Algorithmic bias has become a significant issue in the fast- growing field of artificial intelligence (AI) and machine learning. Even when it is unintentional, algorithmic bias can appear in several ways, resulting in discriminatory outcomes that unjustly disadvantage particular individuals and groups. Understanding the distinction between avoidable and unavoidable algorithmic bias will become increasingly important for policymakers, developers and consumers as they navigate the evolving relationship between technology and ethical norms. Interestingly, studies suggest that people are currently less troubled by algorithmic bias than by human bias, but this attitude may change over time. Avoidable algorithmic biases are tendencies that can be reduced through meticulous system design, data analysis and inclusive development. Avoidable biases can be caused by skewed data sets, faulty algorithmic design and negligence in the development process. Addressing these biases requires a proactive strategy that emphasizes diversity, openness and continual oversight in deploying AI systems. Unavoidable algorithmic bias, in contrast, refers to bias that is difficult to eliminate due to conflicting fairness principles, the complexity of the data or the limitations of current AI technology. These biases result in complex ethical and practical difficulties, requiring a careful equilibrium between conflicting interests and acknowledging that a certain degree of bias may endure despite earnest attempts. Addressing algorithmic bias, whether avoidable or unavoidable, is both a technical problem and a societal obligation. Policymakers, engineers, ethicists and society must work together to ensure that AI systems possess intelligence, efficiency and fairness. At this pivotal moment in technological advancement, our policy decisions can shape the long-term impact of AI on our societies.

4 p.

9789280891522