Systemic Errors
Systemic errors refer to consistent and repeatable errors that arise from flaws within the system's design, methodology, or implementation. These errors are inherent to the system and can lead to predictable inaccuracies or biases in outcomes. Systemic errors might stem from:
Incorrect assumptions about the data or the environment in which the AI operates can introduce systemic errors. For example, designing a facial recognition system with the assumption that all faces will be well-lit can lead to poor performance in low-light conditions.
Errors in the architecture or design of the AI system, such as inadequate model complexity or inappropriate choice of algorithms, can lead to systemic inaccuracies.
Biases in how data is collected, such as sampling biases where certain groups are overrepresented or underrepresented, can introduce systemic errors.