humans possibly
could. Moreover, due to their efficiency and lack of fatigue, the ground
covered by an AI system is more extensive. The 8.7 million images of child
nudity that had been unearthed on Facebook in 2018 was only possible due to the
creation of a software used which was able to identify and flag all possible
images of children depicted in any sexual capacity. AI in this foray has an
incredible amount of potential but it must be noted that the decision made by
the software cannot be changed once the decision is taken to the courts.
DNA collected at the scene of crime
is crucial evidence. However the DNA collected usually comes from multiple
sources (such as the victim, a pet, a witness, the suspect etc.) It is time
consuming for DNA analysts to separate and distinguish the sources of the DNA
and most of the time, inaccurate as well. In fact a study conducted on 108
forensic labs in the US wrongly detected DNA material from three people instead
of two and in real life this could have resulted in an innocent person being
falsely accused and implicated in the crime.
A system called PACE (Probabilistic
Assessment for Contributor Estimation) which was developed in Syracuse
University, is a machine learning algorithm which has been trained on thousands
of dummy samples which contained DNA from multiple sources. The software
gradually learned to differentiate between the DNA. Although not completely,
accurate it is still more so than the alternate method.
When police are on the lookout for
missing persons or murder victims, knowing what the person is helpful. At
present, forensic anthropologists work by piecing together fragments of a
person’s face and build up the facial tissue using a physical medium such as
clay. This task is laborious and very time consuming and the accuracy usually
depends on the anthropologist. A system is being developed at the Louisiana
State University where the programmer trains the algorithm by feeding it images
of people’s faces in order to find a face that would most closely fit the
reconstructed skull beneath. In order to do this, the system creates several
thousand facial structures and discards thousands more before finding the one that
provides the best match
Similar to its role in procuring
evidence, AI is widely used in various stages of the trial. It begins by
playing a role in legal analytics which is used to predict future events and
identify trends and patterns. This is possible because the AI system can do a
thorough and comprehensive data search, finding relevant points from past
cases.
In witness testimonies, it is
important to ascertain the accuracy of their accounts. AI can help here because
it can detect whether the witness is lying. This is referred to as demeanour
evidence which is used to assess behaviour, conduct and mannerisms in the hopes
of establishing more credibility or lack thereof, to a witnesses testimony.
Facial lie recognition uses micro expression, movement of individual facial
muscles and body language. Such AI algorithms are already being used at
checkpoints between the border crossing points in Europe. A software used in
the court called DARE (Deception Analysis and Reasoning Engine) which was
developed and designed in the University of Maryland, was programmed with
videos from the courtroom. It managed to spot 92% of the micro expressions
displayed. In the case of bails, AI systems have been used in risk assessment
to determine the extent of recidivism (whether the person is likely to repeat
the crime). Judges often grant bail (or do not) based on this.
It is clear to see that AI plays a
substantive and vast role, one that is ever expanding. Although AI does make
for a more efficient system, it is far from perfect. In fact, its usage calls
many other ethical matters into consideration, one such issue being privacy,
another being that AI systems lack empathy and discretion. Is it possible to
allow a machine the sovereignty to
impinge upon the fate of a human being? Moreover, because most software
programmes are proprietary, these companies are not liable to sharing their
code. As a result of this, there is a judicial system that is not required to
explain itself. Due process of the law allows for cross examination on the part
of the defendant which is no longer possible once AI is thrown in the mix. In
the case of an unfair or faulty ruling even judges are apprehensive of changing
the same and take the AI’s input into consideration because it has reviewed thousands
of similar cases. It will take away from the transparency and accountability of
the justice system, which is perhaps the biggest ethical violation. Although AI
systems may be less biased than a human may be. the role of the programmer
still plays an important role and any biases he or she may have, creeps into
the programme as well; as was seen when an AI system wrongly identified dark
skinned members of Congress in the US as criminals.
Although artificial intelligence is
not yet being used to its full potential it still plays a larger role than most
people are aware of. Opinion continues to be divided on whether AI systems can
somebody be competent enough completely take over the roles of attorneys.
Others however believe that AI no matter how advanced can never fully take over
and will merely remain aides in the criminal justice system.
