Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Filling gaps in trustworthy development of AI   The range of application of artificial intelligence (AI) is vast, as is the
potential for harm. Growing awareness of potential risks from AI systems has
spurred action to address those risks, while eroding confidence in AI systems
and the organizations that develop them. A 2019 study found over 80
organizations that published and adopted "AI ethics principles'', and more have
joined since. But the principles often leave a gap between the "what" and the
"how" of trustworthy AI development. Such gaps have enabled questionable or
ethically dubious behavior, which casts doubts on the trustworthiness of
specific organizations, and the field more broadly. There is thus an urgent
need for concrete methods that both enable AI developers to prevent harm and
allow them to demonstrate their trustworthiness through verifiable behavior.
Below, we explore mechanisms (drawn from arXiv:2004.07213) for creating an
ecosystem where AI developers can earn trust - if they are trustworthy. Better
assessment of developer trustworthiness could inform user choice, employee
actions, investment decisions, legal recourse, and emerging governance regimes.

29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph