Biasreduksjon
Biasreduksjon is a term that refers to the process of minimizing or eliminating unwanted systematic errors in data, models, or decision-making processes. These systematic errors, known as biases, can lead to unfair, inaccurate, or discriminatory outcomes. Bias can originate from various sources, including the data collection methods, the assumptions made during model development, or the inherent prejudices of individuals involved.
In machine learning and statistics, biasreduksjon is crucial for developing reliable and ethical AI systems. Techniques
Beyond technical applications, biasreduksjon also applies to human decision-making. This involves cultivating awareness of personal biases,