What do you do if machine learning is impacting data governance with new technology?

Before ML is first integrated into the system, we need to understand the algorithms ML will use and their properties. We need to ensure our data governance (DG) framework is ready for it, and if not, it needs to be adjusted. We also need to assess the ML and the third party offering it from both a data quality and security perspective. Lastly, we need to assess the internal data processing methods being used and look for gaps and risks. To do these steps, there is a huge need to work closely with the data science team to understand our data better and be able to expand the data governance framework to meet the business's needs for using ML.

In the face of machine learning's impact on data governance with new technology, it's crucial to maintain a balance between innovation and responsibility. We need to ensure that our data governance frameworks evolve to accommodate the complexities introduced by machine learning models, including issues of transparency, fairness, and accountability. This involves establishing clear guidelines and standards for data collection, processing, and model deployment, while also fostering a culture of ethical decision-making and continuous monitoring. Ultimately, it's about leveraging the transformative power of machine learning while upholding the integrity and trustworthiness of our data ecosystems.

Review and update data governance policies and procedures to accommodate the use of machine learning algorithms and models. Address considerations such as data privacy, model interpretability, bias detection, and model explain ability.

Implement explainability to ensure transparency, build trust, and support compliance with regulations requiring explanations for automated decisions. Either with demonstration or techniques like LIME, SHAP or SAGE provide insights into why a particular prediction was made, enhancing transparency and trust among stakeholders while ensuring appropriate documentation.

Nowadays, we all are data users. Before, that was a role, while now, is the reality all over the company. That's why the "evangelization" about data use, products, and processes is crucial, so all the employees can see it as a mandatory part of business and IT operations.

Provide education and training on data governance principles, ML concepts, and best practices to stakeholders involved in ML projects. Help them understand the importance of data governance in ensuring the reliability, fairness, and ethical use of ML models.

Privacy and Data Protection: 1) ML models also rely on sensitive data (e.g., medical records, financial transactions). 2) Privacy-preserving ML methods protect individual privacy while enabling effective model training. Accountability and Responsibility: 1) Organizations and data scientists must take responsibility for the impact of ML models. 2) Clear guidelines and ethical frameworks are essential to guide ML development.

Implement mechanisms for continuous monitoring and auditing of ML models to detect deviations from expected behavior, performance degradation, or compliance issues. Establish processes for model retraining, validation, and version control to maintain model quality over time.

In ML projects, setting metadata attributes for privacy and ethical algorithm usage during the definition, analysis, or development phases is crucial. Labeling the use case with these attributes ensures privacy and ethical considerations are integrated from the outset. Monitoring data accuracy is also essential for effective ML models. Data quality monitoring tasks after implementing data quality rules help ensure models are based on accurate and reliable data, enhancing model performance and decision-making capabilities.

Recognize that data governance practices may need to evolve in response to changes in ML technology and regulatory requirements. Maintain a flexible and adaptive approach to data governance to accommodate emerging challenges and opportunities in the ML landscape.

When machine learning (ML) intersects with data governance, it introduces both opportunities and challenges. ML models generate insights from data, but their effectiveness relies on the quality and trustworthiness of that data.