Facebook Fails Basic Audit of 2019 Civil Rights Legal Settlement

Dion Diamond of the Non-Violent Action Group during a sit-in at the Cherrydale Drug Fair in Arlington, Virginia gets harassed by white nationalists. Despite physical blows and lit-cigarettes being thrown, two-weeks of protests in June 1960 led to Arlington, Alexandria and Fairfax restaurants removing explicit racism from their services. Gus Chinn/Courtesy of the DC Public Library Washington Star Collection/Washington Post

A fascinating new paper (Algorithms That “Don’t See Color”: Comparing Biases in Lookalike and Special Ad Audiences) audits an obfuscated security fix of Facebook algorithms and finds a giant vulnerability remains.

The conclusion (spoiler alert) is that Facebook’s ongoing failure to fix its platform security means it should be held accountable for an active role in unfair/harmful content distribution.

Facebook itself could also face legal scrutiny. In the U.S., Section 230 of the Communications Act of 1934 (as amended by the Communications Decency Act) provides broad legal immunity to Internet platforms acting as publishers of third-party content. This immunity was a central issue in the litigation resulting in the settlement analyzed above. Although Facebook argued in court that advertisers are “wholly responsible for deciding where, how, and when to publish their ads”, this paper makes clear that Facebook can play a significant, opaque role by creating biased Lookalike and Special Ad audiences. If a court found that the operation of these tools constituted a “material contribution” to illegal conduct, Facebook’s ad platform could lose its immunity .

Facebook’s record on this continue to puzzle me. They have run PR campaigns about concern for general theories of safety, yet seem always to be engaged in pitiful disregard for the rights of their own users.

It reminds me a million years ago, when I led security for Yahoo “Connected Life”, how my team had zero PR campaigns yet took threats incredibly seriously. We regularly would get questions from advertisers for access or identification that could harm user rights.

A canonical test, for example, was a global brand asks for everyone’s birthday for an advertising campaign. We trained day and night for handling this kind of request, which we would push back immediately to protect trust in the platform.

Our security team was committed to preserving rights and would start conversations with a “why” and sometimes would get to four or five more. As cheesy as it sounds we even had t-shirts printed that said “why?” on the sleeve to reinforce the significance of avoiding harms through simple sets of audit steps.

Why would an advertiser ask for a birthday? A global brand would admit they wanted ads to target a narrow age group. We consulted with legal and offered them instead a yes/no answer for a much broader age group (e.g. instead of them asking for birthdays we allowed them to ask is a person older than 13). Big brand accepted our rights-preserving counter-proposal, and we verified they saw nothing more from our system than binary and anonymous yes/no.

This kind of fairness goal and confidentiality test procedure was a constant effort and wasn’t rocket-science, although it was seen as extremely important to protecting trust in our platform and the rights of our users.

Now fast-forward to Facebook’s infamous reputation for leaking data (like allegedly billions of data points per day fed to Russia), and their white-male dominated “tech-bro” culture of privilege with its propensity over the last ten years to repeatedly fail user trust.

It seems amazing that the U.S. government haven’t moved forward with their plan for putting the Facebook executives in jail. Here’s yet another example of how Facebook leadership fails basic tests as if they can’t figure security out themselves:

Earlier this year the company was in a massive civil rights lawsuit.

The suit comes after a widely-read ProPublica article in which the news organization created an ad targeting people who were interested in house-hunting. The news organization used Facebook’s advertising tools to prevent the ad from being shown to Facebook users identified as having African American, Hispanic, and Asian ethnic affinities.

As a result of this lawsuit Facebook begrudgingly agreed to patch its “Lookalike Audiences” tool and claimed the fix would make it unbiased.

The tool originally earned its name by taking a source audience from an advertiser and then targeting “lookalike” Facebook users. “Whites-only” apparently would have been a better name for how the tool was being used, according to the lawsuit examples.

The newly patched tool was claimed to remove the “whites-only” Facebook effect by blocking the algorithm from input of certain demographic features in a source audience. The tool also unfortunately was renamed to “Special Ad Audiences” allegedly as an “inside” joke to frame non-white or diverse audiences as “Special Ed” (the American term pejoratively used to refer to someone as stupid).

The simple audit of this patch, as described by authors of the new paper, was submitting a biased source audience (with known skews in politics, race, age, religion etc) into parallel Lookalike and Special Ad algorithms. The result of the audit is…drumroll please…Special Ad audiences retain the biased output of Lookalike, completely failing the Civil Rights test.

Security patch fail.

With great detail the paper illustrates how removing demographic features for the Special Ad algorithm did not make the output audience differ from the Loookalike one. In other words, and most important of all, blocking demographic inputs fails to prevent Facebook algorithm generation of predictably biased output.

As tempting as it is to say we’re working on the “garbage input, garbage output” problem, we shouldn’t be fooled by anyone claiming their algorithmic discrimination will magically be fixed by just adjusting inputs.

