BiasLogik

BiasLogik is a conceptual framework for analyzing and mitigating bias in decision-making processes and data-driven systems. It aims to provide structured methods to identify how bias enters judgments and predictions, how it propagates through models, and how to reduce adverse impacts while preserving usefulness. The term blends bias with logic to emphasize formal reasoning about bias.

Origin and scope: Coined in scholarly discussions of AI ethics and cognitive science in the 2010s, BiasLogik has been described as a generic framework rather than a single algorithm. It emphasizes transparent documentation, reproducible procedures, and audit trails to support accountability across data collection, labeling, model training, and deployment.

Core components: bias sources (cognitive biases in human judgment, sampling and measurement biases in data, and algorithmic biases in model design), formal representations (probabilistic models, causal diagrams), metrics for evaluation (fairness, calibration, disparate impact), and governance procedures (pre-registration of bias tests, independent audits, version control).

Methods and practices: practitioners use BiasLogik to map potential biases, simulate their effects, and apply mitigation strategies such as reweighting, data augmentation, de-biasing objectives, or post-model adjustments, all while maintaining audit logs.

Applications and reception: used in AI model development, risk assessments, and policy analysis; cited in debates about fairness and accountability. Criticisms include definitional disputes about what constitutes bias, normative trade-offs, and the risk of misapplication if data and context are ignored.

See also: fairness in machine learning, explainable AI, data governance.