The machine learning frameworks flourished in the last decades, allowing
artificial intelligence to get out of academic circles to be applied to
enterprise domains. This field has significantly advanced, but there is still
some meaningful improvement to reach the subsequent expectations. The proposed
framework, named AI$^{2}$, uses a natural language interface that allows a
non-specialist to benefit from machine learning algorithms without necessarily
knowing how to program with a programming language. The primary contribution of
the AI$^{2}$ framework allows a user to call the machine learning algorithms in
English, making its interface usage easier. The second contribution is
greenhouse gas (GHG) awareness. It has some strategies to evaluate the GHG
generated by the algorithm to be called and to propose alternatives to find a
solution without executing the energy-intensive algorithm. Another contribution
is a preprocessing module that helps to describe and to load data properly.
Using an English text-based chatbot, this module guides the user to define
every dataset so that it can be described, normalized, loaded and divided
appropriately. The last contribution of this paper is about explainability. For
decades, the scientific community has known that machine learning algorithms
imply the famous black-box problem. Traditional machine learning methods
convert an input into an output without being able to justify this result. The
proposed framework explains the algorithm's process with the proper texts,
graphics and tables. The results, declined in five cases, present usage
applications from the user's English command to the explained output.
Ultimately, the AI$^{2}$ framework represents the next leap toward native
language-based, human-oriented concerns about machine learning framework.

提出了一个名为 AI$^{2}$ 的框架，该框架利用自然语言接口，使非专家可以从机器学习算法中受益，其主要贡献是用英语调用机器学习算法，使其界面更易于使用。