Discussion On Automated Facial Recognition Authentication

Question

Task: Write a detailed report illustrating the risks, benefits, and ethical implications associated with Automated Facial Recognition Authentication.

Answer

Introduction
The present study outlines the main purpose and various aspects of Automated Facial Recognition Authentication. Security and privacy have emerged as the primary concerns for the users and the business organizations in the current times. This is because there are a number of new mechanisms that are being developed to violate the security and privacy norms. One of the initial measures towards security is the authentication and authorization of the users associated with a particular system or application. There has been immense transformation in this field as well. Biometric recognition systems are the latest security measures that are being used for user authentication. These systems record the biological features of the user and map them with the ones stored in the database to validate the user identity. Facial recognition systems are one of the biometric recognition systems that have been developed(Patel, 2018).

One such facial recognition systems called Automated Facial Recognition Authentication; AFRA has been developed by the State Government. The purpose of the system is to secure the access to the various state-level services. The pilot application in which Automated Facial Recognition Authentication will be implemented deals with the renewal of vehicle, boat, and firearms licenses. The paper covers the risks, benefits, and ethical implications associated with AFRA.

Risks & Benefits of Automated Facial Recognition Authentication – Identification of Individual Users
Set of Risks
There are several risks that are associated with the use of Automated Facial Recognition Authentication for the identification of the users that may wish to have their license renewed. The major risk is of lesser accuracy of the technology and the increased bias that may have negative implications on the users. The essential principle on which Automated Facial Recognition Authentication will work is the recording of the face of the user through a surveillance or web camera. The recorded details will be matched with the ones that will be present in the database to determine if there is a match or not. The users with the match will be provided the access and the ones without the match will not be given the access. As per the study conducted by the UK Government, there were only 2% of the cases wherein the technology was found to provide accurate results. In 98% of the cases, the outcomes were incorrect (Noden, 2018). This is a major risk for the users as the accuracy of the services will not be proper. The users that may want to have their license renewed may not be given the access due to these loopholes.

Another study by NIST has shown that there were inaccuracies that were reported on the basis of the skin color. The same may be witnessed in the case of Automated Facial Recognition Authentication. The algorithm designed for the recognition of the faces may fail to recognize the people of specific colors. For example, Idemia, French algorithm for facial recognition mixed up the faces of black men and women (Simonite, 2019). The similar risks may be witnessed in AFRA which may lead to unwanted bias for the users due to the system limitations and inaccuracies.

There is also a significant bias that may be associated with the technology and the information present in the database. The database will have the characteristics that will be recorded and saved so that the match with the user’s face can be made. The database will be accessible by the State Government personnel and there can be database security issues and insider threats that may be launched. The bias may be done towards a particular category of the users deliberately on the basis of their color or gender. The users will be deprived of using and availing the license services in such cases (Harding, 2019).

What are the potential advantages of Automated Facial Recognition Authentication? There are also several advantages that the users that wish to renew their licences can achieve with the implementation of Automated Facial Recognition Authentication.

• With the accuracy of the matching results, the users will be assured of the elimination of forging of their identity and the privacy of the information will be preserved.
• There can also be an alarm system that may be included which may send the alerts to the State Government and the original user in the case of an attempt to forge the identity.
• The processing speed of the technology will be higher than the options that are currently available. This will assist the users in getting the services done quickly(Khan & Upadhyay, 2015).

Ethical Implications – Individual Privacy
There are negative implications on the ethical aspects in association with the use and deployment of Automated Facial Recognition Authentication for the state licensing service.
As stated earlier, the information regarding the users will be stored in the databases which will be accessible by some of the State Government personnel. The deliberate attacks on this information to manipulate or access the user details may lead to the violation of privacy. It is also possible that the data acquired from these databases is then misused for larger purposes. For instance, the governments may misuse the data for their political aims and objectives.
In terms of the inaccurate results shown by the technology, the users will be deprived of their right to access the state services. Also, the data of the users may be exposed once such instances are reported. If the database includes the face map of the user along with the personal information of the users, such as their name, address, etc. then there will be massive privacy violation that may occur (Sutton, 2019). The personal information of the users may be put at stake which will be a major drawback for the State Government and all the entities involved.
One of the ways in which it can be avoided is to adopt the intelligent ways of storing the information. The Automated Facial Recognition Authentication database that will have the face map of the users recorded for the purpose of matching must not have any personal information of the user stored along with it. This may ensure that the ethics and privacy aspects of the technology and the user can be preserved.

Risks, Benefits, and Ethical Implications – State Police
Set of Risks
There is a major risk of law enforcement that may be associated with the technology. There is still no defined law or legislative policy that is in place for the use of facial recognition technology. With the use of the technology by the State Police and Government for the licensing services, the database will expand as there are a majority of the people that will require this service at some or the other point. In the case of the occurrence of privacy violations, inaccuracy of the technology, or the bias based on the information recorded of the users, the accountability of these issues will be on the state departments and government (Martin, 2019).

These entities will be answerable to the people and the other stakeholders involved. The probability of inaccuracy of the results is high and there can be privacy violations that may also occur with the insider threats or integrity violations. The State Police may not be able to manage and handle all of these aspects.

Potential Benefits

• If the technology is designed to be accurate and it provides the accurate outcomes, the burden on the members of the staff will reduce. There will be automated checks and maps for the users which will bring down the load on the staff members and they may be utilized in a better manner(Pushilal et al., 2018).
• The overall processing speed and operational speed will improve with the automation of the authentication process. This will have positive outcomes on the overall productivity and efficiency levels of the department.

Ethical Implications
The database associated with Automated Facial Recognition Authentication may be accessible by a few State Police personnel. There may be external entities that may get in touch with the police personnel with access to the database. The government officials, ruling parties, and opposition political parties may wish to get their specific objectives achieved. The insider threats may take place if the personnel agree to transfer the information to any of these entities without the due permission to do so. In such cases, the ethical implications of the technology and its use will be extremely negative.

The inaccurate results shown by the technology can also have negative ethical implications. The bias of the technology due to the differentiation on the basis of the user color or gender can lead to the disturbances of social harmony. The State Police will have to bear all of this and will be answerable. Also, the social and political unrest will also be required to be managed properly to avoid any further damage (Varley-Winter, 2020).

Legal and Ethical Implications
Data privacy has been recognized as one of the most significant aspects towards security. There are legislations and policies that have been developed to make sure that the privacy of the data is protected at all times.

Privacy Act is one of the significant legislations that are followed. It has been developed to protect the user and data privacy. The violation of the Privacy Act can be a possibility with the installation of Automated Facial Recognition Authentication. This is because the user details will be available in the database. The users will be able to access the state licensing services and the ones in which Automated Facial Recognition Authentication will be deployed later on only when the facial recognition stage is cleared. The manipulations with the database comprising of these details or the insider threats on the databases can be possible and are highly probable as well. In such cases, the user private details will be put at stake and these may also be used in an inappropriate manner. It may result in the capturing of the personal details, bias on the basis of these details, and further unauthorized sharing of information. Any of these can have negative implications on the users accessing the state services(Farsi & Komari Alaei, 2015).

The implementation of AFRA is planned for the state services and the licensing services are the initial one to begin with. There are several street services which may also make use of Automated Facial Recognition Authentication for the purpose of authentication. For example, the user may be provided with the access to the borders of different cities and states as per the information recorded by the technology and the map determined. In such cases, the Privacy Act, if not complied with can possess serious risks to the State and the Federal Governments and the National Security as well. It is possible that the databases are forged to provide the unauthorized access which may lethal.

There are ethical theories that are also used to determine the implications of the technology. The Deontology Ethics is the theory that makes use of the rules and protocols to understand if the act is ethical or not. In this case, it is probable that the legislations, such as the Privacy Act are violated upon the implementation of Automated Facial Recognition Authentication. As a result, the theory does not consider the implementation of AFRA as ethical.

There are also other ethical theories which are determined. Virtue Ethics also does not determine the act as ethical. This is because there are insider threats that may be executed and these may be based on the selfish virtues of the personnel involved. All of these can lead to negative ethical implications as well(Harding, 2019).

Conclusion
There are risks and benefits that are determined for the users and the State Government in association with the Automated Facial Recognition Authentication. The risks are primarily determined in the categories as inaccurate results with the use of the technology, bias due to the inaccuracy of the results, privacy & security violations, and insider threats. These risks can also have significant impacts on the State Government and the departments involved. There are also benefits that are determined with the technology, such as the faster processing of the steps, inability to forge the information, and likewise. It is necessary that the storage of the information in the database is intelligently done to avoid negative ethical, privacy, and legal implications. The personal details of the users shall not be included in the databases which may be easily misused. The technological implementation and the methodology selected will also have the significant impacts on the overall outcomes associated with the technology.

References
Farsi, H., & Komari Alaei, H. (2015). A Novel Algorithm to Tackle Eyeglasses and Beard Issues in Facial IR Recognition. Automated Facial Recognition AuthenticationELCVIA Electronic Letters on Computer Vision and Image Analysis, 14(1). https://doi.org/10.5565/rev/elcvia.696

Harding, D. (2019, July 22). Facial Recognition: When Convenience and Privacy Collide. Securitymagazine.Com; Security Magazine. https://www.securitymagazine.com/articles/90533-facial-recognition-when-convenience-and-privacy-collide

Khan, J. K., & Upadhyay, D. (2015). Security issues in face recognition. 2014 5th International Conference - Confluence The Next Generation Information Technology Summit (Confluence). https://doi.org/10.1109/confluence.2014.6949341

Martin, N. (2019). The Major Concerns Around Facial Recognition Technology. Forbes. https://www.forbes.com/sites/nicolemartin1/2019/09/25/the-major-concerns-around-facial-recognition-technology/#767071204fe3

Noden, A. (2018, May 17). Automated facial recognition technology wrong in 98% of cases, says new report. Government Europa. https://www.governmenteuropa.eu/automated-facial-recognition-technology-report/87498/

Patel, G. D. (2018). Various Approaches for Face Recognition and Detection: A Survey. International Journal for Research in Applied Science and Engineering Technology, 6(3), 3330–3337. https://doi.org/10.22214/ijraset.2018.3707

Pushilal, A., Chakraborty, S., Singhania, R., & Mahalakshmi, P. (2018).

Implementation of Facial Recognition for Home Security Systems. International Journal of Engineering & Technology, 7(4.10), 55. https://doi.org/10.14419/ijet.v7i4.10.20706 Simonite, T. (2019, July 22). The Best Algorithms Still Struggle to Recognize Black Faces. Wired; WIRED.

https://www.wired.com/story/best-algorithms-struggle-recognize-black-faces-equally/ Sutton, H. (2019). Report finds department abused facial recognition software. Campus Security Report, 16(4), 9–9. https://doi.org/10.1002/casr.30546

Varley-Winter, O. (2020). The overlooked governance issues raised by facial recognition. Automated Facial Recognition AuthenticationBiometric Technology Today, 2020(5), 5–8. https://doi.org/10.1016/s0969-4765(20)30061-8