Democratisation of Usable Machine Learning in Computer Vision

RR Bond, Ansgar Koene, Alan Dix, Jennifer Boger, Maurice Mulvenna, MG Galushka, Bethany Waterhouse-Bradley, Browne Fiona, H. Wang, Alexander Wong

Research output: Contribution to conferencePaper

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.

Conference

ConferenceWorkshop on Fairness Accountability Transparency and Ethics
in Computer Vision at CVPR 2019
CountryUnited States
CityLong Beach
Period17/06/1917/06/19
Internet address

Fingerprint

Computer vision
Learning systems
Computer programming
Automation
Inspection
Decision making
Monitoring
Industry

Keywords

  • machine learning
  • democratisation
  • AI
  • computer vision
  • AI literacy

Cite this

Bond, RR., Koene, A., Dix, A., Boger, J., Mulvenna, M., Galushka, MG., ... Wong, A. (2019). Democratisation of Usable Machine Learning in Computer Vision. Paper presented at Workshop on Fairness Accountability Transparency and Ethics
in Computer Vision at CVPR 2019 , Long Beach, United States.
Bond, RR ; Koene, Ansgar ; Dix, Alan ; Boger, Jennifer ; Mulvenna, Maurice ; Galushka, MG ; Waterhouse-Bradley, Bethany ; Fiona, Browne ; Wang, H. ; Wong, Alexander. / Democratisation of Usable Machine Learning in Computer Vision. Paper presented at Workshop on Fairness Accountability Transparency and Ethics
in Computer Vision at CVPR 2019 , Long Beach, United States.
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Bond, RR, Koene, A, Dix, A, Boger, J, Mulvenna, M, Galushka, MG, Waterhouse-Bradley, B, Fiona, B, Wang, H & Wong, A 2019, 'Democratisation of Usable Machine Learning in Computer Vision' Paper presented at Workshop on Fairness Accountability Transparency and Ethics
in Computer Vision at CVPR 2019 , Long Beach, United States, 17/06/19 - 17/06/19, .

Democratisation of Usable Machine Learning in Computer Vision. / Bond, RR; Koene, Ansgar; Dix, Alan; Boger, Jennifer; Mulvenna, Maurice; Galushka, MG; Waterhouse-Bradley, Bethany; Fiona, Browne; Wang, H.; Wong, Alexander.

2019. Paper presented at Workshop on Fairness Accountability Transparency and Ethics
in Computer Vision at CVPR 2019 , Long Beach, United States.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Democratisation of Usable Machine Learning in Computer Vision

AU - Bond, RR

AU - Koene, Ansgar

AU - Dix, Alan

AU - Boger, Jennifer

AU - Mulvenna, Maurice

AU - Galushka, MG

AU - Waterhouse-Bradley, Bethany

AU - Fiona, Browne

AU - Wang, H.

AU - Wong, Alexander

PY - 2019

Y1 - 2019

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AB - 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.

KW - machine learning

KW - democratisation

KW - AI

KW - computer vision

KW - AI literacy

UR - https://uwaterloo.ca/intelligent-technologies-wellness-independent-living/publications/democratisation-usable-machine-learning-computer-vision

M3 - Paper

ER -

Bond RR, Koene A, Dix A, Boger J, Mulvenna M, Galushka MG et al. Democratisation of Usable Machine Learning in Computer Vision. 2019. Paper presented at Workshop on Fairness Accountability Transparency and Ethics
in Computer Vision at CVPR 2019 , Long Beach, United States.