Technological Innovations in Crime Prevention and Policing Report

This report delves into the realm of technological innovations within the context of crime and law enforcement. It begins by defining and differentiating between 'soft technologies' and 'hard technologies,' providing examples such as Automated Fingerprint Identification Systems (AFIS) and CCTV cameras, and exploring their applications in crime prevention and control. The report then critically evaluates the effectiveness of AFIS, examining its functionalities, advantages, and challenges, including those related to fingerprint data acquisition and matching algorithms. Furthermore, it discusses the ethical considerations, challenges to privacy, and fairness issues associated with the use of such surveillance technologies. The report draws upon research to assess the impact of these technologies on policing strategies and their overall contributions to crime prevention and law enforcement practices.

Running head: TECHNOLOGICAL INNOVATIONS IN CRIME
Technological Innovations in Crime
Name of the Student
Name of the University
Author’s note

1TECHNOLOGICAL INNOVATIONS IN CRIME
Question 1: Key characteristics of ‘soft technologies’ and ‘hard technologies'
Soft technologies are such kind of technological innovations that involve the use of
strategic systems for gathering information for the purpose of preventing crime. In the recent
years, a wide range of innovations in the field of soft technology have developed. The soft
technologies are defined as such kind of technologies that are mainly operated by software
systems that are used by computers.
Some of the key characteristics of soft technologies are:
1. The soft technologies help in improving the performance of police to gather data with
the help of software systems (Tang et al. 2014). These technologies include new kind of software
programs, crime analysis techniques, data sharing techniques of system integration and
classification techniques.
2. Some of the latest innovations such as the rise of threat assessment protocols, software
programs, tools for identification of bullying incidents, data privacy tools and many other have
been developed with the rise of innovation in soft technologies.
3. Soft technologies, which include data analysis techniques, crime mapping software and
many others are capable of preventing serial offenders and development of strategies of crime
prevention.
4. Some other soft technologies, which are used to prevent incidents of crime are in-car
video recording capability, wireless mode of video streaming, license plate readers and many
others.

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On the other hand, hard technologies include devices, new materials and other
equipment, which can be used for either committing a crime or preventing them. With the rise of
several crime incidents, there has also been a rise in the growth of hard technologies to prevent
several incidents of crime (Carroll 2014). These include baggage screening systems present in
airports, metal detector in schools and bullet proof windows at banks.
Some key characteristics of hard technologies are:
1. Some hard technological innovations such as improved street lighting and growth of
CCTVs have shown tremendous growth in the recent years (Kennedy and Wilken 2016). These
devices have revolutionized the security aspects in various areas.
2. Different crime prevention organizations imply the use of hard technologies to secure
their systems from various incidents of crime and situations of fraud. Some additional factors
that are included is the amount of harm, which is prevented on victims who are harassed or
harmed repeatedly.
As discussed by Bryne and Max (2011), one example of ‘hard’ technology of
surveillance is CCTV cameras that are installed in homes and offices. One example of ‘soft’
technology of surveillance is Automated Fingerprint Identification Systems (AFIS).
Among these examples, the AFIS would be related to a strategy of pre-emptive policing
based on crime control. The software technology installed within AFIS is a new kind of
innovation within police technology. With the impact of this kind of technological systems, there
has been a control in the number of incidents of crimes (Girelli 2015). Different recent reviews
based on technological innovations and adoption of such strategies by police agencies have
highlighted that with the impact of such kind of software systems, they have been able to curb

3TECHNOLOGICAL INNOVATIONS IN CRIME
the increasing incidents of crime. Such kind of systems as AFIS have been able to map systems,
track the activities of individuals and also report suspected incidents.
Question 2: Evaluation of chosen example from chosen technology
The Automated Fingerprint Identification Systems (AFIS) had emerged in the 1980s. The
use of AFIS systems have mainly affected the sector of criminal identification, which is one of
the central work of police organizations and various law enforcement agencies. The AFIS has
been defined as an effective system, which has proved its effectiveness in the identification of
people and establishing of criminal history for offenders (Alberink, de Jongh and Rodriguez
2014). The AFIS is also capable of storing data in their huge database systems and scrutinize
them. The system supports potential kind of fingerprint matches within a few minutes. This is a
product based on intensive form of research and development.
The AFIS is capable of tracing the roots of several incidents of crime. With the evolution
of technology in the recent years, there have also been a highlight on the opportunities based on
effectiveness of gathering physical evidence. The most notable aspects considered within AFIS
is fingerprint, which is mainly used for improving the performance based on solving of crimes.
However, there are certain challenges, which are also faced by AFIS systems. These are:
1. The reading and capturing of the traditional form of ink-on-card fingerprint image.
2. The assessment of distinguishing features within the captured image.
3. The indexing of captured records.
4. The comparison of minutiae data with a large form of database based on similar
records.

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The challenges posed towards the AFIS could be mitigated by encompassing the tenprints
and latent prints. The tenprints technique comprise of an entire set of fingerprints that would be
collected from an individual and would be stored within a single sheet (Anthonioz and Champod
2014). Tenprints are generally referred as known prints due to the reason that the identity of
source and the impression of finger is unknown. In a traditional manner, the process of tenprints
is applied with a thin coating of ink that is spread across the end of fingers and thus are rolled on
a card. In a recent manner, live scan devices have also been used (Peralta et al. 2015). Latent
prints are recovered from any scene of crime with the help of chemical, physical or lightning
based techniques. These prints are highly or partially fragmented and thus they pose real
problems as compared to reliable techniques of automated matching.
AFIS thus completely fills in with the objective for which it is been used by the crime
investigation agencies. The effective use of highly sophisticated algorithms is one of the crucial
elements within the process (Yao et al. 2016). These algorithms have thus developed over the
years and have enhanced with the passage of time with the basis of real world experiences. The
most commonly used examples include feature extraction, indexing, image enhancement and
matching.

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References
Alberink, I., de Jongh, A. and Rodriguez, C., 2014. Fingermark evidence evaluation based on
automated fingerprint identification system matching scores: the effect of different types of
conditioning on likelihood ratios. Journal of forensic sciences, 59(1), pp.70-81.
Anthonioz, N.E. and Champod, C., 2014. Evidence evaluation in fingerprint comparison and
automated fingerprint identification systems—modeling between finger variability. Forensic
science international, 235, pp.86-101.
Carroll, J.M., 2014. SOFT VERSUS HARD. HUMAN–COMPUTER, p.424.
Girelli, C.M.A., 2015. Laterally Reversed Fingerprints Detected in Fake Documents. Journal of
Forensic Identification, 65(1).
Kennedy, J. and Wilken, R., 2016. Disposable technologies: The halfwayness of USB portable
hard drives. Journal of Mobile Media, 10(1).
Peralta, D., Galar, M., Triguero, I., Paternain, D., García, S., Barrenechea, E., Benítez, J.M.,
Bustince, H. and Herrera, F., 2015. A survey on fingerprint minutiae-based local matching for
verification and identification: Taxonomy and experimental evaluation. Information
Sciences, 315, pp.67-87.
Tang, D., He, C., Li, Y., Zang, H., Xiong, C. and Zhang, J., 2014. Soft error reliability in
advanced CMOS technologies-trends and challenges. Science China Technological
Sciences, 57(9), pp.1846-1857.
Yao, Z., Le Bars, J.M., Charrier, C. and Rosenberger, C., 2016. Literature review of fingerprint
quality assessment and its evaluation. IET Biometrics, 5(3), pp.243-251.