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Model Risk Management And the Role of Explainable Models(With Python Code)

Analytics Vidhya

The post Model Risk Management And the Role of Explainable Models(With Python Code) appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon. Photo by h heyerlein on Unsplash Introduction Similar to rule-based mathematical.

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Risk Management for AI Chatbots

O'Reilly on Data

Doing so means giving the general public a freeform text box for interacting with your AI model. Welcome to your company’s new AI risk management nightmare. ” ) With a chatbot, the web form passes an end-user’s freeform text input—a “prompt,” or a request to act—to a generative AI model.

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XAI: Accuracy vs Interpretability for Credit-Related Models

Analytics Vidhya

When too much risk is restricted to very few players, it is considered as a notable failure of the risk management framework. […]. The post XAI: Accuracy vs Interpretability for Credit-Related Models appeared first on Analytics Vidhya.

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Big Data Hub

As more businesses use AI systems and the technology continues to mature and change, improper use could expose a company to significant financial, operational, regulatory and reputational risks. AI governance refers to the practice of directing, managing and monitoring an organization’s AI activities.

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Successful Change Management with Enterprise Risk Management

Speaker: William Hord, Vice President of ERM Services

A well-defined change management process is critical to minimizing the impact that change has on your organization. Leveraging the data that your ERM program already contains is an effective way to help create and manage the overall change management process within your organization. Organize ERM strategy, operations, and data.

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Risk Management Framework for AI/ML Models

KDnuggets

How sound risk management acts as a catalyst to building successful AI/ML models.

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Managing risk in machine learning

O'Reilly on Data

Considerations for a world where ML models are becoming mission critical. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. Before I continue, it’s important to emphasize that machine learning is much more than building models. Model lifecycle management.