Periodically assess the assumptions behind your models. Engage cross-functional teams to provide diverse perspectives and challenge existing assumptions. Use a variety of datasets from different sources to capture a wider range of scenarios and reduce the risk of bias. This can include demographic data, behavioral data, and contextual information. Implement mechanisms for continuous learning, where models are regularly updated with new data. This helps correct inaccuracies and adapt to changing trends over time. Employ ensemble methods that combine multiple models to improve predictive performance. This can help mitigate the risk of bias from any single model.

Compare different models on the same data to see how each performs and assess their robustness. Also, employ model explainability tools to understand the logic behind predictions. These steps help identify and mitigate biases, ensuring the model accurately represents and responds to the data and problem at hand. Regularly monitoring predictive models helps ensure their ongoing accuracy and reliability. By detecting and addressing data drift or concept drift early on, businesses can prevent the model's predictions from becoming outdated or inaccurate, thus preserving the effectiveness of their decision-making process.

Identify potential biases in your dataset through techniques like exploratory data analysis. If certain groups are underrepresented or overrepresented, consider techniques like oversampling or undersampling to balance the dataset.

Facing biases in your predictive models? To ensure accuracy, start by identifying and analyzing the sources of bias in your data. Use diverse, representative datasets and apply techniques like resampling or weighting to correct imbalances. Regularly test and validate models, involve domain experts, and employ fairness metrics to measure model performance across different groups.

Bias is a hunting expedition. The first place to hunt is in the potential cesspool better known as your data. How was it collected, who collected it, what purpose was it collected for, what did management or governance want the data collection to show, was data systematically omitted and why, and which key elements are missing from the data and might be available elsewhere. The second place to hunt is every aspect of modeling. This includes assessing modeling assumptions, model incompleteness, model fitting criteria, model oversimplification, model complexity that both fails to extrapolate or interpolate well relative to the data collection, testing models on withheld data subsets, and fully understanding model/data context.