Remove Interactive Remove Measurement Remove Risk Management
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5 tips for better business value from gen AI

CIO Business Intelligence

Set clear, measurable metrics around what you want to improve with generative AI, including the pain points and the opportunities, says Shaown Nandi, director of technology at AWS. In HR, measure time-to-hire and candidate quality to ensure AI-driven recruitment aligns with business goals.

Sales 143
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AI brings complexity to cybersecurity and fraud

CIO Business Intelligence

The 2024 Security Priorities study shows that for 72% of IT and security decision makers, their roles have expanded to accommodate new challenges, with Risk management, Securing AI-enabled technology and emerging technologies being added to their plate.

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Sweat the small stuff: Data protection in the age of AI

CIO Business Intelligence

As concerns about AI security, risk, and compliance continue to escalate, practical solutions remain elusive. as AI adoption and risk increases, its time to understand why sweating the small and not-so-small stuff matters and where we go from here. AI usage may bring the risk of sensitive data exfiltration through AI interactions.

Risk 105
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Managing machine learning in the enterprise: Lessons from banking and health care

O'Reilly on Data

After the 2008 financial crisis, the Federal Reserve issued a new set of guidelines governing models— SR 11-7 : Guidance on Model Risk Management. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk. Model risk management. AI projects in financial services and health care.

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Where CIOs should place their 2025 AI bets

CIO Business Intelligence

Deloittes State of Generative AI in the Enterprise reports nearly 70% have moved 30% or fewer of their gen AI experiments into production, and 41% of organizations have struggled to define and measure the impacts of their gen AI efforts. One opportunity is for CIOs to help their marketing departments improve brand loyalty.

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AI and cybersecurity: A double-edged sword

CIO Business Intelligence

As a secondary measure, we are now evaluating a few deepfake detection tools that can be integrated into our business productivity apps, in particular for Zoom or Teams, to continuously detect deepfakes. In other words, humans are still required to interpret any business contextual information that AI might miss.

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Managing risk in machine learning

O'Reilly on Data

Fortunately, a recent survey paper from Stanford— A Critical Review of Fair Machine Learning —simplifies these criteria and groups them into the following types of measures: Anti-classification means the omission of protected attributes and their proxies from the model or classifier.