For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/08/privacy-a-surmountable-challenge-for-computer-vision-a-presentation-from-santa-clara-university/ Susan Kennedy, Assistant Professor of Philosophy at Santa Clara University, presents the “Privacy: A Surmountable Challenge for Computer Vision” tutorial at the May 2022 Embedded Vision Summit. Ethical concerns about privacy come with the territory of computer vision—after all, it’s hard to deny the privacy implications when considering the digital images or videos that are the bread and butter of these applications. But privacy concerns need not be a roadblock that stands in the way of technological innovation. While the idea of “informed consent” has been a popular method of preserving privacy in some domains, it may not always be either possible or desirable in the context of emerging technologies like computer vision. In this talk, Kennedy considers a more flexible approach to preserving privacy that pays special attention to the context that a device is being designed for. In addition, she reviews several examples that demonstrate how small changes to the design of technology can make a big difference when it comes to preserving privacy.

1. Privacy:
A Surmountable Challenge
for Computer Vision
Susan Kennedy, PhD
Assistant Professor of Philosophy
Santa Clara University

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3. Privacy: A Roadblock for Innovation?
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4. • Limitations of Current Privacy Procedures
• Contextual Integrity Framework
• Case studies: Privacy in Practice
• Key Takeaways
Agenda
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5. Looking Beyond Informed Consent
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6. • Context shapes users’ expectations
of privacy
• Privacy is a right to the
appropriate flow of information
Contextual Integrity
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7. Context-Relative Informational Norms
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Context: What is the prevailing context?
Actors: Who are the subjects, senders and
recipients of information?
Attributes: What is the type or nature of
information?
Transmission Principle: What are the
constraints on the flow of information?

8. Context-Relative Informational Norms
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Context: What is the prevailing context?
Actors: Who are the subjects, senders and
recipients of information?
Attributes: What is the type or nature of
information?
Transmission Principle: What are the
constraints on the flow of information?
If the new practice results in any
changes to these features, the
practice is flagged as violating
privacy

9. • The contextual integrity framework can
pinpoint privacy concerns and help guide
design solutions
• Practices that are flagged as violating
privacy may still be permissible, all things
considered
Second Chances
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10. Fall Detection Devices
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Context: Caregiving in health care facilities
Actors: Patients, Caregivers, Annotators at
Kepler
Attributes: Videos and Images of Patients
Transmission Principle: Caregiver’s mandate,
confidential
Application of Contextual Integrity Framework
Fall Detection
Device

12. • Design Solutions
• Security measures for the annotation process
• Moral Considerations
• Users directly benefit from improved model accuracy
• Invasions of user privacy are minimized compared to
the alternative best practices for caregivers
Fall Detection Device - Analysis
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Context: Identity verification and fraud
detection for the IRS
Actors: IRS, ID.me, third parties
Attributes: Images of users
Transmission Principle: Mandatory
Application of Contextual Integrity Framework
ID.me Facial
Recognition

14. • Design Solutions
• Alternative verification option for users that does not
involve facial recognition
• Create an avenue for users to provide consent for
data sharing
• Moral Considerations
• Users directly benefit from improved fraud detection
• Improves equity by enabling online access to
government services for users who are overseas
ID.me Facial Recognition - Analysis
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15. Operating Room Monitoring
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“Human Pose Estimation on Privacy-Preserving Low-Resolution Depth Images,” Srivastav, Gangi, Padoy 2019

16. Design Choices: Levels of Access
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“Blinkering Surveillance: Enabling Video Privacy through Computer Vision,” Senior, Pankanti, Hampapur, Brown, Tian, Ekin 2004.

17. • The Contextual Integrity framework can be used to:
• Pinpoint privacy concerns
• Guide design solutions
• Frame moral considerations
• Design choices: Hardware selection, de-identification, data minimization,
different levels of information access, etc.
Key Takeaways
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18. Resources
1 – Helen Nissenbaum’s book Privacy in
Context
https://dl.acm.org/doi/10.5555/1822585
2 – Privacy by Design: The 7 Foundational
Principles
https://www.ipc.on.ca/wp-
content/uploads/resources/7foundationalprin
ciples.pdf
3 – Privacy by Design – Practical Guidance
https://iapp.org/media/pdf/resource_center/p
bd_implement_7found_principles.pdf
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2022 Embedded Vision Summit
“Ask the Ethicist: Your Questions
about AI Privacy, Bias, and Ethics
Answered”
Tuesday, May 17th
2:40 – 3:10 pm PT
Business Insights 1 – Theater
(upstairs)