Which type of harm can facial recognition technology cause?

Facial recognition technology can indeed lead to unreliable results, particularly for marginalized groups. This issue arises due to biases inherent in the datasets used to train these systems. Often, these datasets may not be representative of the diversity found in the general population, leading to greater inaccuracies for individuals from underrepresented racial, ethnic, or gender groups. As a result, these technologies can face challenges in accurately recognizing faces that do not fit the predominant features represented in the training data, resulting in higher rates of false positives or negatives for these groups.

The potential harms include discrimination, unjust profiling, and invasion of privacy, which disproportionally affect marginalized communities. These risks highlight the importance of responsible implementation and the necessity for rigorous testing across diverse populations to ensure fairness and equity in the deployment of facial recognition technologies. Addressing these issues is critical to preventing further marginalization and ensuring that technology serves all parts of society equitably.