Development of Integrated Criminal Justice Expert System
Applications
Kevin J. Lynch, M.S.,
Artificial Intelligence Group
Management Information Systems Department
University of Arizona
Tucson, Arizona
Frank J. Rodgers, B.S.
Latent Print Section Supervisor
Laboratory Bureau
Phoenix Police Department
Phoenix, Arizona
Introduction
In April of 1988, the Phoenix Police Department acquired twenty million dollars of bond
funds to upgrade its information processing capabilities over the next five years. Enhancements
include additional systems for computer-aided dispatch, computerized record management, and
automated fingerprint identification. On a national and international level, the use of computer
technology in the criminal justice system today is characterized by a large number of complex
systems, large data stores with redundant and sometimes conflicting information of varying
quality [13,14], little integration among systems, and poor support of the life-cycle from the
initial crime report to final adjudication. "Expert system" technology has recently become
available from the field of artificial intelligence that would enable `intelligent' access and use of
existing data stores to greatly increase the effectiveness of the law enforcement community.
Expert systems are being developed for burglary [16], murder [26,28], forensic science [27], and
hostage-taking incidents [29]. The FBI considers artificial intelligence techniques to be
extremely valuable for effective law enforcement through the next decade [3,30]. To fully utilize
this new technology, an architecture is necessary to define an integrated, expandable structure
that capitalizes on the significant investment in existing systems and technologies.
Existing Systems
The Phoenix Police Department is either using or installing a number of criminal justice
information systems (CJIS). The summaries below give examples of the types of systems that
might be integrated for substantial benefit to the law enforcement community:
CAPRI - Computer-Aided Police Records Index - manages criminal history information at a
local and regional level. Queries can be initiated on-site, from remote terminals, and from in-
vehicle mobile data terminals. Data includes criminal histories, incident report references, name
indexes, fingerprint classifications, personal identification numbers, subject demographic
information and violence potential [23].
CAPRI is the forerunner of PACE, described below.
PACE - Police Automated Computer Entry - evolved from the recognition that a growing
burden of paperwork was threatening the efficiency and effectiveness of the Phoenix Police
Department. PACE will automate the access and update of offense reports, supplements, arrest
records, juvenile referrals, and field interrogation cards. PACE is anticipated to reduce patrol
officers' report-writing time by 50%, and eliminate data redundancy existing in the current
disjunct systems [24].
AFIS - Automated Fingerprint Identification System - provides automated searches of 10print cards and latent fingerprints and generates a ranked candidate list. Unsolved latents can be
entered and automatically searched against new 10-print cards [22].
WIN - Western Identification Network - is a regional network designed to access AFIS
systems in eleven western states (Alaska, Arizona, California, Colorado, Idaho, Montana,
Nevada, Oregon, Utah, Washington, Wyoming) [9].
NCIC - National Crime Information Center - a national information system which uses a
telecommunications network to store and retrieve wanted person and and stolen item data
(vehicles, guns, license plates, securities).
Expert System Technology
Historically, criminal justice computer automation has focused on tasks such as storage and
retrieval of criminal histories, modus operandi, and fingerprint information - problems addressed
by deterministic, algorithmic solutions. Much more difficult is the computerized solution of
problems that require considerable human expertise, i.e., forensic investigation. Expert systems,
frequently termed "knowledge-based" systems, address this important class of problems. Expert
systems are computer programs that perform at the level of a human expert, typically in a
`narrow' field. For instance, an expert system developed for the police department assists
detectives in identifying possible burglary suspects [25]. One important feature of an expert
system is its ability to provide users with an explanation of the reasoning employed to reach a
conclusion. In addition, expert systems can work with incomplete or unavailable information,
and can produce ranked results based on a weighting of the significance of the inputs.
An expert system is constructed by first capturing the expertise of one or more recognized
experts in the application area. Typical experts apply assorted "heuristics," or rules-of-thumb, as
well as an application-specific body of knowledge to the problem at hand. The task of
embedding this knowledge in an expert system is known as "knowledge engineering."
Knowledge engineering is an iterative process that allows the expert and knowledge engineer to
continue to refine the rules and facts embedded in the expert system until it functions at the level
of the expert. Unlike conventional computer programs, expert systems are designed to be readily
modifiable to incorporate new, conflicting, or incomplete information.
Expert systems provide users with consultation on problems in the application domain. Often,
user input is followed by system inquiries for information in order to further direct its search.
