Remove what-are-the-ethical-risks-of-your-ai-project
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What Are the Ethical Risks of Your AI Project?

Dataiku

AI systems may cause unintended harms and their increasing adoption leads to additional risks and scrutiny. In this blog post, I present tools project teams can use to identify ethical concerns associated with their AI use cases.

Risk 88
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CIOs grapple with the ethics of implementing AI

CIO Business Intelligence

AI has whet the appetites of organizations across nearly every sector. As AI pilots move toward production, discussions about the need for ethical AI are growing, along with terms like “fairness,” “privacy,” “transparency,” “accountability,” and the big one —”bias.”

Modeling 131
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Bringing an AI Product to Market

O'Reilly on Data

The Core Responsibilities of the AI Product Manager. Product managers for AI must satisfy these same responsibilities, tuned for the AI lifecycle. If you’re an AI product manager (or about to become one), that’s what you’re signing up for. Identifying the problem. Agreeing on metrics.

Marketing 362
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6 tough AI discussions every IT leader must have

CIO Business Intelligence

Few technologies have provoked the same amount of discussion and debate as artificial intelligence, with workers, high-profile executives, and world leaders waffling between praise and fears over AI. IT leaders say they’re discussing everything from the costs of AI implementations to whether AI is the existential threat to humanity some fear.

IT 140
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ChatGPT, Author of The Quixote

O'Reilly on Data

Copyright was intended to incentivize cultural production: in the era of generative AI, copyright won’t be enough. Generative AI Has a Plagiarism Problem ChatGPT, for example, doesn’t memorize its training data, per se. TL;DR LLMs and other GenAI models can reproduce significant chunks of training data.

Modeling 275
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The importance of diversity in AI isn’t opinion, it’s math

IBM Big Data Hub

We expect technologies such as artificial intelligence (AI) to not lie to us, to not discriminate, and to be safe for us and our children to use. Yet many AI creators are currently facing backlash for the biases, inaccuracies and problematic data practices being exposed in their models. Consider the diversity prediction theorem.

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10 things to watch out for with open source gen AI

CIO Business Intelligence

It seems anyone can make an AI model these days. Even if you don’t have the training data or programming chops, you can take your favorite open source model, tweak it, and release it under a new name. According to Stanford’s AI Index Report, released in April, 149 foundation models were released in 2023, two-thirds of them open source.