Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Explainability in Machine Learning: a Pedagogical Perspective   Given the importance of integrating of explainability into machine learning,
at present, there are a lack of pedagogical resources exploring this.
Specifically, we have found a need for resources in explaining how one can
teach the advantages of explainability in machine learning. Often pedagogical
approaches in the field of machine learning focus on getting students prepared
to apply various models in the real world setting, but much less attention is
given to teaching students the various techniques one could employ to explain a
model's decision-making process. Furthermore, explainability can benefit from a
narrative structure that aids one in understanding which techniques are
governed by which questions about the data.
  We provide a pedagogical perspective on how to structure the learning process
to better impart knowledge to students and researchers in machine learning,
when and how to implement various explainability techniques as well as how to
interpret the results. We discuss a system of teaching explainability in
machine learning, by exploring the advantages and disadvantages of various
opaque and transparent machine learning models, as well as when to utilize
specific explainability techniques and the various frameworks used to structure
the tools for explainability. Among discussing concrete assignments, we will
also discuss ways to structure potential assignments to best help students
learn to use explainability as a tool alongside any given machine learning
application.
  Data science professionals completing the course will have a birds-eye view
of a rapidly developing area and will be confident to deploy machine learning
more widely. A preliminary analysis on the effectiveness of a recently
delivered course following the structure presented here is included as evidence
supporting our pedagogical approach.

31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg