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Test Finds Amazon's Facial Recognition Software Wrongly Identified Members Of Congress As Persons Arrested. A Few Legislators Demand Answers

In a test of Rekognition, the facial recognition software by Amazon, the American Civil Liberties Union (ACLU) found that the software misidentified 28 members of the United States Congress to mugshot photographs of persons arrested for crimes. Jokes aside about politicians, this is serious stuff. According to the ACLU:

"The members of Congress who were falsely matched with the mugshot database we used in the test include Republicans and Democrats, men and women, and legislators of all ages, from all across the country... To conduct our test, we used the exact same facial recognition system that Amazon offers to the public, which anyone could use to scan for matches between images of faces. And running the entire test cost us $12.33 — less than a large pizza... The false matches were disproportionately of people of color, including six members of the Congressional Black Caucus, among them civil rights legend Rep. John Lewis (D-Ga.). These results demonstrate why Congress should join the ACLU in calling for a moratorium on law enforcement use of face surveillance."

List of 28 Congressional legislators mis-identified by Amazon Rekognition in ACLU study. Click to view larger version With 535 member of Congress, the implied error rate was 5.23 percent. On Thursday, three of the misidentified legislators sent a joint letter to Jeffery Bezos, the Chief executive Officer at Amazon. The letter read in part:

"We write to express our concerns and seek more information about Amazon's facial recognition technology, Rekognition... While facial recognition services might provide a valuable law enforcement tool, the efficacy and impact of the technology are not yet fully understood. In particular, serious concerns have been raised about the dangers facial recognition can pose to privacy and civil rights, especially when it is used as a tool of government surveillance, as well as the accuracy of the technology and its disproportionate impact on communities of color.1 These concerns, including recent reports that Rekognition could lead to mis-identifications, raise serious questions regarding whether Amazon should be selling its technology to law enforcement... One study estimates that more than 117 million American adults are in facial recognition databases that can be searched in criminal investigations..."

The letter was sent by Senator Edward J. Markey (Massachusetts, Representative Luis V. Gutiérrez (Illinois), and Representative Mark DeSaulnier (California). Why only three legislators? Where are the other 25? Nobody else cares about software accuracy?

The three legislators asked Amazon to provide answers by August 20, 2018 to several key requests:

  • The results of any internal accuracy or bias assessments Amazon perform on Rekognition, with details by race, gender, and age,
  • The list of all law enforcement or intelligence agencies Amazon has communicated with regarding Rekognition,
  • The list of all law enforcement agencies which have used or currently use Rekognition,
  • If any law enforcement agencies which used Rekogntion have been investigated, sued, or reprimanded for unlawful or discriminatory policing practices,
  • Describe the protections, if any, Amazon has built into Rekognition to protect the privacy rights of innocent citizens cuaght in the biometric databases used by law enforcement for comparisons,
  • Can Rekognition identify persons younger than age 13, and what protections Amazon uses to comply with Children's Online Privacy Protections Act (COPPA),
  • Whether Amazon conduts any audits of Rekognition to ensure its appropriate and legal uses, and what actions Amazon has taken to correct any abuses,
  • Explain whether Rekognition is integrated with police body cameras and/or "public-facing camera networks."

The letter cited a 2016 report by the Center on Privacy and Technology (CPT) at Georgetown Law School, which found:

"... 16 states let the Federal Bureau of Investigation (FBI) use face recognition technology to compare the faces of suspected criminals to their driver’s license and ID photos, creating a virtual line-up of their state residents. In this line-up, it’s not a human that points to the suspect—it’s an algorithm... Across the country, state and local police departments are building their own face recognition systems, many of them more advanced than the FBI’s. We know very little about these systems. We don’t know how they impact privacy and civil liberties. We don’t know how they address accuracy problems..."

Everyone wants law enforcement to quickly catch criminals, prosecute criminals, and protect the safety and rights of law-abiding citizens. However, accuracy matters. Experts warn that the facial recognition technologies used are unregulated, and the systems' impacts upon innocent citizens are not understood. Key findings in the CPT report:

