Description: Bias in recommendations made by artificial intelligence (AI) systems refers to the tendency of these systems to favor certain outcomes or decisions based on data patterns that may not be representative or fair. This phenomenon can arise from various sources, such as the quality of training data, algorithm selection, and design choices made by developers. Bias can manifest in multiple forms, including racial, gender, or socioeconomic biases, which can lead to discriminatory or unfair outcomes. The presence of bias in recommendations is particularly concerning in critical contexts, such as hiring, credit granting, or content moderation on digital platforms, where automated decisions can significantly impact people’s lives. Therefore, it is essential to address and mitigate these biases to ensure that AI systems operate ethically and equitably, promoting justice and inclusion in automated decision-making.
