The Impact of Bias and Fairness Issues on the Robustness and Security of AI Systems

EasyChair Preprint 13461

Abstract

Artificial Intelligence (AI) systems have witnessed significant advancements and widespread adoption across various domains, revolutionizing industries and transforming the way we interact with technology. However, the increasing reliance on AI systems has unveiled critical challenges related to bias and fairness, which can have profound implications for the robustness and security of these systems. This abstract explores the impact of bias and fairness issues on AI system reliability, resilience, and vulnerability to security threats.

Bias in AI systems can arise from various sources, including biased training data, algorithmic design, or human biases encoded in decision-making processes. Such biases can lead to unfair outcomes, perpetuate social inequalities, and discriminate against certain individuals or groups. Moreover, biased AI systems may exhibit reduced accuracy, fail to generalize to diverse populations, and undermine user trust. These issues not only compromise the ethical integrity of AI systems but also pose significant risks to their robustness and security.

Fairness concerns in AI systems are closely linked to bias, as fairness aims to ensure equitable treatment and protection against discriminatory outcomes. Unfair AI systems can result in negative consequences, such as biased hiring practices, discriminatory loan approvals, or prejudiced law enforcement decisions. When fairness is compromised, AI systems become more susceptible to adversarial attacks and exploitation. Malicious actors can manipulate biased AI systems to bypass security measures, deceive algorithms, or exploit vulnerabilities, leading to significant privacy breaches, data manipulations, or even physical harm.

Keyphrases: Artificial Intelligence, Security, bias, ethical concerns, fairness, robustness

@booklet{EasyChair:13461,
  author    = {Godwin Olaoye and Edwin Frank},
  title     = {The Impact of Bias and Fairness Issues on the Robustness and Security of AI Systems},
  howpublished = {EasyChair Preprint 13461},
  year      = {EasyChair, 2024}}