Democratisation of Usable Machine Learning in Computer Vision

TL;DR

This paper analyzes the democratization of usable machine learning in computer vision, emphasizing the need for education and responsibility among non-expert users to ensure ethical deployment.

Contribution

It introduces a SWOT analysis framework and proposes data science literacy criteria to support responsible use of ML by lay-users.

Findings

SWOT analysis highlights key strengths and threats in democratizing ML

Proposes literacy criteria for responsible ML application development

Emphasizes importance of education for ethical ML deployment

Abstract

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.