Ethical Use of Facial Recognition: Balancing Technology and Privacy

The Different Types of Facial Recognition 

Facial recognition technology, which has evolved significantly since its inception in the 1960s, is now prevalent in various applications due to recent technological advancements. While many people are familiar with facial recognition for unlocking mobile phones, this technology is also widely used in law enforcement and commercial settings to enhance security and efficiency. In law enforcement, facial recognition helps identify suspects, victims, and missing persons by comparing images from crime scenes or CCTV footage against extensive databases. This application aids in crime suspect identification, tracking at-large criminals, and finding missing individuals. In commercial environments, facial recognition is employed to determine if an individual is on a pre-determined watchlist, which might include felons, shoplifters, employees, or VIPs. This technology supports touchless access control, watchlist screening, VIP alerting, and time and attendance reporting. The scope of databases used in these applications varies, with law enforcement often utilizing massive collections of images, whereas commercial uses typically involve more targeted and limited datasets. 

Addressing Ethical and Legal Concerns 

Privacy and Informed Consent 

One of the foremost ethical issues is privacy. The GDPR underscores the importance of protecting personal data, requiring explicit consent from individuals before their data is collected or used. Article 7 of the GDPR states that consent must be “freely given, specific, informed, and unambiguous.”  There is a need for clear policies that align with these requirements, ensuring individuals understand how their data is collected and used. 

Data Minimization and Purpose Limitation 

GDPR principles also include data minimization and purpose limitation. According to Article 5 of the GDPR, personal data must be “collected for specified, explicit and legitimate purposes” and “adequate, relevant and limited to what is necessary.”  So, facial recognition systems must be used to collect only the data necessary for their intended purpose and to avoid overreach. 

In law enforcement there are exemptions. It is generally forbidden to use biometric identification systems except in very specific and narrowly defined circumstances. “Real-time” RBI can only be utilized if stringent safeguards are in place, such as being restricted in duration and geographic area, and requiring prior judicial or administrative approval. Examples of permitted uses include targeted searches for missing persons or preventing terrorist attacks. Using these systems after an event (“post-remote RBI”) is considered high-risk and also requires judicial authorization, specifically in connection with a criminal offense. 

Mitigating Bias and Ensuring Fairness 

Bias in facial recognition systems is another critical concern. It is important to use diverse datasets to train algorithms, which reduces the risk of bias and increases accuracy across different demographic groups. This approach is essential to prevent discrimination and ensure that the technology is fair and equitable for all users. 

Ensuring Ethical Use of Facial Recognition Technology 

With around 700 million video cameras in operation globally, privacy and ethical considerations are paramount in the use of facial recognition technology. The following are some considerations to ensure that it is implemented according to the requirements. 

Ethical facial recognition systems must prioritize user consent and privacy. Data should not be collected or shared without explicit permission, and watchlists must be user-created without using third-party sources like social media. Data encryption and the storage of mathematical vectors instead of images are essential for secure data handling. Organizations should also provide clear notice to individuals whose images are analyzed and obtain their consent, ensuring compliance with regulations like GDPR and other local laws. 

Addressing bias is crucial for enhancing the accuracy of facial recognition technology. Utilizing diverse and extensive datasets that include various demographics can significantly reduce bias. An internal ethics review board is necessary to evaluate potential uses of the technology, ensuring ethical deployment, particularly in sensitive areas such as law enforcement and government applications. Additionally, watchlists should be created from scratch based on specific needs, using high-quality images and anonymized identifiers to improve accuracy while protecting privacy. 

Advanced privacy options and robust performance under real-world conditions are vital. Features like face blurring and discarding non-watchlist detections protect bystanders’ identities, while the technology should excel in challenging environments like low light or large crowds using advanced algorithms. Object recognition based on clothing or body characteristics can aid in investigations without using facial features, maintaining privacy. Clearly defined ethical usage policies, customizable user roles, and permissions help prevent misuse, ensuring the technology is used lawfully and ethically. 

Setting the Right Thresholds Based on the Use Case 

In facial recognition systems, the threshold is the score at which the system determines whether a match is valid. Configuring this threshold is crucial for balancing accuracy and minimizing false positives or negatives. A low threshold increases the chances of detecting matches but can lead to more false positives, making it suitable for high-stakes environments where missing a detection is more critical. Conversely, a high threshold reduces false positives and enhances accuracy but risks missing valid identifications. This approach is ideal for large databases or one-to-one scenarios where precise matching is essential. In lower-risk applications, a higher threshold can maintain accuracy while optimizing acceptance rates. 

The distinction between working with facial IDs and thresholds lies in the system’s methodology. Facial IDs involve generating unique identifiers for individuals in the database, which are used to compare new images against stored ones. Setting the appropriate threshold involves understanding the trade-off between sensitivity and specificity, ensuring the system’s performance aligns with the application’s specific requirements. Whether the goal is to maximize security, ensure precise identifications, or optimize user convenience, adjusting the threshold is key to achieving the desired balance between detection and accuracy. 

Prioritizing Privacy and Human Judgment in Facial Recognition 

Ethical Use of facial recognition is essential to respect individuals’ rights, including their right to privacy, and ensure that autonomous decisions are not made without human oversight. In public safety and law enforcement, governments should work with authorities to develop acceptable use policies that protect citizens’ rights while enabling security measures. 

In any situation, facial recognition should be used to support rather than replace human judgment. For example, in law enforcement, facial recognition algorithms can quickly generate a set of possible matches for further human analysis, but the final decision should always be made by a person. This ensures that the technology serves as a tool to reduce potential outcomes, rather than making definitive autonomous decisions. 

Moreover, transparency and user consent are paramount. Organizations must clearly communicate how facial recognition data is collected, used, and protected. This transparency builds public trust and ensures compliance with legal standards. 

In a nutshell 

Facial recognition technology holds immense potential to enhance security and convenience across various sectors, but its ethical deployment is crucial. By adhering to principles of transparency, informed consent, and continuous improvement, organizations can ensure the responsible use of this technology. Properly configuring thresholds based on specific use cases, employing diverse datasets to mitigate bias, and implementing robust data privacy measures are essential steps.