The data revolution has generated a huge demand for data-driven solutions.
This demand propels a growing number of easy-to-use tools and training for
aspiring data scientists that enable the rapid building of predictive models.
Today, weapons of math destruction can be easily built and deployed without
detailed planning and validation. This rapidly extends the list of AI failures,
i.e. deployments that lead to financial losses or even violate democratic
values such as equality, freedom and justice. The lack of planning, rules and
standards around the model development leads to the ,,anarchisation of AI".
This problem is reported under different names such as validation debt,
reproducibility crisis, and lack of explainability. Post-mortem analysis of AI
failures often reveals mistakes made in the early phase of model development or
data acquisition. Thus, instead of curing the consequences of deploying harmful
models, we shall prevent them as early as possible by putting more attention to
the initial planning stage.
In this paper, we propose a quick and simple framework to support planning of
AI solutions. The POCA framework is based on four pillars: Performance,
Opaqueness, Consequences, and Assumptions. It helps to set the expectations and
plan the constraints for the AI solution before any model is built and any data
is collected. With the help of the POCA method, preliminary requirements can be
defined for the model-building process, so that costly model misspecification
errors can be identified as soon as possible or even avoided. AI researchers,
product owners and business analysts can use this framework in the initial
stages of building AI solutions.

该论文提出了一个名为 POCA 的快速简单的框架，支持 AI 方案的规划，包含 Performance、Opaqueness、Consequences 和 Assumptions 四个方面，帮助在建立 AI 方案的初期阶段定义初步要求，避免昂贵的模型错误。

机器学习方案负责规划的简单问题：绩效、不透明度、后果和假设

Performance, Opaqueness, Consequences, and Assumptions: Simple questions for responsible planning of machine learning solutions

Closed-form, interpretable mathematical models have been instrumental for
advancing our understanding of the world; with the data revolution, we may now
be in a position to uncover new such models for many systems from physics to
the social sciences. However, to deal with increasing amounts of data, we need
"machine scientists" that are able to extract these models automatically from
data. Here, we introduce a Bayesian machine scientist, which establishes the
plausibility of models using explicit approximations to the exact marginal
posterior over models and establishes its prior expectations about models by
learning from a large empirical corpus of mathematical expressions. It explores
the space of models using Markov chain Monte Carlo. We show that this approach
uncovers accurate models for synthetic and real data and provides out-of-sample
predictions that are more accurate than those of existing approaches and of
other nonparametric methods.

引入了一种贝叶斯机器科学家，它使用对模型的后验分布的明确逼近来确定模型的合理性，并通过从数学表达式的大量经验语料库中进行学习来确定模型的先验期望。该方法可以自动从数据中提取精确的模型，并且在合成数据和真实数据上提供比现有方法和其他非参数方法更准确的外样本预测。