What are the ethical principles of facial recognition technology?

The huge potential of facial recognition technology in various fields is almost unimaginable. However, certain common pitfalls in its functionality and some ethical considerations need to be addressed before its most complex applications can be implemented.

An accurate facial recognition system uses biometric technology to map facial features from photos or videos. It compares the information to a database of known faces to find a match. Facial recognition can help verify a person's identity, but it also raises privacy concerns.

Decades ago, we could not have predicted that facial recognition would become an almost integral part of our lives in the future. From unlocking smartphones to conducting online or offline transactions, this technology is deeply ingrained in our daily lives today.

A facial recognition system is an application of the computer vision and machine learning components of artificial intelligence. It works like this: an algorithm is trained to determine various different details of a person's face, such as the space between their eyes. Pixel count or curvature, and other details that are logically interpreted to reconstruct the face in the system. This recreation is then compared to a large number of faces stored in the system database. For example, if the algorithm detects a match to a face present in the database, the system "recognizes" it and performs the user's task.

In addition to completing the entire process in seconds, today's facial recognition systems are capable of working even in poor lighting, image resolution, and viewing angles. Like other artificial intelligence technologies, facial recognition systems need to follow some ethical principles when used for various purposes.

These regulations include:

1. Fairness in facial recognition

First, the development of facial recognition devices must completely prevent, or at least minimize , gender, facial features, deformity or other bias against any person or group. There is now ample evidence that facial recognition systems are unlikely to be 100% fair in their operation. As a result, companies building systems that support this technology often spend hundreds of hours removing all traces of bias found in their systems.

Reputable companies like Microsoft often hire qualified experts from as many ethnic communities as possible. During the research, development, testing and design phases of their facial recognition systems, diversity allowed them to create massive data sets to train AI data models. While large data sets reduce bias, diversity is also symbolic. Selecting individuals from around the world helps reflect the diversity found in the real world.

To eliminate bias from facial recognition systems, companies must put in extra effort. To achieve this, the datasets used for machine learning and labeling must be diverse. Most importantly, the output quality of a fair facial recognition system will be very high as it will work seamlessly anywhere in the world without any element of bias.

To ensure the fairness of facial recognition systems, developers can also involve end customers during the beta testing phase. The ability to test such a system in real-world scenarios will only improve the quality of its functionality.

2. Openness about the inner workings of AI

Businesses using facial recognition systems in the workplace and in cybersecurity systems need to know all the details about where the machine learning information is stored. Such businesses need to understand the limitations and capabilities of the technology before implementing it in their daily operations. Companies providing AI technology must be fully transparent with customers about these details. Additionally, service providers must ensure that customers can use their facial recognition systems anywhere. Any updates in the system must be validly approved by the customer before proceeding.

3. Corporate Responsibility Issues

To sum up, face recognition systems are deployed in many fields. Companies that manufacture such systems must be held accountable, especially where the technology has the potential to directly impact law enforcement and surveillance by any person or group. Accountability in such systems means including use cases to prevent physical or health-based harm, financial misappropriation, or other problems that may arise from the system. To introduce an element of control into the process, a qualified individual takes charge of the systems in the business to make measured and logical decisions. Beyond that, businesses that incorporate facial recognition systems into their daily operations must immediately address customer dissatisfaction with the technology.

4. Consent and notification before monitoring

Under normal circumstances, facial recognition systems shall not be used to spy on individuals, groups or other behaviors without the consent of the individual or group. Some institutions, such as the European Union, have a standardized set of laws to prevent unauthorized businesses from spying on individuals within the governing body's jurisdiction. Businesses with such systems must comply with all U.S. data protection and privacy laws.

5. Legal surveillance to avoid human rights violations

Companies cannot use facial recognition systems for surveillance unless authorized by the national government or decisive regulatory agency for purposes related to national security or other important situations. any person or group. Basically, this technology is strictly prohibited from being used to violate the human rights and freedoms of victims.

Although programmed to follow these regulations without exception, facial recognition systems can cause problems due to operational errors.

Some of the major issues related to this technology are:

1. Verification errors at the time of purchase

As mentioned above, facial recognition systems are incorporated into digital payment applications to facilitate users Transactions can be verified using this technology. Due to the existence of this technology, criminal activities such as facial identity theft and debit card fraud are very possible. Customers choose facial recognition systems because of the great convenience it provides users. Despite the security protocols in place in facial recognition systems, face copying can lead to the misappropriation of funds.

2. Inaccuracy in Law Enforcement Applications

Facial recognition systems are used to identify public criminals before they are captured. While the technology as a concept is undoubtedly useful in law enforcement, there are some obvious problems with its working. Criminals can abuse this technology in a number of ways. For example, the concept of biased AI provides inaccurate results for law enforcement officers because the systems sometimes fail to distinguish between people of color. Typically, such systems are trained on datasets containing images of white men. So the way the system works is wrong when it comes to identifying people from other races.

There are several examples where businesses or public institutions have been accused of using advanced facial recognition systems to illegally spy on civilians. Video data collected through continuous surveillance of individuals can be used for a variety of nefarious purposes. One of the biggest drawbacks of facial recognition systems is that the output they provide is too general.

For example, if a person is suspected of committing a felony, their photo will be taken and run along with several photos of the criminal to check if the person has any criminal record. However, stacking this data together means that the facial recognition database will retain photos of the man and experienced felons. So, despite the individual's innocence, his or her privacy has been violated. Second, this person may be perceived as a bad person even though he is innocent by all accounts.

We can see that the main problems and errors related to facial recognition technology stem from the lack of advancement in technology, the lack of diversity in data sets, and the inefficient handling of the system by enterprises. In my opinion, the scope of AI and its applications in real-world needs is unlimited, and the risks of face recognition technology usually occur when the technology works differently from actual needs.

With the further development of technology in the future, technology-related problems will be solved. Issues related to bias in AI algorithms will eventually be resolved. However, in order for the technology to work flawlessly without violating any ethical norms, companies must maintain a strict level of governance over such systems. With greater governance, facial recognition system bugs could be addressed in the future. Therefore, the research, development, and design of such systems must be improved to achieve positive solutions.

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