Description: Fractured data refers to incomplete or fragmented datasets that can lead to biased outcomes in artificial intelligence (AI). This phenomenon occurs when the information used to train AI models is not representative of reality, resulting in erroneous or discriminatory decisions. Fractured data can arise from various sources, such as lack of access to complete data, errors in data collection, or exclusion of certain demographic groups. The fragmented nature of this data can lead to inherent bias in algorithms, affecting their ability to generalize and make fair decisions. Therefore, the quality and integrity of data are fundamental to the development of ethical and responsible AI systems. Identifying and correcting fractured data is a critical challenge in the field of AI, as it directly impacts the fairness and effectiveness of technological applications in various domains, including but not limited to healthcare, finance, and criminal justice. In summary, fractured data represents a significant risk to ethics in artificial intelligence, highlighting the need for careful and conscious management of the data used in model training.
