How transparent are AI developers?
Sources:
Transparency among AI developers varies significantly between "open" and "closed" developers, but overall, major foundation model developers fall short of providing adequate transparency.
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Data Transparency: Open developers are generally more transparent about the data their models were trained on (47% for open vs. 9% for closed), the labor and compute resources used, and the methods and model access. However, closed developers slightly outperform in areas like capabilities, risks, and mitigations due to their controlled environments 1.
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Algorithmic Transparency: The distinction between systemic transparency (overall visibility into AI systems) and algorithmic transparency (understanding of how algorithms make decisions) is crucial. Systemic transparency is often emphasized to build trust by showing how systems are constructed, what data is used, and addressing potential biases 2 3.
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Explainability: Ethical AI design must incorporate explainability from the start. Retrofitting transparency is challenging, so proactive measures are needed to ensure algorithms can be understood and trusted by users 4.
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Industry Challenges: Despite efforts, there's a fundamental lack of transparency in the AI industry. Developers adopting best practices from competitors could significantly improve overall transparency 1.
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Ethical and Responsible AI: Ethical considerations such as non-bias, explainability, and non-destructive use are critical. Frameworks and regulations are being evaluated to ensure AI is implemented responsibly 4.
In summary, while open developers tend to be more transparent overall, the AI industry still faces significant challenges in achieving comprehensive transparency.
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