Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Risk Assessment for Machine Learning Models   In this paper we propose a framework for assessing the risk associated with
deploying a machine learning model in a specified environment. For that we
carry over the risk definition from decision theory to machine learning. We
develop and implement a method that allows to define deployment scenarios, test
the machine learning model under the conditions specified in each scenario, and
estimate the damage associated with the output of the machine learning model
under test. Using the likelihood of each scenario together with the estimated
damage we define \emph{key risk indicators} of a machine learning model.
  The definition of scenarios and weighting by their likelihood allows for
standardized risk assessment in machine learning throughout multiple domains of
application. In particular, in our framework, the robustness of a machine
learning model to random input corruptions, distributional shifts caused by a
changing environment, and adversarial perturbations can be assessed.

34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th