Could NASCAR Be America’s Blueprint for Driverless Ethics?

As the old New Yorker cartoon used to say…

Honesty is the best policy, but it’s not company policy.

Years ago I wrote about the cheating of NASCAR car drivers. And recently at the last BSidesLV conference I pointed out in my talk how human athletes in America get banned for cheating, while human car drivers get respect.

Anyway I was reading far too much on this topic when I starting thinking how NASCAR studies of ten years ago to end cheating could be a compelling area of research for ethics in driverless cars:

Proposed solutions include changing the culture within the NASCAR community, as well as developing ethical role models, both of which require major action by NASCAR’s top managers to signal the importance of ethical behavior. Other key stakeholders such as sponsors and fans must create incentives and rewards for ethical behavior, and consider reducing or ending support for drivers and teams that engage in unethical conduct.

That’s some high-minded analysis given the inaugural race at Talladega (Alabama International Motor Speedway) had a 1969 Ford with its engine set back nearly a foot from stock (heavier weight distribution to the rear — violating the rules).

This relocation of the engine was easily seen by any casual observer yet the car was allowed to race and finished 9th. Bill France owned the car. Yes, that Bill France. The same guy who owned the track and NASCAR itself…entered an illegal car.

An illegal car actually is icing on the cake, though. Bill France built this new track with unsafe parameters and when drivers tried to boycott the conditions, he solicited drivers to break the safety boycott and issued free tickets to create an audience.

NASCAR retells a story full of cheating as the success that comes from ignoring ethics:

“I really admired that he told everybody to kiss his ass, that that race was going to run,” Foyt said.

The sentiment of getting everyone together to agree to an ethical framework sounds great, until you realize NASCAR stands for the exact opposite. It seems to have a history where cheating without getting punished is their very definition of winning.

Robots Get “Butter” Driving Skills

Tech philosophers in America watch closely the attempts of highly-individualistic short-term investor-run truck companies to behave as much on shared infrastructure like trains as possible without realizing the societal benefits of a train

Nvidia boasts in the Sacramento Bee of a truck that was able to drive a load of butter across America

…the first coast-to-coast commercial freight trip made by a self-driving truck, according to the company’s press release. Plus.ai announced on Tuesday that its truck traveled from Tulare, California, to Quakertown carrying over 40,000 pounds of Land O’Lakes butter.

Mercury News says it took three days on two interstate routes (e.g. human life prohibited) and didn’t experience any problems.

The truck, which traveled on interstates 15 and 70 right before Thanksgiving, had to take scheduled breaks but drove mostly autonomously. There were zero “disengagements,” or times the self-driving system had to be suspended because of a problem, Kerrigan said.

Indeed. The truck appears to have operated about as much like a train as one could get, although at much higher costs. If we had only invested a similar amount of money into startups to achieve a 250 mph service on upgraded tracks across America…

2800 miles at 250 mph is just 11 hours. Using electric line-of-sight delivery drones to load and unload the “last mile” from high-speed train stations at either end would even further expedite delivery time.

Trains = 12 hours or less and clean
Trucks = 24 hours or more + distributed environmental pollutants (fuel exhaust, tire wear, brake wear, wiper fluids…)

Trucks can’t improve their time much more because they start becoming more and more a threat to others trying to operate safely on the Interstate’s self-run collision avoidance systems. And it’s exactly the delta between operating speeds of vehicles on the same lines that generates the highest risks of disaster.

The absolute best whey going forward to skim time (yes, I said it) should be clear (it’s trains), although we’re talking long-term thinking here, which investors lurking around startups for 2-year 20% returns on their money have never been known to embrace.

Driverless trucks in this context are a form of future steampunk, like someone boasting today their coal-fired dirigible has upgraded to an auto-scooper so they no longer need to abduct children into forced labor.

Congratulations on being less of a selfish investor threat to others, I guess? Now maybe try adopting a socially conscious model instead.

1953 Machina Speculatrix: The First Swarm Drone?

A talk I was watching recently suggested researchers finally in 2019 had cracked how robots could efficiently act like a swarm. Their solution? Movement based entirely on a light sensor.

That sounded familiar to me so I went back to one of my old presentations on IoT/AI security and found a slide showing the same discovery claim from 1953. Way back then people used fancier terms than just swarm.

W. Grey Walter built jelly-fish-like robots that were reactive to their surroundings: light sensor, touch sensor, propulsion motor, steering motor, and a two vacuum tube analog computer. He called their exploration behavior Machina Speculatrix and the individual robots were named Elmer or Elsie (ELectro MEchanical Robots, Light Sensitive)

The rules for swarm robots back then were as simple as they will be today, as one should expect from swarms:

If light moderate (safe)
Then move toward
If light bright (unsafe)
Then move away
If battery low (hungry)
Then return for charge