Description: Non-representative data refers to datasets that do not accurately reflect the diversity and characteristics of the population they are supposed to represent. This phenomenon is critical in the field of artificial intelligence (AI), as models trained on biased data can perpetuate and amplify existing inequalities. Lack of representativeness can arise from various sources, such as biased sample selection, exclusion of specific demographic groups, or data collection in limited contexts. As a result, AI models may make erroneous or unfair decisions, disproportionately affecting individuals and communities. Ethics in AI demands that developers and data scientists be aware of the composition of their datasets and how this can influence outcomes. Representativeness is essential to ensure that AI applications are fair, equitable, and effective, thus avoiding the perpetuation of stereotypes and discrimination. In summary, non-representative data is a significant challenge in the development of responsible and ethical AI technologies, and identifying and correcting it are crucial steps to improve the quality and fairness of AI systems.
