Description: Data gaps refer to the absence of necessary information that can lead to biased decisions and outcomes in the field of artificial intelligence (AI). These gaps can arise for various reasons, such as the lack of representation of certain demographic groups in datasets, the omission of relevant variables, or the biased collection of data. As a result, AI models can perpetuate or even amplify existing inequalities, raising serious ethical concerns. Identifying and correcting these gaps is crucial for developing fair and equitable AI systems. Ethics in AI demands that developers be aware of the limitations of the data they use and how these limitations can influence outcomes. Therefore, addressing data gaps is not only a technical challenge but also a moral imperative aimed at ensuring that technology benefits all sectors of society equitably.
