Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Trust in AutoML: Exploring Information Needs for Establishing Trust in
  Automated Machine Learning Systems   We explore trust in a relatively new area of data science: Automated Machine
Learning (AutoML). In AutoML, AI methods are used to generate and optimize
machine learning models by automatically engineering features, selecting
models, and optimizing hyperparameters. In this paper, we seek to understand
what kinds of information influence data scientists' trust in the models
produced by AutoML? We operationalize trust as a willingness to deploy a model
produced using automated methods. We report results from three studies --
qualitative interviews, a controlled experiment, and a card-sorting task -- to
understand the information needs of data scientists for establishing trust in
AutoML systems. We find that including transparency features in an AutoML tool
increased user trust and understandability in the tool; and out of all proposed
features, model performance metrics and visualizations are the most important
information to data scientists when establishing their trust with an AutoML
tool.

2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th