ethical

Creating Constantly Improving Datasets for Ethical AI Training: A Focus on Spawning’s Goals

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Jordan Meyer and Mathew Dryhurst founded Spawning AI to create tools that help artists exert more control over how their works are used online. Meyer claims that, despite the fact that it’s substantially smaller than some other generative AI training data sets out there, Source.Plus’ data set is already “high-quality” enough to train a state-of-the-art image-generating model. Generative AI models “learn” to produce their outputs (e.g., photorealistic art) by training on a vast quantity of relevant data — images, in that case. Image Credits: Spawning“Source.Plus isn’t just a repository for training data; it’s an enrichment platform with tools to support the training pipeline,” he continued. And, Meyer says, Spawning might build its own generative AI models using data from the Source.Plus datasets.

Exxon’s Temper Flares as Shareholders Exercise Their Rights

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Top executives report to the CEO, the CEO answers to the board, and the board serves at the whim of the shareholders. On Sunday evening, the oil supermajor filed a lawsuit in federal court asking for permission to ignore a shareholder resolution at its next annual meeting. The new shareholder resolution calls on the oil company to reduce its Scope 3 emissions or those that result from the use of its products. Other shareholders tended to be deferential to management, letting them run their business as they saw fit. But as shareholder primacy took root in the public consciousness, more shareholders began to exercise their rights.

Effective Measures for Ethical Application of Generative AI by Corporate Leaders

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It’s becoming increasingly clear that businesses of all sizes and across all sectors can benefit from generative AI. McKinsey estimates generative AI will add $2.6 trillion to $4.4 trillion annually across numerous industries. That’s just one reason why over 80% of enterprises will be working with generative AI models, APIs, or applications by 2026. However, simply adopting generative AI doesn’t guarantee success. However, only 17% of businesses are addressing generative AI risks, which leaves them vulnerable.

A Concise Handbook for Promoting Ethical and Responsible Governance in Artificial Intelligence

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As AI applications continue to proliferate across industries, they hold the promise of revolutionizing customer experience, optimizing operational efficiency, and streamlining business processes. In recent years, concerns about ethical, fair, and responsible AI deployment have gained prominence, highlighting the necessity for strategic oversight throughout the AI life cycle. The rising tide of AI applications and ethical concernsThe proliferation of AI and ML applications has been a hallmark of recent technological advancement. AI governance has emerged as the cornerstone for responsible and trustworthy AI adoption. Strong ethical and risk-management frameworks are essential for navigating the complex landscape of AI applications.

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