Bias

Bias within the field of AI is a multifaceted issue that can manifest in several forms, significantly affecting the fairness and integrity of AI systems. Computational bias arises when systematic errors or deviations occur in AI predictions, often stemming from the assumptions encoded within the AI models or the data used for training. Cognitive bias involves inaccurate judgments or distorted thinking by individuals who design, develop, or interact with AI systems, potentially leading to skewed outcomes. Societal bias reflects broader prejudices, favoritism, or discrimination entrenched in society, which can be perpetuated or amplified by AI systems. These biases can lead to unfair outcomes, posing risks to individual rights and liberties, and necessitating measures for identification, mitigation, and correction to ensure equitable AI applications.