As facial recognition spreads across police forces and retail stores, UK biometrics commissioners are warning that national oversight is lagging far behind the technology’s rapid expansion.
Last year, the Home Office admitted facial recognition cameras were more likely to incorrectly identify black and Asian people than their white counterparts, and women more than men, and there have been conflicting studies on their overall accuracy.
Let’s not overlook yet another insidious part of it:
Home Bargains eventually issued him an apology and a £100 voucher as a “gesture of goodwill without admission”, on the condition that the details of the incident remain confidential. Clayton declined: “I just thought: ‘Really, you’re trying to buy my silence?’”
Not only are they trying to buy his silence, they think they can do it so cheaply. Shows their own world view where they think people will fall over each other trying to sell their rights for a hundred.
and its only 100$ instore credit. hope they can sue to expose them, and then you wonder where they are getting the biases from, select all non-whites as thieves.
At least the deacon who sexually harassed me and tried to buy my silence that time wrote a three or four hundred dollar check, which I promptly cashed, and told the whole neighborhood.
Trying to buy his silence is insulting enough, but they offer a pitiful amount and can’t even be bothered to offer actual currency?
even more insulting it is only instore credit.
The company’s website claims that its system has a 99.98% accuracy rate
99.98% accuracy of the people it flags as shoplifters or 99.98% accuracy overall? And if the latter then what proportion of the population are even shoplifters? Could you achieve similar levels of “accuracy” by saying nobody is a shoplifter? Maybe throw in a few positives here and there to make it look like your product does something other than harass the public?
You’re right to question this.
In machine learning Accuracy means the correct % of overall classifications. There’s some other terms like:
- Precision which is the % of correctly identified positives divided by the number of positive classifications. A high precision score would mean that of everyone who flagged as a match you had relatively few who were not actual shoplifters.
- Recall (true positive rate) which is the % of correctly identified positives divided by all actual positives. A high recall score measures how many shoplifters you caught and would minimize false negatives, but at the cost of more false positives.
So in the case of classification of shoplifters ideally you would focus on Precision as false positives are undesired, but if a company doesn’t care about false positives as much as getting the shoplifters they’d focus on Recall. In either event, Accuracy is a poor metric to use or advertise in an imbalanced data set like shoplifting as most customers are not shoplifters so even if the model didn’t classify anyone as a shoplifter they’d still be 99+% accurate.
I suppose all we can do is refuse to shop in the stores that use this. But, we need to do it now. Because, soon, every store will use this, and we won’t have a choice.
I suppose if they keep getting false positives, they’ll make some change, but who knows?
just cut their internet from the outside before going in… then pay cash. you know none of this is being ran locally
maybe there are EM jammers or some electronic warfare stuff we can use.
How do you do that?
snip snip
Isn’t the cable underground? How do I find it? How do I know which one is it? How do I access it? How do I do it without anyone noticing?
not my problem
The first human that exercises the will of a damn clanker on me is going to get pummeled into the ground. You are a human, if you listen to a probability machine, you yourself are playing in the world of non-deterministic behavior.
