Examples of using Interpretability in English and their translations into Chinese
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Programming
We need to prioritize interpretability- even in predictive contexts.
Interpretability(can the analytical model be easily explained to decision makers?).
This is important because in some domains, interpretability is critical.
Interpretability means giving explanations to the end users for a particular decision or process.
This is important because in some domains, interpretability is quite important.
However, because they are somewhat rigid in nature,these models provide a higher level of interpretability.
We are developing methods that allow better interpretability of machine learning systems.
Prior to this, interpretability methods only explained what neural networks were doing in terms of“input features.”.
We are developing methods that allow better interpretability of machine learning systems.
Both interpretability and justifiability are subjective and depend on the knowledge and experience of the decision maker.
We are developing methods that allow better interpretability of machine learning systems.
In either case, chasing interpretability may not satisfy our desire for a straightforward, plain-English description of a neural net output.
Methods from inferential statistics can range from quite simple to wildly complex, varying also in their precision,abstractness, and interpretability.
To achieve this high level of interpretability, GE needs to invent completely new technologies.
The next 3 methods are the alternative approaches that can provide better prediction accuracy andmodel interpretability for fitting linear models.
More work on improving model interpretability and on reducing model complexity without sacrificing accuracy is needed.
A good experimentation layer brings automation for feature engineering, feature selection, model selection,model optimization and model interpretability.
Part of this movement involves a reemphasis on interpretability in models, as opposed to black-box models.
For this reason, interpretability is a paramount quality that machine learning methods should aim to achieve if they are to be applied in practice.
We need to demystify the black box machine learning models andimprove transparency and interpretability to make them more trustworthy and reliable.
Fairness, Interpretability and Explainability are identified as the most frequently mentioned ethical challenges across 59 ethical AI principle documents.
When it comes to these non-cognitive tasks, interpretability of trained models will become more valuable, noted by Murphy.
Yet regulated industries(like banking) remain hesitant,often prioritizing regulatory compliance and algorithm interpretability over accuracy and efficiency.
The purpose of image computing is to improve interpretability of the reconstructed image and extract clinically relevant information from it.
For post hoc interpretability, work in this field should fix a clear objective and demonstrate evidence that the offered form of interpretation achieves it.
This comes at the expense of a small increase in the bias and some loss of interpretability, but generally greatly boosts the performance in the final model.
Ensure Human Interpretability of Algorithmic Decisions: AI systems must be designed with the minimum requirement that the designer can account for an AI agent's behaviors.
Moreover, this grammar allows us to systematically explore the space of interpretability interfaces, enabling us to evaluate whether they meet particular goals.
Sara Hooker, Google Brain researcher working on interpretability and model compression, founder of data for good non-profit Delta Analytics.
The complexity of the study intervention limits interpretability, but assessment is warranted of whether implementing this intervention in routine settings reduces acute care readmission.