Description: Inherent bias refers to the tendency of data to reflect certain prejudices or inclinations that can negatively influence the quality and fairness of the results generated by artificial intelligence (AI) systems. This phenomenon is particularly relevant in the context of ethics and data anonymization, as AI algorithms are often trained using datasets that may contain historical or cultural biases. As a result, automated decisions can perpetuate or even amplify these biases, disproportionately affecting specific groups of people. Inherent bias can manifest in various forms, such as discrimination in hiring, access to financial services, or law enforcement. Identifying and mitigating these biases is crucial to ensuring that AI technologies are fair and equitable, thereby promoting trust in their use. Data anonymization, while essential for protecting privacy, can further complicate this issue, as the removal of identifying information does not always eliminate the underlying biases present in the data. Therefore, it is essential to address inherent bias from an ethical perspective, ensuring that AI systems are developed and used responsibly and with awareness of their social implications.
