Algorithmic Bias
Posted: Thu Feb 12, 2026 10:53 am
Algorithmic Bias
Algorithmic bias occurs when an AI system consistently and unfairly discriminates against certain groups or outcomes as a result of the algorithms and the data it has been trained on. Algorithmic biases often reflect existing prejudices or inequalities present in the training data or the algorithm's decision-making process. Key sources include:
Data Bias - If the training data contains historical biases or imbalances (e.g., overrepresentation of certain demographics in criminal databases), the AI model is likely to learn and perpetuate these biases.
Algorithmic Design - The choice of algorithms and their parameters can introduce bias, particularly if they are more suited to certain types of data or tasks, leading to unfair outcomes for others.
Feedback Loops - AI systems, especially those that learn from their interactions, can develop biases over time through feedback loops, where initial biases get reinforced, further skewing the system's decisions.
Algorithmic bias occurs when an AI system consistently and unfairly discriminates against certain groups or outcomes as a result of the algorithms and the data it has been trained on. Algorithmic biases often reflect existing prejudices or inequalities present in the training data or the algorithm's decision-making process. Key sources include:
Data Bias - If the training data contains historical biases or imbalances (e.g., overrepresentation of certain demographics in criminal databases), the AI model is likely to learn and perpetuate these biases.
Algorithmic Design - The choice of algorithms and their parameters can introduce bias, particularly if they are more suited to certain types of data or tasks, leading to unfair outcomes for others.
Feedback Loops - AI systems, especially those that learn from their interactions, can develop biases over time through feedback loops, where initial biases get reinforced, further skewing the system's decisions.