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Weighing risk and reward with gen AI vendor selection

CIO Business Intelligence

The risk of going out of business is just one of many disaster scenarios that early adopters have to grapple with. And it’s not just start-ups that can expose an enterprise to AI-related third-party risk. Model training Vendors training their models on customer data isn’t the only training-related risk of generative AI.

Risk 130
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Automating Model Risk Compliance: Model Monitoring

DataRobot Blog

If the assumptions are being breached due to fundamental changes in the process being modeled, the deployed system is not likely to serve its intended purpose, thereby creating further model risk that the institution must manage. The accuracy of a model is another essential metric that informs us about its health in a deployed setting.

Risk 59
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What to Do When AI Fails

O'Reilly on Data

This article answers these questions, based on our combined experience as both a lawyer and a data scientist responding to cybersecurity incidents, crafting legal frameworks to manage the risks of AI, and building sophisticated interpretable models to mitigate risk. AI incidents, in other words, don’t require an external attacker.

Risk 359
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Build a RAG data ingestion pipeline for large-scale ML workloads

AWS Big Data

RAG is a machine learning (ML) architecture that uses external documents (like Wikipedia) to augment its knowledge and achieve state-of-the-art results on knowledge-intensive tasks. We introduce the integration of Ray into the RAG contextual document retrieval mechanism. Open the CreateRayCluster document. json| jq '.data[].paragraphs[].qas[].question'

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Financial services firms turn to automated, data-driven processes for new products and services

CIO Business Intelligence

Between the host of regulations introduced in the wake of the 2009 subprime mortgage crisis, the emergence of thousands of fintech startups, and shifting consumer preferences for digital payments banking, financial services companies have had plenty of change to contend with over the past decade.

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7 steps for turning shadow IT into a competitive edge

CIO Business Intelligence

Ask IT leaders about their challenges with shadow IT, and most will cite the kinds of security, operational, and integration risks that give shadow IT its bad rep. That’s not to downplay the inherent risks of shadow IT.

IT 137
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6 best practices to develop a corporate use policy for generative AI

CIO Business Intelligence

But just like other emerging technologies, it doesn’t come without significant risks and challenges. According to a recent Salesforce survey of senior IT leaders , 79% of respondents believe the technology has the potential to be a security risk, 73% are concerned it could be biased, and 59% believe its outputs are inaccurate.

Risk 122