Biaseltávolítás

Biaseltávolítás is a Hungarian term that translates to "bias removal" or "bias correction." It refers to techniques and processes used to identify and mitigate or eliminate biased elements from data, algorithms, or decision-making systems. Bias can manifest in various forms, leading to unfair, discriminatory, or inaccurate outcomes.

In the context of data analysis and machine learning, biaseltávolítás aims to address systemic prejudices present in datasets. These biases can arise from historical inequalities, sampling methods, or the way data is collected and labeled. For example, a dataset used to train a facial recognition system might be heavily skewed towards certain demographics, leading to poorer performance for underrepresented groups. Biaseltávolítás techniques in this area might involve re-sampling, data augmentation, or using fairness-aware algorithms.

Beyond data, biaseltávolítás also applies to algorithmic processes and human decision-making. Algorithms can inadvertently perpetuate or even amplify biases present in their training data or through their design. Biaseltávolítás in algorithms might involve modifying objective functions, introducing fairness constraints, or employing post-processing adjustments to ensure equitable outputs. In human decision-making, it refers to recognizing and actively counteracting personal prejudices that can influence judgments in areas like hiring, loan applications, or legal proceedings. The goal is to promote objectivity and fairness.