Responsible AI by Patrick Hall & Rumman Chowdhury

Author:Patrick Hall & Rumman Chowdhury [Patrick Hall]
Language: eng
Format: epub
Publisher: O'Reilly Media, Inc.
Published: 2022-08-24T16:00:00+00:00

Benchmarks and Alternatives

When starting a ML modeling task, it’s best to begin with a peer-reviewed training algorithm, and ideally to replicate any benchmarks associated with that algorithm. While academic algorithms rarely meet all the needs of complex business problems, starting from a well-known algorithm and benchmarks provides a baseline assurance that the training algorithm is implemented correctly. Once this sanity check is addressed, then think about tweaking a complex algorithm to address specific quirks of a given problem.

Along with comparison to benchmarks, evaluation of numerous alternative algorithmic approaches is another best practice that can improve safety and performance outcomes. The exercise of training many different algorithms and judiciously selecting the best of many options for final deployment typically results in higher-quality models because it increases the number of models evaluated and forces users to understand differences between them. Moreover, evaluation of alternative approaches is important in complying with a broad set of U.S. non-discrimination and negligence standards. In general, these standards require evidence that different technical options were evaluated and an appropriate trade-off between consumer protection and business need was made before deployment.

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