Toward Ethical Applications of Artificial Intelligence: Understanding Current Uses of Facial Recognition Technology and Advancing Bias Mitigation Strategies

Abstract

Facial recognition technology (FRT) is a biometric software-based tool that mathematically maps and analyzes an individual’s facial features for the purpose of making identifying conclusions from photographs and video. FRT is being implemented throughout society at a rapid rate as the tool offers significant economic benefits for identification processes and policing. In spite of FRT’s benefits its broadening implementation comes with significant risk to society, as the potential for misuse or identification errors and bias with FRT can lead to large-scale violation of individual’s civil and human rights. The key risks using FRT come from two sources: first, FRT uses curated facial datasets for training, it has been shown that labeling errors and lack of diverse facial demographics in the datasets leads to poorly trained and error-prone outcomes with regard to underrepresented groups. Second, there are only limited regulatory frameworks and ethical standards of use for FRT, leading to situations where FRT is either misused or extended beyond its practical utility, leading to violation of individual privacy and legal assemble rights and the perpetuation of cultural bias. The legal and ethical issues surrounding FRT have come under scrutiny in recent years following increased public awareness from mainstream media reports on the use of FRT in large-scale protest events and in law enforcement use cases. Currently, there are a few examples of state-level regulation and industry self-regulation through guiding ethical principles that restrict and monitor the use of FRT in both government and industry applications. These minimal and isolated forms of regulation leave tremendous gaps in the effective and ethical implementation of FRT, leaving ample room for unregulated and unethical use cases. This thesis primarily aims to advance promising bias mitigation strategies. The key recommendations made are: 1) education for users and increased engagement by stakeholders, 2) comprehensive guidelines that can lead to federal regulation, and 3) a push towards explainable AI. FRT regulation has become a controversial and increasingly challenging task; the time for urgency and regulation is now in order to put a halt to the negative consequences of the technology as it currently exists.