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Proposing an Interactive Audit
Pipeline for Visual Privacy
Research
Jasmine DeHart, Chenguang Xu, Lisa Egede, Christan Grant
OUDATALAB.com
2021 IEEE International Conference on Big Data (BigData)
December 15 – 18, 2021
Traditional machine learning pipelines do not consider fairness, privacy,
and ownership issues as they arise.
We recommend frameworks to use for designing new ML pipelines.
In the following slides, we present a scenario that will describe the main
points of the paper.
The Boss Engineer #1 Engineer #2
🪖
You’re now the lead for our
machine learning team.
And, I have this great idea…
You’ve been doing a
great job!
Thanks, Boss!
Let’s build a People
Counter for the downtown
Smart City initiative.
Sounds innovative. I will
put together our
traditional machine
learning pipeline!
There are so many parts
and I have to build this
pipeline from scratch...
I’ll need to design three ML
pipeline phases:
1. Data Preparation Phase;
2. Modeling Phase;
3. Deployment Phase.
Phase 1: Data Preparation
I’ll start with the Data
Preparation Phase.
Phase 2: Modeling
This Modeling Phase
might take a while.
Phase 3: Deployment
This is looking good!
Here is the
complete pipeline
for camera-based
people counter!
Phase 3: Deployment
Phase 1: Data Preparation Phase 2: Modeling
This looks great. It
follows our
traditional pipeline
standards!
We can sell
this model
and data to
companies!
Wait a minute! There are
some additional things
we need to consider.
Historical bias Algorithmic bias Software Discrimination
See paper for more details…
Multiparty Conflict Image Removal Request Obtaining Content
Consent
Human-over-the-loop
• Regular updates help to avoid
and minimize costly errors
• Allows humans to step in pro re
nata to perform corrections or
updates
• Resolve biases that may be
imposed from humans or the
model during learning
Interactive Audit Strategies
Fairness Forensic Auditing System (FASt)
• Inspect a dataset or a model via
techniques and tools for bias
• FASt has three tasks: bias
detection, bias interpretation,
and bias mitigation.
Visual Privacy Auditor (ViP)
• Inspect a dataset or a model via
techniques and tools for privacy
concerns
• Visual privacy mitigation strategies
built into the ViP Auditor.
See paper for more details…
Here’s our updated
pipeline.
Human-over-the-loop
feedback is integrated
with the Audit
Strategies.
Conclusion
• We identify portions of the machine learning pipeline that contain visual
privacy and fairness issues.
• We walkthrough the need for responsible auditing systems to bring
accountability into the ML pipeline.
• We propose using human-over-the-loop strategies for auditing fairness and
privacy issues.
Acknowledgements
• Department of Defense SMART Scholarship
• National Science Foundation Grant # 1952181
• Photo Actors: Makya Stell (The Boss)
Jessica Reese & A’Kile Stone (Engineer 1 & 2)
Backup/Old slides
Intro/Motivate
• Definitions?
• Scenario? Could use reference throughout the presentation
High-level pipeline
• Quick overview of the ml pipeline setup
Issues Pipeline
• Add a issue or two at each spot
• Describe the issues
• Point to the paper
Solution Pipeline
• Discuss those two and how they solve the problem
• FASt
• ViP
Conclude/Future Work
Proposing an Interactive Audit Pipeline for Visual Privacy Research
Proposing an Interactive Audit Pipeline for Visual Privacy Research
Proposing an Interactive Audit Pipeline for Visual Privacy Research
Proposing an Interactive Audit Pipeline for Visual Privacy Research

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Proposing an Interactive Audit Pipeline for Visual Privacy Research

  • 1. Proposing an Interactive Audit Pipeline for Visual Privacy Research Jasmine DeHart, Chenguang Xu, Lisa Egede, Christan Grant OUDATALAB.com 2021 IEEE International Conference on Big Data (BigData) December 15 – 18, 2021
  • 2. Traditional machine learning pipelines do not consider fairness, privacy, and ownership issues as they arise. We recommend frameworks to use for designing new ML pipelines. In the following slides, we present a scenario that will describe the main points of the paper. The Boss Engineer #1 Engineer #2
  • 3. 🪖 You’re now the lead for our machine learning team. And, I have this great idea… You’ve been doing a great job! Thanks, Boss!
  • 4. Let’s build a People Counter for the downtown Smart City initiative. Sounds innovative. I will put together our traditional machine learning pipeline!
  • 5. There are so many parts and I have to build this pipeline from scratch... I’ll need to design three ML pipeline phases: 1. Data Preparation Phase; 2. Modeling Phase; 3. Deployment Phase.
  • 6. Phase 1: Data Preparation I’ll start with the Data Preparation Phase.
  • 7. Phase 2: Modeling This Modeling Phase might take a while.
  • 8. Phase 3: Deployment This is looking good!
  • 9. Here is the complete pipeline for camera-based people counter! Phase 3: Deployment Phase 1: Data Preparation Phase 2: Modeling
  • 10. This looks great. It follows our traditional pipeline standards!
  • 11. We can sell this model and data to companies! Wait a minute! There are some additional things we need to consider.
  • 12. Historical bias Algorithmic bias Software Discrimination See paper for more details… Multiparty Conflict Image Removal Request Obtaining Content Consent
  • 13. Human-over-the-loop • Regular updates help to avoid and minimize costly errors • Allows humans to step in pro re nata to perform corrections or updates • Resolve biases that may be imposed from humans or the model during learning
  • 14. Interactive Audit Strategies Fairness Forensic Auditing System (FASt) • Inspect a dataset or a model via techniques and tools for bias • FASt has three tasks: bias detection, bias interpretation, and bias mitigation. Visual Privacy Auditor (ViP) • Inspect a dataset or a model via techniques and tools for privacy concerns • Visual privacy mitigation strategies built into the ViP Auditor. See paper for more details…
  • 15. Here’s our updated pipeline. Human-over-the-loop feedback is integrated with the Audit Strategies.
  • 16. Conclusion • We identify portions of the machine learning pipeline that contain visual privacy and fairness issues. • We walkthrough the need for responsible auditing systems to bring accountability into the ML pipeline. • We propose using human-over-the-loop strategies for auditing fairness and privacy issues.
  • 17. Acknowledgements • Department of Defense SMART Scholarship • National Science Foundation Grant # 1952181 • Photo Actors: Makya Stell (The Boss) Jessica Reese & A’Kile Stone (Engineer 1 & 2)
  • 19. Intro/Motivate • Definitions? • Scenario? Could use reference throughout the presentation
  • 20. High-level pipeline • Quick overview of the ml pipeline setup
  • 21. Issues Pipeline • Add a issue or two at each spot • Describe the issues • Point to the paper
  • 22. Solution Pipeline • Discuss those two and how they solve the problem • FASt • ViP