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Facial Recognition
AO 2023-XX
March 1, 2023
Increasingly common in society
Pervasive, yet not well-known
No federal regulations, guidelines
How it works:
● Maps your face; Most systems
assign it a mathematical identifier
● Compares it to some kind of
database (driver’s license, mug
shots, private)
Facial Recognition
Problems
Inaccuracies and Racist Effect
- Does not always accurately recognize the faces of people of
color
- Users can lower “confidence levels” if they do not get
matches, leading to even lower accuracy for identification
- Disproportionately affect people of color by mis-identifying
PoC most frequently of all demographics
● Robert Williams
● NIST study: False positive vs. False negative; 10 to 100 times
disparity
Problems (cont.)
Lack of existing regulation and oversight
● No regulation requiring transparency from FR
companies
● No current Muni policy
Data Privacy concerns
- Unlike other forms of data, faces cannot be encrypted
- Data breach involving facial recognition data would
increase potential for identity theft, stalking, and
harassment
- Can’t change face unlike passwords and financial data
- No consent
- Potential for abuse/misuse
Ordinance Language
Ban it, allow certain narrow exceptions for limited purposes:
● Prevent the municipality from acquiring the technology and from
conducting business with facial recognition companies
○ Provide definitions for FR software
● Financial penalties, employee discipline for using it
● Allow carve-outs for partnerships w/other agencies, personal devices
Goals
1. Protect civil liberties and individual data
privacy
2. Prevent the Muni from relying on
inaccurate, harmful technologies
3. Avoid potential litigation
Questions?

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Facial Recognition Presentation Mar 1.pdf

  • 2. Increasingly common in society Pervasive, yet not well-known No federal regulations, guidelines How it works: ● Maps your face; Most systems assign it a mathematical identifier ● Compares it to some kind of database (driver’s license, mug shots, private) Facial Recognition
  • 3. Problems Inaccuracies and Racist Effect - Does not always accurately recognize the faces of people of color - Users can lower “confidence levels” if they do not get matches, leading to even lower accuracy for identification - Disproportionately affect people of color by mis-identifying PoC most frequently of all demographics ● Robert Williams ● NIST study: False positive vs. False negative; 10 to 100 times disparity
  • 4. Problems (cont.) Lack of existing regulation and oversight ● No regulation requiring transparency from FR companies ● No current Muni policy Data Privacy concerns - Unlike other forms of data, faces cannot be encrypted - Data breach involving facial recognition data would increase potential for identity theft, stalking, and harassment - Can’t change face unlike passwords and financial data - No consent - Potential for abuse/misuse
  • 5. Ordinance Language Ban it, allow certain narrow exceptions for limited purposes: ● Prevent the municipality from acquiring the technology and from conducting business with facial recognition companies ○ Provide definitions for FR software ● Financial penalties, employee discipline for using it ● Allow carve-outs for partnerships w/other agencies, personal devices
  • 6. Goals 1. Protect civil liberties and individual data privacy 2. Prevent the Muni from relying on inaccurate, harmful technologies 3. Avoid potential litigation