Watson OpenScale is used by the notebook to log payload and monitor performance, quality, and fairness.

Drive fairer outcomes Watson OpenScale detects and helps mitigate model biases to highlight fairness issues. The platform provides plain text explanation of the 

Machine Learning with Jupyter 2021-02-10 · IBM Watson OpenScale is an enterprise-grade environment for AI infused applications that provides enterprises with visibility into how AI is being built, used, and delivering ROI – at the scale of their business. Teams. Q&A for work. Connect and share knowledge within a single location that is structured and easy to search.

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The following details for fairness metrics are supported by Watson OpenScale: The favorable percentages for each of groups Fairness averages for all the fairness groups Distribution of the data for each of the monitored groups Distribution of payload data Fairness and Drift 1. Fairness and Drift Configuration. OpenScale helps organizations maintain regulatory compliance by tracing and 2. Run Scoring Requests. Now that we have enabled a couple of monitors, we are ready to "use" the model and check if 3.

Can you trust your machine learning models to make fair decisions? Whether you're in a highly-regulated industry or simply looking to ensure that your busine

An IBM Cloud Account. What Openscale does is measure a model's fairness by calculating the difference between the rates at which different groups, for example, women versus men, received the same outcome. A fairness value below 100% means that the monitored group receives an unfavorable outcome more often than the reference group. Thus IBM Watson OpenScale not only helps customers identify Fairness issues in the model at runtime, it also helps to automatically de-bias the models.

You will understand how to use Watson OpenScale to build monitors for quality, fairness, and drift, and how monitors impact business KPIs. You will also learn 

You will understand how to use Watson OpenScale to build monitors for quality, fairness, and drift, and how monitors impact business KPIs. This offering teaches you how IBM Watson OpenScale on IBM Cloud Pak for Data lets business analysts, data scientists, and developers build monitors for artificial intelligence (AI) models to manage risks. You will understand how to use Watson OpenScale to build monitors for quality, fairness, and drift, and how monitors impact business KPIs. You will understand how to use Watson OpenScale to build monitors for quality, fairness, and drift, and how monitors impact business KPIs. You will also learn how monitoring for unwanted biases and viewing explanations of predictions helps provide business stakeholders confidence in the AI … You will learn how Watson OpenScale lets business analysts, data scientists, and developers build monitors for artificial intelligence (AI) models to manage risks. You will understand how to use Watson OpenScale to build monitors for quality, fairness, and drift, and how monitors impact business KPIs.

Additional information: The answer was coded as 'not applicable' if the respondent is. no member of the  research with the aim of accelerating the area of fairness in AI systems. A technical solution that IBM has developed for this purpose is called AI OpenScale.
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Typically, the reference group represents the majority group and the monitored group represents the minority group (or the group AI models could exhibit bias against). Let’s talk Deploy a Custom Machine Learning engine and Monitor Payload Logging and Fairness using AI OpenScale - IBM/monitor-custom-ml-engine-with-watson-openscale Watson OpenScale is used by the notebook to log payload and monitor performance, quality, and fairness.
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IBM Watson® OpenScale™, a capability within IBM Watson Studio on IBM Cloud Pak for Data, monitors and manages models to operate trusted AI. With model monitoring and management on a data and AI platform, an organization can: Monitor model fairness, explainability and drift. Visualize and track AI models in production.

Optionally, deploy a sample machine learning model to the WML instance. Configure the sample model instance to OpenScale, including payload logging, fairness checking, feedback, quality checking, drift checking, and explainability. If you would like to find out more about how AI in Control with Watson OpenScale can help empower you to have confidence in your AI and achieve your desired business outcomes while mitigating inherent risks around integrity, explainability, fairness, and resilience as you scale, please contact us. 2021-02-28 · OpenScale is configured so that it can monitor how your models are performing over time. The following screen shot gives one such snapshot: As we can see, the model for Tower C demonstrates a fairness bias warning of 92%. What is a fairness-bias and why do we need to mitigate it? Data in this day and age comes from a wide variety of sources.