# 计算机代写|机器学习代写machine learning代考|CS7641

## 计算机代写|机器学习代写machine learning代考|Data and Model Transparency

Another requirement on the operational assessment of the explainable methods is the underlying transparency requirement. Sometimes the data that goes into the model is transformed. Also, before using the explanation system, users need to assess their the knowledge on the inner workings of the model. Users should also check whether the features are human understandable or not. For example, when predicting room temperature, you can either have a simple room temperature variable in the output or a squared sum of the room temperature and the room height. When the input is not easily understandable, the system designer may want to provide a list of data transformations.

The audience for any explanation method may vary from a domain expert to a person who is unaware of the model and explainability domain. There can be two types of audiences within domain experts: one with $\mathrm{AI} / \mathrm{ML}$ knowledge and the other with only the business domain knowledge. Therefore, being vigilant about the level and type of background knowledge of the audience comprehending a method becomes very important. Furthermore, the transparency of the features (or the model) should be considered with respect to the intended audience. For example, consider a system that explains its predictions using natural language sentences. Given the language skills of the recipient, the system can use sentences of varying grammatical and vocabulary complexity to facilitate easier comprehension; for example, explaining a disease diagnosis to doctors as opposed to patients or their families.

## 计算机代写|机器学习代写machine learning代考|Validation Requirements

Finally, explainable systems should be validated with user studies or synthetic experiments in a setting similar to the intended deployment scenario. A well-designed user study could also provide a clear answer to some of the (qualitative) explanation properties listed in the previous sections. For example, it can evaluate the effectiveness of an explanation for a particular audience, assess the background knowledge necessary to benefit from an explanation, or check the level of technical skills required to use it, because not everyone will be comfortable with a particular explanatory medium. Standardized user studies are not a silver bullet. However, a protocol (such as randomized controlled trials in medical sciences) should be in place, given that they are the most acceptable approach to validate the explainability powers of a new method.

A different set of, mostly synthetic, validation approaches was proposed using simulated data with known characteristics to validate the correctness of explanations and testing stability and consistency of explanations

• Simulatability measures how well a human can re-create or repeat (simulate) the computational process based on the provided explanations of a system.
• Algorithmic transparency measures the extent to which a human can fully understand a predictive algorithm: its training procedure, the provenance of its parameters, and the process governing its predictions.
• Decomposability quantifies the ability of the listener to comprehend individual parts (and their functionality) of a predictive model: understanding features of the input, parameters of the model (e.g., a monotonic relationship of one of the features), and the model’s output.

# 机器学习代考

## 计算机代写|机器学习代写machine learning代考|Validation Requirements

• 可模拟性衡量人类根据提供的系统解释重新创建或重复（模拟）计算过程的能力。
• 算法透明度衡量人类可以完全理解预测算法的程度：它的训练过程、它的参数的来源，以及控制它的预测的过程。
• 可分解性量化了听者理解预测模型的各个部分（及其功能）的能力：理解输入的特征、模型的参数（例如，其中一个特征的单调关系）和模型的输出。

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