Description: Algorithmic bias refers to systematic and repeatable errors in a computer system that generate unfair results. This phenomenon occurs when algorithms, which are sets of rules and procedures for solving problems, reflect human prejudices or limitations in the data used to train them. In the context of artificial intelligence and machine learning, algorithmic bias can arise at various stages, from data preprocessing to the implementation of algorithms. A lack of diversity in datasets, inadequate feature selection, and misinterpretation of results are some of the causes that can contribute to this bias. Explainable artificial intelligence seeks to address these issues by providing transparency in how algorithms make decisions. The relevance of algorithmic bias is critical, as it can perpetuate social inequalities and affect trust in emerging technologies. Therefore, it is essential for developers and data scientists to be aware of these biases and actively work to mitigate them, ensuring that systems are fair and equitable for all users.
History: The concept of algorithmic bias has gained attention in the last decade, especially with the rise of artificial intelligence and machine learning. While bias in human decision-making has been a topic of study for a long time, the formalization of algorithmic bias began to be discussed in the context of technology starting in the 2000s. In 2016, the term became even more popular following the publication of several studies demonstrating how algorithms can perpetuate racial and gender stereotypes. The growing concern for ethics in artificial intelligence has led to a more rigorous focus on identifying and mitigating algorithmic bias in recent years.
Uses: Algorithmic bias is present in various applications, from recommendation systems to hiring algorithms. In advertising, algorithms may display biased ads based on demographic data, which can affect fairness in access to opportunities. In the criminal justice sector, crime prediction algorithms can perpetuate biases if trained on historical data that reflects inequalities. Therefore, it is crucial to address algorithmic bias to ensure that technologies are fair and equitable.
Examples: A notable example of algorithmic bias occurred in 2018 when Amazon’s facial recognition algorithm, Rekognition, showed significantly higher error rates in identifying women and people of color compared to white men. Another case is Amazon’s hiring algorithm, which was discarded because it showed bias against women, as it was trained on resumes predominantly from men. These examples underscore the importance of addressing algorithmic bias in technology development.
