Historical Bias
The skewed representation or outcomes in AI systems due to flawed or prejudiced data reflecting past societal norms or inequalities.
Historical bias in AI occurs when models are trained on data sets that carry forward societal prejudices or imbalances from the past, which can lead to unfair or discriminatory outcomes. It is significant because it can perpetuate systemic inequalities, such as racial or gender biases, in AI applications even when the algorithms themselves are neutral. In practical terms, historical bias can manifest in various domains like hiring algorithms that favor certain demographic groups due to biased historical hiring data. Understanding and mitigating historical bias is essential in advancing ethical AI practices—developers and researchers must critically examine training data and adjust algorithms to account for and correct inherent biases.
The term historical bias has been in use since the early 2010s as AI researchers began recognizing the importance of training data quality. However, it gained more widespread attention in the mid-2010s as AI systems became more integral to decision-making processes across different sectors, prompting a closer examination of data ethics.
Key contributors to the development of the concept include AI ethicists and researchers like Kate Crawford and Joy Buolamwini, whose work emphasized the implications and challenges of biased data in AI systems. Their contributions have been pivotal in advocating for more equitable AI development and in establishing frameworks for detecting and addressing historical bias.