Heuristics determine whether a possible conclusion or multiple conclusions are reachable. When
a set of conclusions is reached, the expert system ranks them with respect to their certainty based
on available information. The user can query the expert system at any time for the rationale used
to solve a problem. This explanation capability provides significant assistance regardless of the
user's degree of expertise, because it allows an objective evaluation of the schemes being used to
reach a conclusion.
Expert systems are necessarily domain-specific applications. The heuristics used in one
domain do not generalize easily to other domains, in most cases. Terminology and the body of
knowledge applied in the area of expertise is often very specific, although it usually builds on
much more general areas (i.e., criminalistics involves the application of the natural sciences in
forensic investigations). A considerable amount of work is necessary to build an expert system
that typically combines 500-1500 rules with what can be a very complex representation of the
body of knowledge for the application domain. The actual cost of constructing expert systems
continues to decline, however, as new, more powerful tools are introduced [5,19].
Expert System Applications
An expert system's matching and ranking capabilities, combined with the power of enlarging
or narrowing the geographic scope of an inquiry, provide powerful tools for investigative
analysis. In a crime scene scenario, for instance, with little or no evidence, an expert system can
generate a demographic profile of a suspect based on variables such as type of crime, location,
time of day, and method of entry (if applicable). The demographic profile can then be used to
generate candidate lists while expanding or narrowing a geographic search based on similar
criteria.
At a different level of analysis, the modus operandi (MO) of a particular crime can be used by
the expert system to identify the set of solved and unsolved crimes with similar MOs. It is the
expert system's ability to match the MO of crimes based on similarity that is so powerful,
because it can take into account conflicting or incomplete information that conventional
computer programs are hard-pressed to deal with effectively.
In the Phoenix Police Department, the CAPRI or PACE systems would be searched to
identify crimes with similar MOs. Once these crimes are identified, a number of possibilities
arise. The first involves aggregating the physical evidence at other similar crime scenes to
narrow a candidate list based on the subset of physical evidence that seems particularly
applicable. This, in many cases, may pare the candidate list by an order of magnitude simply by
adding limiting parameters suggested by the aggregated evidence.
Another possibility involves a scenario in which a suspect is stopped and has a television in
the back seat of his car. As the officer enters the serial number into the PACE computer through
his mobile data terminal (MDT), across town another officer has just finished taking a stolen
property report and begins entering the data into PACE. A match of the stolen TV is made and
the suspect is arrested. Again, the expert system can be used to identify similar unsolved crimes
and the information associated with those crimes, both circumstantial and physical, used to
interview the apprehended suspect, in an attempt to solve additional crimes.
Architecture
Expert systems provide ideal "front-ends" to the wealth of information that currently resides
in multiple, heterogeneous CJIS. If the data in these systems is made available to end-users
through an intelligent interface that represents the collective expertise of experts in their
respective fields, the use of this data can be much more effective and efficient. By integrating
existing systems through a single interface specifically tailored to the task, intelligent searches
are performed of only the necessary or pertinent systems, and are completely transparent to the
user. Those searches will ultimately be more efficient than currently because expertise will be
applied to the query, thereby limiting its use of computer resources by intelligently adding
search-limiting parameters [11]. This approach saves considerable time by providing the enduser with a single, intelligent interface, specific to their task, capable of guiding them through
their search for information. Implementation of this scheme requires an architecture which takes
into account the multiplicity of existing heterogeneous systems as well as the domain-specificity
of expert system applications. Such an architecture, using existing or proposed systems in the
Phoenix Police Department, is graphically depicted in Figure 1.
The "CJIS Interface" provides a single, unified view of the existing CJIS to the expert
systems. Any expert system needs only to specify what data is required, while the details of how
to perform the search are the responsibility of the CJIS Interface. Due to the possibly redundant,
conflicting, or absent information in a given set of CJIS, the CJIS Interface controls the search of
appropriate systems, reconciles conflicting information, and notifies the expert system when data
for its request does not exist in the supported CJIS [7]. Expert systems can be constructed for
additional applications and have immediate access to all of the information in the existing CJIS.
New or modified systems can be added as well, making the new information immediately
available to the existing expert systems by updating the CJIS Interface.
The CJIS Interface provides a lower-cost alternative to complete integration of existing CJIS.
The CJIS do not have to "talk to" each other in this architecture, nor do they need to know the
other systems exist. The CJIS Interface has knowledge about the type of information that resides
in the different systems, as well as knowledge about how to access it, effectively integrating the
systems from the point of view of the user interface. The systems may be on different machines,
with different operating systems, communication protocols, media, and file formats. That
information is contained in the CJIS Interface, isolating both the user and the builder of the
expert system from the procedural details of providing access to the data in the existing CJIS. In
addition, this architecture exploits the inherent parallelism of the CJIS - different searches can be
performed on two different CJIS at exactly the same time.
