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I L T A N E T . O R G
Metrics for measuring bias include statistical parity
difference, equal opportunity difference, and disparate
impact ratio. These help identify whether certain groups
are unfairly affected by the model's predictions.
Mitigation strategies involve methods before, during, and
after model training. Pre-processing techniques adjust the
data to balance representation, in-processing methods
modify the learning algorithm to reduce bias, and post-
processing adjusts the model's outputs. Ensuring these
techniques don't reduce the model's accuracy in legal
applications is vital.
ensure integrity, fairness, and accountability across
various domains of AI technologies.
When applied to legal practice, these ethical concerns
become even more significant. The potential
consequences of biased AI decisions in the legal field
can impact the justice system's credibility and the lives of
individuals. Therefore, maintaining high ethical standards
and putting strong frameworks in place to address these
challenges is crucial.
MODEL BIAS DETECTION AND MITIGATION
STRATEGIES
Bias in AI models can lead to unfair outcomes and damage
the justice system's integrity.
FEATURES
