For police, is AI deception technology a 'truth meter' yet?
For police, is AI deception technology a 'truth meter'
yet?
BY WILLIAM J. BRATTON, OPINION CONTRIBUTOR — 07/17/19
12:00 PM EDT
Artificial intelligence (AI) quickly has become a
transformative technology impacting many aspects of our lives through
augmentation of processes and tasks that normally require human intelligence,
such as visual perception, speech recognition, decision-making and language
translation. This technology allows for machines to learn from experience,
refine new data inputs and perform tasks with almost human-like responsiveness.
As companies increasingly rely on AI technology to solve
the most complex and pressing business challenges, law enforcement has turned
to AI as a tool to help execute on the multifaceted mission of modern-day
policing. However, for all the potential that AI possesses for law enforcement,
we are still at the early stages of achieving fully viable and legally
permissible options to meet law enforcement needs — particularly when it comes
to capabilities such as video analytics and facial recognition.
Both have introduced challenges related to accuracy and
bias, already generating skepticism by the public and, in some cases, legal
action or bans by elected officials in pockets around the country. The latest
development garnering attention in the world of AI and law enforcement is
“deception analysis,” which utilizes AI technology to assess an individual’s
truthfulness in criminal investigations and judicial administrative
proceedings.
In policing, we’ve benefitted from AI by gaining the
ability to rapidly analyze large data sets to aid in the identification of
individuals, make predictions on criminal activity and facilitate enhanced
communications. As the police commissioner for the New York Police Department
(NYPD), I embraced the utilization of AI technology to aid in the management of
our precision policing methodology using CompStat — an accountability and crime
reduction approach that leverages intelligence and crime data to inform the
rapid deployment of police resources. However, AI for the purpose of assessing
truthfulness via deception analysis signals yet another example of the
near-term limitations of using this technology.
Deception analysis, which utilizes algorithms to classify
facial micro expressions (there are seven universal micro expressions: disgust,
anger, fear, sadness, happiness, surprise and contempt), coupled with vocal
patterns to indicate an individual’s truthfulness, is attempting to find its
way into the criminal justice process. Deception systems currently being tested
by the Department of Homeland Security try to detect changes in a suspect’s eye
movement, voice and body posture to assess the individual’s likelihood of
acting deceptively.
However, as with polygraph examinations — commonly known
as “lie detector tests” — results from these deception analyses can be skewed
by various physiological and psychological factors. AI-based deception systems
face a similar criticism: To date, there is no scientific evidence of a
consistent relationship between an individual’s internal mental state, his or
her intent, and any kind of external inducements. As a result, models and
algorithms designed to predict or identify deceptiveness may be deemed
unreliable.
For instance, machine learning in AI technology, which is
based on the idea that systems can identify patterns and make decisions with
minimal human intervention, need to ingest baseline data to “learn” behavior
patterns or other indicators of, in this instance, “deception.” This raises
concerns that, currently, in the absence of a data set representative of a
predictable correlation between individual intentions, actions, motivations and
deceptiveness, the results of deception analysis remain limited in terms of
judicial admissibility.
Additional challenges to the reliability of AI-based
deception technology include the process by which the systems are built and
deployed, combined with the legality of the information that law enforcement
can collect and utilize in the face of changing privacy regulations.
Deception analysis is one type of risk assessment
process, which seeks to draw a conclusion about an individual’s truthfulness or
dishonesty based on certain inputs, assumptions and logic. Both law enforcement
and government agencies must fully understand these concerns — and their
potential legal and ethical implications — before they adopt an automated
assessment process to understand an individual’s tendency towards deception, especially
when attempting to adjudicate a suspect’s guilt or innocence.
In my nearly 50 years of law enforcement experience, I
can attest that not everyone behaves in the same manner, especially when they
are trying to hide the truth. Thus, finding a baseline pattern of behavior from
which to develop machine-learning algorithms remains a difficult task. My
concern is that a high probability that someone is lying does not guarantee
certainty that that person is untruthful. And when it comes to enforcing the law,
any mistake could come with a significant toll on individual lives and overall
public safety and trust.
Similar to the recent ban on facial recognition
technology for police and other agency use in San Francisco, I foresee
near-horizon legal challenges for the utilization of AI-based deception
technology. Agencies that seek to quickly deploy micro-facial AI-based
technology for deception identification, without the necessary testing and data
validation, may bear the brunt of the legal challenges. However, these
challenges will not negate or stop further development of this technology.
Instead, they will provide precedents for resolving future privacy and
developmental concerns so that AI-based deception technology eventually can be
a viable tool for law enforcement.
Going forward, the adoption of artificial intelligence by
law enforcement agencies ultimately will help align safety and mitigation
strategies in a dynamically changing threat environment. However, the legal,
technical and ethical challenges which accompany deception analysis or facial
recognition capabilities today should guide law enforcement’s implementation of
AI for investigative support, not investigative conclusion. In the realms of
law enforcement and justice — where the stakes are measured in human lives, and
both nuance and precision are paramount — there is no room for uncertainty or
error.
William J. Bratton is executive chairman of Teneo Risk
Advisory, a global consulting firm headquartered in New York, and vice chairman
of the U.S. Secretary of Homeland Security's advisory council. He was twice
police commissioner of the City of New York, 2014-16 and 1994-96, and was
police chief in Los Angeles for seven years — the only person ever to lead the
nation's two largest police departments. His 46-year career in law enforcement
includes serving as Boston's police commissioner and New York City's transit
police chief.
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