Artificial intelligence (AI) holds vast potential for societal and industrial transformation. However, ensuring AI systems are safe, fair, inclusive, and trustworthy depends on the quality and integrity of the data upon which they are built. Biased datasets can produce AI models that perpetuate harmful stereotypes, discriminate against specific groups, and yield inaccurate or unreliable results. This article explores the complexities of data bias, outlines practical mitigation strategies, and delves into the importance of building inclusive datasets for the training and testing of AI models [1].
Understanding the Complexities of Data Bias
Data plays a key role in the development of AI models. Data bias can infiltrate AI systems in various ways. Here's a breakdown of the primary types of data bias, along with real-world examples [1,2]: