Applications for facial recognition technology can be found in a number of sectors, including security, retail, and healthcare. Our phones and PCs can now be unlocked with just our faces. While it is fascinating to give machines the very human capacity to recognise someone by taking a cursory look at their face, there are some serious ethical issues that must be carefully considered.
How Does Facial Recognition Work?
The definition of “facial recognition” is quite obvious. The system uses computer vision techniques to map, examine, and verify a face’s identification in a picture or a video. The facial recognition process can be reduced to the following three steps, notwithstanding the fact that each solution (which frequently uses proprietary algorithms) works differently:
Detection: The process of finding a face in an input image is referred to as detection. Therefore, a bounding box is formed around each face. The facial recognition algorithms must first be trained to understand how a face appears on various data entries before this stage can be finished.
Analysis: Analyzing involves listing the features of each face. This is accomplished by calculating the distances between the eyes, nose, and mouse as well as figuring out how the chin is shaped. The so-called faceprint is created by combining those measurements and turning them into a singular set of integers.
Recognition: The act of actually identifying a person in the input photo is referred to as recognition. In certain applications, categorisation takes the role of this step. In these situations, the algorithms designate a person as belonging to one of the separate groups, such as by gender or age, without verifying the person’s identity.
The Benefits of Facial Recognition Technology
Facial recognition technology has the potential to bring numerous benefits, including:
Improved security measures in public spaces
Facial recognition software can help to identify and locate missing persons or criminal suspects, increasing the chances of successful investigations and arrests. The technology can also be used to screen individuals entering public buildings or events, helping to prevent potential threats or security breaches.
Enhanced ability to identify and locate missing persons or criminal suspects
By quickly identifying individuals who may be involved in criminal activities, law enforcement agencies can increase the chances of successful investigations and arrests. The technology can also be used to locate missing persons, potentially helping to reunite families and reduce the burden on law enforcement resources.
Streamlined processes in industries such as retail and healthcare
Facial recognition technology can be used to verify the identity of customers or patients, reducing the need for manual verification processes and improving efficiency. This can lead to faster and more convenient experiences for customers or patients, as well as freeing up staff time for other tasks.
The technology can be used to unlock devices or make payments, providing users with a quick and easy way to access their devices or make transactions. This can save time and effort for users, as well as increasing security by reducing the risk of unauthorized access.
Improved customer experiences
In the retail industry, facial recognition software can be used to personalize shopping experiences, helping businesses to better understand and meet the needs of their customers. For example, the technology can be used to identify returning customers and recommend products based on their past purchases or preferences. This can improve the overall shopping experience and increase customer loyalty.
The Ethical Concerns With Facial Recognition Technology
Lack of informed consent and transparency
Any sort of data mining raises privacy concerns, particularly online where most data is anonymised. Large datasets of photographs, ideally taken repeatedly under various lighting conditions and angles, are ideal for testing and training facial recognition systems. A person’s face can be used to identify them, which increases the possibility of access to a wide range of additional data and raises ethical questions.
Online services, particularly public Flickr photographs submitted under copyright licences that permit liberal reuse and occasionally shady social media networks, are the main sources of images.
Researchers at the Washington-based The largest dataset in the world, MSCeleb5, was compiled by Microsoft Research and contains around 10 million photos of 100,000 people, including musicians, journalists, and academics.
Concerns about privacy in relation to facial recognition centres on data storage procedures that potentially expose facial recognition information and other possible security risks. The majority of businesses still save their facial data on local computers, which creates security holes and a shortage of IT security experts to guarantee network security.
When data is stored in the cloud, facial recognition technology can guarantee the highest level of security. However, good encryption is the only way to ensure data integrity. In order to store data properly and provide consumers authority over their own data, which will increase accountability and stop harmful traffic, IT cybersecurity specialists must be deployed.
On the plus side, given the option to disable or not use the function, consumer devices equipped with facial recognition technologies are less contentious. However, because of the degradation of privacy, consumer goods firms continue to be targets of prohibitions. Yet, they keep selling goods using facial technology by positioning them as cutting-edge security features. Devices that enable a victim to pursue monetary restitution for the privacy violation can be used to support the decision to pursue legal action. For instance, the social networking company Facebook paid $650 million to resolve a class-action lawsuit in Illinois involving the collection of private images for facial recognition.
Although worries about mass surveillance may seem like an exaggerated conspiracy theory, they have actually quite dramatically affected Americans. Large internet companies are worried that they may receive subpoenas to identify individuals who have visited abortion clinics as a result of the elimination of federal abortion protections.
Massive public movement logging may initially appear appropriate, but it may not be, as the example of the abortion clinic shows. A key component of most democracies is the public’s freedom to congregate and freely voice support or opposition to current concerns. The use of mass monitoring and facial recognition to track suspected “bad behaviour” against civilians is a possibility. A few aspects of this ethical concern have already materialised.
Bias and accuracy concerns
The existence of racial bias in the algorithms is a frequently voiced ethical problem with facial recognition technology. This fear, however, is a reflection of a deeper worry about the general accuracy of facial recognition technologies. Demographic classification algorithms that aim to utilise a face to predict attributes like ethnicity, gender, and age are frequently cited as the source of alleged proof of bias. Darker-skinned women were misclassified more often than any other category, according to a 2018 MIT study.
Algorithms can be biassed by a variety of things, such as the age of a photo compared to the face of a person or the difficulty of distinguishing between identical twins or doppelgängers. If you’re trying to identify your favourite customers, this might not seem like a big deal, but if law enforcement judgements are based on facial recognition technology, it’s a serious worry.
These are just a few examples of the ethical issues that have been raised in relation to facial recognition technology.
- In 2019, it was reported that Amazon’s facial recognition tool, “Rekognition,” had falsely matched 28 members of Congress with mugshots in a test run by the American Civil Liberties Union (ACLU). This raised concerns about the potential for biased and inaccurate results, particularly when used by law enforcement.
- In 2018, a study conducted by the Massachusetts Institute of Technology (MIT) found that facial recognition algorithms were significantly less accurate at identifying people of color, particularly women. This raises concerns about the potential for facial recognition technology to perpetuate and amplify existing biases.
- In 2017, the city of San Francisco banned the use of facial recognition technology by city agencies due to concerns about civil liberties and the potential for abuse.
- In 2018, it was revealed that the Chinese government was using facial recognition technology as part of its “social credit” system, which rates and rewards or punishes citizens based on their behavior. This raised concerns about the potential for the technology to be used for mass surveillance and control.
It’s important for developers, users, and policymakers to consider the potential impacts of this technology on privacy, civil liberties, and other ethical concerns.
Possible Solutions to These Ethical Issues
To address the ethical concerns surrounding facial recognition software, there are a few potential solutions that could be implemented.
One is the regulation and oversight of the technology by government bodies. This could include the development of clear guidelines for the use of the technology, as well as independent oversight to ensure that it is being used ethically and responsibly.
Another solution would be the implementation of ethical guidelines by companies using the technology. This could involve creating internal policies on the use of the software and seeking input from stakeholders, such as civil liberties organizations and community groups.
Finally, increased transparency and accountability in the use of facial recognition software is crucial. This could include requirements for companies to disclose how they are using the technology and to make their algorithms and data sets available for independent review. It could also involve the creation of mechanisms for individuals to challenge incorrect identifications or misuse of their data.
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