  Many industries are now investing heavily in data science and automation to
replace manual tasks and/or to help with decision making, especially in the
realm of leveraging computer vision to automate many monitoring, inspection,
and surveillance tasks. This has resulted in the emergence of the 'data
scientist' who is conversant in statistical thinking, machine learning (ML),
computer vision, and computer programming. However, as ML becomes more
accessible to the general public and more aspects of ML become automated,
applications leveraging computer vision are increasingly being created by
non-experts with less opportunity for regulatory oversight. This points to the
overall need for more educated responsibility for these lay-users of usable ML
tools in order to mitigate potentially unethical ramifications. In this paper,
we undertake a SWOT analysis to study the strengths, weaknesses, opportunities,
and threats of building usable ML tools for mass adoption for important areas
leveraging ML such as computer vision. The paper proposes a set of data science
literacy criteria for educating and supporting lay-users in the responsible
development and deployment of ML applications.
