The Watchful Eye: Bias and Privacy in Facial Recognition Technology

Facial recognition isn’t just about accuracy—it’s about who gets seen and who gets left out. These systems learn from data, and if that data doesn’t reflect real-world diversity, the results will reflect bias. Studies show these tools perform far worse on darker skin tones, often misidentifying people from marginalized groups. That means more false matches, more accusations, and more harm—especially for communities already under scrutiny. The problem isn’t just technical; it’s built into how the systems are trained, using datasets that often favor light-skinned faces from specific regions. When algorithms make mistakes, they don’t just fail—they can lead to real consequences

The idea of constantly scanning people—like tracking migrants or monitoring public spaces—takes surveillance to a new level. People aren’t just being watched; they’re being tracked through their daily movements, without consent or a clear reason. That kind of monitoring chips away at privacy and freedom. The data generated would be massive and hard to secure, making breaches a real risk. Worse, without oversight, authorities could use it to target certain groups or suppress dissent. This isn’t science fiction—it’s already happening in some places.

Key Concerns in Facial Recognition Systems

• Accuracy disparities: Systems are significantly less accurate when identifying people with darker skin tones, especially in underrepresented groups. This isn’t just a glitch—it’s a design flaw rooted in biased training data.
• Mass surveillance risks: Continuous, unconsented tracking raises alarms about civil liberties, freedom of movement, and potential misuse by authorities. People are not just observed—they’re monitored, often without knowing it.
• Accountability gaps: When errors happen—like misidentifying someone—there’s no clear process for redress or responsibility. The systems aren’t transparent, and consequences are rarely addressed fairly.
• Lack of transparency: Developers rarely share what data they use or how their systems perform across different groups. This secrecy makes it hard to trust or regulate the technology.
• Need for regulation: Clear rules are needed to limit where and how facial recognition is used, especially in public spaces or sensitive settings. Rules should protect privacy and prevent harm to vulnerable communities.

We can’t accept technology that treats people differently just because of the color of their skin. Without serious changes in how these tools are built, tested, and used, we risk creating systems that don’t just fail—they actively harm.