Introduction to IBM Watson OpenScale
IBM Watson OpenScale documents all of the algorithms used in machine learning across its lifecycle, and adapts and governs these algorithms to changing business situations – for models built and running anywhere.
IBM Watson OpenScale
- Businesses from every industry are significantly investing in AI, ramping up platforms and skills. Although they can see the business value that AI will deliver, some companies are hesitating to move their AI experiments from pilot to production.
- AI can seem like a black box. It is hard to explain an AI model’s predictions, and difficult to ensure outcomes are not being affected by unwanted biases in the model.
- They must have confidence in AI’s ability to augment decision-making so that model predictions result in compliant, fair and explainable outcomes.
- IBM Watson OpenScale can provide these capabilities by bringing bias detection, explainability, and governance to AI and machine learning models. It is designed to complement a business’s AI strategy.
- Watson OpenScale can automatically identify attributes like sex, ethnicity, marital status and age and recommend they be monitored.
- Flagging attributes up front removes the need for manual selection of attributes to monitor and makes data preparation easier and gives data scientists more time to focus on building and improving models instead of monitoring them.
- Its tools, tests and metrics are presented in a way that business users can understand, giving them the power to contextually examine models. This reduces bottlenecks in production caused by too few expert users, typically on the data science team.
- Because perfect model accuracy and perfect model fairness are often at odds, these capabilities provide the ‘knobs’ to make adjustments so that businesses can control bias and ensure explainability and fairness for both regulatory compliance and operationalization.
Summary (unfinished)
- As the use of AI becomes more mainstream and a growing number of organizations are capitalizing on its many capabilities, there exists the need to address unwanted bias in AI models.
- Businesses must ensure that their model’s predictions produce fair, accurate, and unbiased outcomes before allowing those results to supplement decision making throughout the enterprise.
- Contact us to learn how Watson OpenScale can complement your organization’s AI strategies and help maintain compliance with corporate policies and regulatory
