How the machine "thinks"¶
Burrell, J. (2016). How the machine "thinks": Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1-12.
Cited by¶
1 citation across 1 artifact.
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Primes¶
- Transparency
- The AI-systems row deserves particular attention because algorithmic opacity creates a distinctive transparency challenge that Burrell (2016) decomposes into three forms — intentional corporate secrecy, technical illiteracy of audiences, and the irreducible opacity of high-dimensional learned models — each requiring different disclosure mechanisms.
This sourceDecomposes algorithmic opacity into three forms — intentional corporate or state secrecy, technical illiteracy of audiences, and the irreducible opacity of high-dimensional learned models — clarifying why standard disclosure mechanisms underdetermine algorithmic transparency.
- The AI-systems row deserves particular attention because algorithmic opacity creates a distinctive transparency challenge that Burrell (2016) decomposes into three forms — intentional corporate secrecy, technical illiteracy of audiences, and the irreducible opacity of high-dimensional learned models — each requiring different disclosure mechanisms.
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