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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. It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits.

Risk 70
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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. All predictive models are wrong at times?—just

Risk 361
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How to Leverage Machine Learning for AML Compliance

BizAcuity

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. In fact, online casinos as an industry carries the biggest risk of money laundering. OCR is widely used to digitize all kinds of physical documentation. Predictive Analytics can help businesses in reducing risk (eg.

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How to Leverage Machine Learning for AML Compliance

BizAcuity

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. In fact, online casinos as an industry carries the biggest risk of money laundering. OCR is widely used to digitize all kinds of physical documentation. Predictive Analytics.

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20 for 20: IRM Critical Capabilities & Top 20 Functions / Features

John Wheeler

We continue our “20 for 20” theme this year by highlighting the integrated risk management (IRM) critical capabilities and top 20 software functions / features. These five capabilities support both integrated view of strategic, operational and technology risk as well as the related business outcomes, processes and assets.

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Why you should care about debugging machine learning models

O'Reilly on Data

Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1] 1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. That’s where model debugging comes in. 6] Debugging may focus on a variety of failure modes (i.e.,

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The AI Contribution to Decision-Making

DataRobot Blog

Your artificial intelligence (AI) system has given you this “predicted feature” in addition to what you already know about the applicant. Business rules set by the credit committee to control business risk and the loan portfolio also constrain accepting this application and advancing the loan.