AI in the Courts
Once a crime has been committed and
a violator has been identified by the police, the case goes to court. A court
is a system that has the authority to make decisions based on law. Criminal
cases are heard by trial courts with general jurisdictions. Usually, a judge
and jury are both present. It is the jury’s responsibility to determine guilt
and the judge’s responsibility to determine the penalty, though in some states
the jury may also decide the penalty. Unless a defendant is found “not guilty,”
any member of the prosecution or defense (whichever is the losing side) can
appeal the case to a higher court.
There are numerous researches which
attempt to apply, justify, or as a matter of fact, unjustify the use of the
Artificial Intelligence in court rulings. AI software used for finding patterns
in the process of decision-making are suggestable options in predicting the
outcome of court trials. As reported by an article in The Guardian, a group of
computer scientists at University College London devised an AI Judge to predict
the results of real life cases. The artificial judge arrived at approximately
the same verdicts as the judges at the European Court of Human Rights in almost
four in five cases involving torture, derogatory treatment and privacy. The software
was designed to accommodate legal evidence along with moral questions of right
and wrong. The algorithm examined data sets for related cases. In each case,
the software analysed information and made a judicial decision, 79% of which
were the same as the verdicts delivered by the court.
However, the concept of artificial
judicial judgements would require replicating a human conscience altogether,
which falls under the purview of strong artificial intelligence. Technology has
not made such high advancements that it could replicate a human brain.
Another important research was
conducted by the National Bureau of Economic Research in the USA. A software to
measure the likelihood of defendants fleeing or committing new crimes while
they are awaiting trial in liberty was developed (Júnior, 2017). The algorithm
assigned a risk score based on the offense they are charged with, when and
where the person was detained, their age and their criminal record. The
software has been tested on numerous criminal cases in New York and has been
proven to be more efficient at assessing risk than judges.
However, the question of their
accountability and transparency of these algorithms still stand since they may
reproduce human prejudice and prevalent racial disparities. Such algorithms
should be verifiable and auditable in order to prevent non-transparent decision
making criteria.
The
character of justice. Using
hyper-complex modelling decision making techniques guarantees that cases with
meaningfully identical features always have the same outcome. (Brennan-Marquez,
& Henderson, 2018)
However, even if the AI can make the “right verdict” on a
case, should it be allowed to?
The argument boils down to the fact
that in any liberal democracy, there should be an aspect of role-reversibility
to judgements. In some contexts, those who exercise judgment should be
vulnerable, in reverse, to its processes and effects. And those subject to its
effects should be capable, reciprocally, of exercising judgment
(Brennan-Marquez, & Henderson, 2018). What matters is whether
decision-makers are situated to imagine themselves into the role of an affected
party, and vice versa—such that both participants, and in some sense the entire
moral community, can understand judgment as a democratic act.
Even in the case of jury trials,
role reversibility is exercised to some extent. Even when a jury trial does not
lead to a different outcome than a trial before an institutional judge, it
facilitates the systematic recognition of judgment’s human toll. Thus
transforming the trial into a fairly democratic act.
Should the execution of laws of the
community be entrusted to AI? Even though the delegation of such power can lead
to consistent and accurate decisions, relieving us from the agony of decision
making. Each decision is primarily based on a value system and an
implementation outcome (which has its roots in logic). To ensure the same, it
is important to keep humans ‘in the loop,’ exercising ultimate say over the
decision-making process.
Scope for future development. Although having AI judges is a debatable
concept for now and in the near future, it is feasible for AI to play a
supporting role in the decision making process. A decision making version of
the Eisenhower Matrix can be
employed which helps distinguish between
what is important and what is urgent. Urgent tasks are time sensitive whereas
important tasks are more strategic. When it comes to decision making, decisions
can vary in their reversibility and the level of consequences that they may
have. (Farnam Street, 2018)
Weighing
these factors can help one delineate possible deadlines for a task and
prioritise the same. It can also help decide whether delgatation of the task
would be a feasible option. Making use of the Eisenhower matrix can help
increase the productivity of a system as well as direct flow of labour
efficiently in the right direction.
If a
software could be developed wherein algorithms could process case information
and organise them on the basis of their urgency, importance, reversibility or
consequences, decision making processes could be more addressing criminal cases
considering said factors.
