BiasReduktion

BiasReduktion is a term used to describe practices, methods, and principles aimed at reducing bias in data, models, and decisions. It covers methodological approaches across research design, data collection, model development, evaluation, and governance, with the goal of minimizing systematic distortions that lead to unfair or inaccurate outcomes for individuals or groups.

In data science and machine learning, BiasReduktion involves identifying biased data distributions, auditing datasets for representational harms, and applying techniques such as re-sampling, reweighting, fairness-aware modeling, and post-processing of predictions. It also emphasizes transparency, including documenting data provenance and model assumptions, as well as engaging stakeholders to define fairness objectives.

In social science research, BiasReduktion refers to procedures that reduce researcher bias in study design, data collection, and interpretation, such as randomization, blinding, preregistration, and preregistered analysis plans. Across domains, evaluation relies on fairness metrics and assessment of trade-offs between bias reduction and other objectives, such as predictive performance or utility.

Policy and governance aspects of BiasReduktion establish standards, audits, and accountability mechanisms. Ethical considerations, legal compliance, and cultural context guide where and how bias-reduction methods are applied. While practical implementations vary by field, the overarching aim is to improve fairness, transparency, and trust in data-driven decisions and research findings.

See also algorithmic fairness, debiasing, fairness metrics, responsible AI.