  1. "Law enforcement face recognition networks include over 117 million American adults. Face recognition is neither new nor rare. FBI face recognition searches are more common than federal court-ordered wiretaps. At least one out of four state or local police departments has the option to run face recognition searches through their or another agency’s system. At least 26 states (and potentially as many as 30) allow law enforcement to run or request searches against their databases of driver’s license and ID photos..."
  2. "Different uses of face recognition create different risks. This report offers a framework to tell them apart. A face recognition search conducted in the field to verify the identity of someone who has been legally stopped or arrested is different, in principle and effect, than an investigatory search of an ATM photo against a driver’s license database, or continuous, real-time scans of people walking by a surveillance camera. The former is targeted and public. The latter are generalized and invisible..."
  3. "By tapping into driver’s license databases, the FBI is using biometrics in a way it’s never done before. Historically, FBI fingerprint and DNA databases have been primarily or exclusively made up of information from criminal arrests or investigations. By running face recognition searches against 16 states’ driver’s license photo databases, the FBI has built a biometric network that primarily includes law-abiding Americans. This is unprecedented and highly problematic."
  4. " Major police departments are exploring face recognition on live surveillance video. Major police departments are exploring real-time face recognition on live surveillance camera video. Real-time face recognition lets police continuously scan the faces of pedestrians walking by a street surveillance camera. It may seem like science fiction. It is real. Contract documents and agency statements show that at least five major police departments—including agencies in Chicago, Dallas, and Los Angeles—either claimed to run real-time face recognition off of street cameras..."
  5. "Law enforcement face recognition is unregulated and in many instances out of control. No state has passed a law comprehensively regulating police face recognition. We are not aware of any agency that requires warrants for searches or limits them to serious crimes. This has consequences..."
  6. "Law enforcement agencies are not taking adequate steps to protect free speech. There is a real risk that police face recognition will be used to stifle free speech. There is also a history of FBI and police surveillance of civil rights protests. Of the 52 agencies that we found to use (or have used) face recognition, we found only one, the Ohio Bureau of Criminal Investigation, whose face recognition use policy expressly prohibits its officers from using face recognition to track individuals engaging in political, religious, or other protected free speech."
  7. "Most law enforcement agencies do little to ensure their systems are accurate. Face recognition is less accurate than fingerprinting, particularly when used in real-time or on large databases. Yet we found only two agencies, the San Francisco Police Department and the Seattle region’s South Sound 911, that conditioned purchase of the technology on accuracy tests or thresholds. There is a need for testing..."
  8. "The human backstop to accuracy is non-standardized and overstated. Companies and police departments largely rely on police officers to decide whether a candidate photo is in fact a match. Yet a recent study showed that, without specialized training, human users make the wrong decision about a match half the time...The training regime for examiners remains a work in progress."
  9. "Police face recognition will disproportionately affect African Americans. Police face recognition will disproportionately affect African Americans. Many police departments do not realize that... the Seattle Police Department says that its face recognition system “does not see race.” Yet an FBI co-authored study suggests that face recognition may be less accurate on black people. Also, due to disproportionately high arrest rates, systems that rely on mug shot databases likely include a disproportionate number of African Americans. Despite these findings, there is no independent testing regime for racially biased error rates. In interviews, two major face recognition companies admitted that they did not run these tests internally, either."
  10. "Agencies are keeping critical information from the public. Ohio’s face recognition system remained almost entirely unknown to the public for five years. The New York Police Department acknowledges using face recognition; press reports suggest it has an advanced system. Yet NYPD denied our records request entirely. The Los Angeles Police Department has repeatedly announced new face recognition initiatives—including a “smart car” equipped with face recognition and real-time face recognition cameras—yet the agency claimed to have “no records responsive” to our document request. Of 52 agencies, only four (less than 10%) have a publicly available use policy. And only one agency, the San Diego Association of Governments, received legislative approval for its policy."

The New York Times reported:

"Nina Lindsey, an Amazon Web Services spokeswoman, said in a statement that the company’s customers had used its facial recognition technology for various beneficial purposes, including preventing human trafficking and reuniting missing children with their families. She added that the A.C.L.U. had used the company’s face-matching technology, called Amazon Rekognition, differently during its test than the company recommended for law enforcement customers.

For one thing, she said, police departments do not typically use the software to make fully autonomous decisions about people’s identities... She also noted that the A.C.L.U had used the system’s default setting for matches, called a “confidence threshold,” of 80 percent. That means the group counted any face matches the system proposed that had a similarity score of 80 percent or more. Amazon itself uses the same percentage in one facial recognition example on its site describing matching an employee’s face with a work ID badge. But Ms. Lindsey said Amazon recommended that police departments use a much higher similarity score — 95 percent — to reduce the likelihood of erroneous matches."

Good of Amazon to respond quickly, but its reply is still insufficient and troublesome. Amazon may recommend 95 percent similarity scores, but the public does not know if police departments actually use the higher setting, or consistently do so across all types of criminal investigations. Plus, the CPT report cast doubt on human "backstop" intervention, which Amazon's reply seems to heavily rely upon.

Where is the rest of Congress on this? On Friday, three Senators sent a similar letter seeking answers from 39 federal law-enforcement agencies about their use facial recognition technology, and what policies, if any, they have put in place to prevent abuse and misuse.

All of the findings in the CPT report are disturbing. Finding #3 is particularly troublesome. So, voters need to know what, if anything, has changed since these findings were published in 2016. Voters need to know what their elected officials are doing to address these findings. Some elected officials seem engaged on the topic, but not enough. What are your opinions?

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