AI in Criminal Correction Systems
When it comes to the field of criminal
corrections, AI has been shown to do two things remarkably. Get the job done
and/or fail spectacularly at it. The applications for AI in the field of
Criminal Correction systems is actually seemingly endless. Given the number of
individuals trapped within the confines of the legal system and ultimately
within jails, it is estimated that 1 in every 38 Americans is or has served
some prison time. Thus AI equipped with Re-Offender algorithms plays an
important role here.
It estimates the capacity of each individual
to not only commit a crime but also whether they will commit the same crime
again. It has been proved however, time and time again that the AI fails rather
miserably. It has intense difficulty identifying faces of colour and at times
has even mistaken members of Congress for criminals (Hao, 2019). It is stated
that modern algorithms are driven by training based on historical crime data.
The error here might lie in the fact that AI
uses a strategy of machine learning such algorithms instead of deep learning
modules that could help advance the scoring mechanisms.
AI has also successfully been used in
systems of modern prison management using Bayesian algorithms. It has been used
in three broad areas in the prison systems, namely, overcoming one-sided cell
allocation strategies (where instances like individuals known to the criminal
are avoided); lack of scientific guidance (Where one needs to overcome the
human errors in cell allocation) and lastly the influences of uncertainties of
allocation results (where previous offenders might try to escape based on
available conditions in relation to their housing cell). These systems have
been implemented in management in areas in China (Jang, Wang and Wu. 2018)
References
Al Fahdi, M., Clarke, N. L., &
Furnell, S. M. (2013). Towards An
Automated Forensic Examiner (AFE) Based Upon Criminal Profiling &
Artificial Intelligence. Retrieved August 13, 2019, from
https://pdfs.semanticscholar.org4fb1/0dbfc73cf8c1b1f4e387344bf8f4af9a3060.pdf
Angwin, J., Mattu, S., Larson, J.,
& Kirchner, L. (2016, May 23). Machine
Bias. Retrieved from
https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
Brennan-Marquez, K., &
Henderson, S. E. (2018). Artificial Intelligence and Role-Reversibility. The Journal of Criminal Law and Criminology,
109(02), 137-164. Retrieved August 14, 2019
C. B. (2019, March 4). The New Weapon in the Fight Against Crime.
Retrieved from
http://www.bbc.com/future/story/20190228-how-ai-is-helping-to-fight-crime
Farnam Street. (2013, April). Eisenhower Matrix: Master Productivity and
Eliminate Noise. Retrieved August 15, 2019, from Farnam Street:
https://fs.blog/2013/04/eisenhower-matrix/
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Evidence. Retrieved from
http://www.slaw.ca/2019/03/24/using-artificial-intelligence-for-demeanour-evidence/
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https://www.theguardian.com/technology/2016/oct/24/artificial-intelligence-judge-university-college-london-computer-scientists
National Research Council. 2001. What's
Changing in Prosecution?: Report of a Workshop. Washington, DC:
The National Academies Press. https://doi.org/10.17226/10114.
Philipsen , S., & Themeli, E.
(2019, May 15). Artificial intelligence
in courts: A (legal) introduction to the Robot Judge. Retrieved from
http://blog.montaignecentre.com/index.php/1942/artificial-intelligence-in-courts-a-legal-introduction-to-the-robot-judge/
Weber, S. (2018, January 10). How artificial intelligence is transforming
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https://www.thoughtworks.com/insights/blog/how-artificial-intelligence-transforming-criminal-justice-system
Credits
Group 8, AI in Criminal Justice:
1. Debargha Roy,1833208-
Documentation
2. Sai Siddharth, 1833210-
Presentation
3. Parth Malhan, 1833216-
Presentation
4. Rishabh Bapat, 1833218-
Scriptwriting
5. Rohit Jaiswal, 1833219- Video
Editing and Direction
6. Sriram Nair 1833225- Acting
7. Y Arulvel, 1833226- Videography
8. Nathan Zachary Fernandez,
1833237- Documentation
9. Radhika Rastogi, 1833280-
Documentation
10. Simone Diya, 1833294-
Documentation
11. Therese Liam Tom, 1833297-
Acting
Areas to Excel