A standardized CJIS Interface, common to all law enforcement agencies, facilitates the
proliferation of expert system technology regardless of the CJIS actually in use at a particular
site. Because all data access is performed through the CJIS Interface, the expert system can be
transplanted to another site with little difficulty. And the interface allows different systems and
data to be applied to the problem-solving task, using the same expert system user interface. A
common interface provides extensibility to the architecture, as systems can be added on either
side of the interface and the increased information made quickly available to the end-user. This
standardization applies at local, regional, national, and international levels, enabling
geographical expansion of inquiries across dissimilar systems. This would enable, for instance,
international policing organizations to identify similar terrorist incidents to aid in the tracking of
world-wide terrorist organizations.
Linking multiple, heterogeneous systems through a single interface is a complex task,
although implementations [8, 12, 20] and standards [2] exist. Other interfaces have already been
constructed that facilitate rapid prototyping and evolutionary support of complex applications
[18], allowing features to be added or changed easily with no adverse impact on existing
functionality. The CJIS Interface incorporates a layered approach to isolate different machine
dependencies while taking advantage of a common query and data representation format.
Additional complexity in interface construction is introduced when considering the efficient
retrieval of textual information from the large data stores that comprise some CJIS. Even though
the search would be limited to those pieces of text that met the limiting qualifications discussed
earlier, the efficient processing of the retrieved text must still be addressed. Much of the
information necessary to compare MOs, for instance, is stored in the form of text. This
information is not indexed in the currently available CJIS to facilitate efficient processing. The
time necessary to manually index the existing information comprehensively makes this approach
unattractive, while automated full-text indexing techniques suffer problems when advanced
compensating techniques are not used [21]. `Smart' indexing of the textual information can be
performed by automated indexing tools outfitted with domain-specific indexing knowledge [6].
These indexing techniques, however, require additional work to be completed on the target CJIS.
Fortunately, the CJIS Interface takes the available indices in the existing CJIS into account when
planning a retrieval strategy [17], thereby performing a high-level optimization of the retrieval
request [4] that partially eliminates text processing inefficiencies.
Ideally, the law enforcement community can mandate the design of and adherence to a CJIS
Interface standard, as has been done for fingerprint identification data interchange [1], and can
include index standardization. This benefits CJIS vendors, expert system vendors, and the law
enforcement community itself. CJIS vendors can then focus on developing their base
technologies, rather than user interfaces. Expert system vendors are guaranteed a uniform
interface for data access which allows them to concentrate on their core technology. End-users
realize benefits in increased system compatibility and functionality. One way to enforce a
proposed interface standard is to require adherence to the standard in future vendor contracts.
In the absence of a formal standard and vendor participation, an interface can still be
constructed for a particular site. An interface designed with only a small subset of systems in
mind, however, is sure to suffer portability and extensibility problems. Still, for a single site, this
is a lower-cost method for integrating existing systems and providing in-house extensibility of
systems than a full-scale system-level integration effort.
Cost Savings
Police departments are labor-intensive organizations. The single highest recurring cost is
salaries. The advent of expert systems in the criminal justice system community will provide for
the more efficient use of human resources to focus on crime prevention. Expert systems, coupled
with integrated data stores, significantly reduce personnel costs by saving time in collecting,
evaluating, and searching for information [15]. Quicker suspect identifications saves
investigative staff-hours and reduces serial offenses.
The expert system "front-ends" operate in a stand-alone fashion for training purposes,
continuously available for consultation. In this capacity, the expert systems perform as "on-line
mentors" for the novice at a fraction of the cost of providing a human expert. The steep learning
curve associated with becoming technically proficient in a given specialty is overcome more
quickly and cost-effectively, because the systems not only advise `what' to do but `why.'
Conclusion
Enormous sums are invested in current CJIS, while equally large amounts are annually
invested in law enforcement personnel salaries. As expert system technology becomes available
in the commercial marketplace, it enables the law enforcement community to significantly
leverage its computer and human resources. Considerable additional benefits are gained by
standardizing an interface to use this new technology. Doing so will preserve the commitment in
existing resources, greatly enhance the abilities of law enforcement personnel through integration
and intelligent use of systems, and provide a mechanism for system portability and growth at the
local, regional, national, and international levels.
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