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AI for Money Managers: Avoid the Black Box – And Do This Instead


The opportunities afforded by AI are truly significant – but can we trust black box AI to produce the right results? Instead of utilizing AI systems that they cannot explain – black box AI systems – they could utilize AI platforms that use transparent techniques , explaining how they arrive at their conclusions.

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Who Is Responsible If Healthcare AI Fails?


Similarly, what if a drug diagnosis algorithm recommends the wrong medication for a patient and they suffer a negative side effect? At the root of AI mistakes like these is the nature of AI models themselves. Most AI today use “black box” logic, meaning no one can see how the algorithm makes decisions.


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Using AI for Predictive Analytics in Aviation Safety

Aiiot Talk

AI is today’s most advanced form of predictive maintenance, using algorithms to automate performance and sensor data analysis. Aircraft owners or technicians set up the algorithm with airplane data, including its key systems and typical performance metrics. Black-box AI poses a serious concern in the aviation industry.

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Is Rapid AI Adoption Posing Serious Risks for Corporations?

ODSC - Open Data Science

Bias and Inequality AI can also introduce societal issues like exaggerating bias if corporations aren’t careful. Amazon’s scrapped hiring AI model infamously penalized women’s resumes as the machine learning algorithm expanded on implicit biases within the training data.

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The future of QA is here, meet QA-GPT


Auto-QA solutions employ various methods such as speech analytics, natural language processing (NLP), sentiment analysis, and machine learning algorithms to automatically review and score customer interactions. In our testing, we found that QA-GPT can cover over 85% of scorecard questions out of the box without any extra configuration.

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Unlocking the Black Box: LIME and SHAP in the Realm of Explainable AI

Unlike traditional ‘black boxAI models that offer little insight into their inner workings, XAI seeks to open up these black boxes, enabling users to comprehend, trust, and effectively manage AI systems. Until then, keep exploring, keep questioning, and let the machines keep talking.

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Copyright, AI, and Provenance

O'Reilly Media

I can also ask for a reading list about plagues in 16th century England, algorithms for testing prime numbers, or anything else. The problem with Lanier’s concept of data dignity is that, given the current state of the art in AI models, it is impossible to distinguish meaningfully between “training” and “generating output.”

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