Explore the historical and societal implications of face recognition technology, including its biases and invasion of privacy. Learn about the functionality of this technology and its disproportionate targeting of certain individuals.
The Historical Context of Face Recognition Technology
( Credit to: Technical )
Face recognition technology has a long history of monitoring and controlling people's movements. From fingerprints to iris scans, various methods have been used to track individuals and determine their freedom of movement. In this section, we will explore the historical context of face recognition technology and its implications.
Face recognition technology has rapidly advanced in recent years, becoming the latest form of biometric surveillance. This technology carries a legacy of monitoring and controlling people's movements, dating back to the pseudoscience of physiognomy in the 18th century. Early applications included anthropometry, fingerprinting, and iris scans, all aimed at providing unique identifiers. The integration of photos into passports in the early 20th century further solidified the relationship between face recognition and border control.
The Functionality of Face Recognition Technology
Face recognition technology operates by capturing and analyzing unique data points from static photographs or video footage. These data points are then compared to a database of existing images to identify individuals. With improvements in accuracy and speed, this technology has become an effective means of surveillance.
The Assumption of Privacy vs. Security
Surveillance systems are built on the assumption that tracking and limiting people's movements enhance security at the expense of privacy. However, this trade-off disproportionately affects certain individuals who are targeted by face recognition technology. In this section, we will explore the societal implications of this assumption and its impact on privacy and security.
Biases and Disproportionate Targeting
Face recognition technology has been used globally for various purposes, often impacting marginalized communities. For example, it has been used for caste surveillance in India, monitoring Uyghurs in China, and attendance tracking in U.S. schools, disproportionately affecting low-income and predominantly Black communities. Additionally, studies have shown that face recognition technology misidentifies Black and Asian individuals at significantly higher rates than white individuals, perpetuating systemic racism. In this section, we will delve into the biases and disproportionate targeting associated with face recognition technology.
Governmental Control and Corporate Profit
Early research into face recognition software was funded by the CIA for border surveillance purposes. While initial attempts were limited in success, private companies have since adopted data harvesting techniques, including face recognition, for profit. This technology is now prevalent in various contexts, from unlocking smartphones to promotional store kiosks. In this section, we will explore the governmental control and corporate profit associated with face recognition technology.
Invasion of Privacy and Targeting
Advancements in face recognition technology have reduced false positives for certain individuals, but the invasion of privacy remains a concern for all. Governmental and private entities capitalize on this technology, paying particular attention to those who are perceived as high-risk based on societal biases and structural inequalities. In this section, we will discuss the invasion of privacy and targeting associated with face recognition technology.
Conclusion
The rise of face recognition technology is rooted in a historical context of surveillance and control. Despite improvements in accuracy, biases persist, disproportionately affecting marginalized communities. The invasion of privacy is a widespread concern, as this technology infiltrates various aspects of our lives. It is crucial to critically examine the ethical and societal implications of face recognition technology and advocate for transparency and accountability in its use.