Some companies agree to not use location data from “sensitive points of interest”

A subset of Network Advertising Initiative companies have voluntarily agreed that they will not use location data associated with “sensitive points of interest,” which include:

Places of religious worship

Correctional facilities

Places that may be used to infer an LGBTQ+ identification

Places that may be used to infer engagement with explicit sexual content, material, or acts

Places primarily intended to be occupied by children under 16

Domestic abuse shelters, including rape crisis centers

Welfare or homeless shelters and halfway houses

Dependency or addiction treatment centers

Medical facilities that cater predominantly to sensitive conditions, such as cancer centers, HIV/ AIDS, fertility or abortion clinics, mental health treatment facilities, or emergency room trauma centers

Places that may be used to infer refugee or immigrant status, such as refugee or immigration centers and immigration services`

Credit repair, debt services, bankruptcy services, or payday lending institutions

Temporary places of assembly such as political rallies, marches, or protests, during the times that such rallies, marches, or protests take place

Military bases

NAI PRECISE LOCATION INFORMATION SOLUTION PROVIDER VOLUNTARY ENHANCED STANDARDS

The announcement is close behind increasing public concern that location data brokers might intentionally or reluctantly provide data on individuals visiting abortion clinics.

Facebook settles housing discrimination lawsuit

In 2019, Facebook was sued for housing discrimination because their machine learning advertising algorithm functioned “just like an advertiser who intentionally targets or excludes users based on their protected class.”

They have now settled the lawsuit by agreeing to scrap the algorithm:

Under the settlement, Meta will stop using an advertising tool for housing ads (known as the “Special Ad Audience” tool) which, according to the complaint, relies on a discriminatory algorithm to find users who “look like” other users based on FHA-protected characteristics.  Meta also will develop a new system over the next six months to address racial and other disparities caused by its use of personalization algorithms in its ad delivery system for housing ads.  If the United States concludes that the new system adequately addresses the discriminatory delivery of housing ads, then Meta will implement the system, which will be subject to Department of Justice approval and court oversight.  If the United States concludes that the new system is insufficient to address algorithmic discrimination in the delivery of housing ads, then the settlement agreement will be terminated.

United States Attorney Resolves Groundbreaking Suit Against Meta Platforms, Inc., Formerly Known As Facebook, To Address Discriminatory Advertising For Housing

Government lawyers will need to approve Meta’s new algorithm, and Meta was fined $115,054, “the maximum penalty available under the Fair Housing Act.”

The DOJ’s press release states: “This settlement marks the first time that Meta will be subject to court oversight for its ad targeting and delivery system.”

Microsoft discontinues face, gender, and age analysis tools

Kashmir Hill for the NYT:

“We’re taking concrete steps to live up to our A.I. principles,” said Ms. Crampton, who has worked as a lawyer at Microsoft for 11 years and joined the ethical A.I. group in 2018. “It’s going to be a huge journey.”

Microsoft Plans to Eliminate Face Analysis Tools in Push for ‘Responsible A.I.’

This coincides with Microsoft’s release of their Microsoft Responsible AI Standard, v2 (see also blog post).

Note, however, that these tools may have been useful for accessibility:

The age and gender analysis tools being eliminated — along with other tools to detect facial attributes such as hair and smile — could be useful to interpret visual images for blind or low-vision people, for example, but the company decided it was problematic to make the profiling tools generally available to the public, Ms. Crampton said.

Trade-offs everywhere.

People don’t reason well about robots

Andrew Keane Woods in the University of Colorado Law Review:

[D]octors continue to privilege their own intuitions over automated decision-making aids. Since Meehl’s time, a growing body of social psychology scholarship has offered an explanation: bias against nonhuman decision-makers…. As Jack Balkin notes, “When we talk about robots, or AI agents, or algorithms, we usually focus on whether they cause problems or threats. But in most cases, the problem isn’t the robots. It’s the humans.”

Robophobia

Making decisions that go against our own instincts is very difficult (see also List of cognitive biases), and relying on data and algorithms is no different.

A major challenge of AI ethics is figuring out when to trust the AI’s.

Andrew Keane Woods suggests (1) defaulting to use of AI’s; (2) anthropomorphizing machines to encourage us to treat them as fellow decision-makers; (3) educating against robophobia; and perhaps most dramatically (4) banning humans from the loop. 😲

AI model predicts who will become homeless

 EMILY ALPERT REYES for the LA Times:

It pulls data from eight county agencies to pinpoint whom to assist, looking at a broad range of data in county systems: Who has landed in the emergency room. Who has been booked in jail. Who has suffered a psychiatric crisis that led to hospitalization. Who has gotten cash aid or food benefits — and who has listed a county office as their “home address” for such programs, an indicator that often means they were homeless at the time.

A computer model predicts who will become homeless in L.A. Then these workers step in

That’s a lot of sensitive personal data. The word “privacy” does not appear in the article.

Data is of course exceptionally helpful in making sure money and resources are applied efficiently. (See also personalized advertising.)

This seems great, so… ok?

Blowing past the Turing Test

Nitasha Tiku for the Washington Post:

“I know a person when I talk to it,” said Lemoine, who can swing from sentimental to insistent about the AI. “It doesn’t matter whether they have a brain made of meat in their head. Or if they have a billion lines of code. I talk to them. And I hear what they have to say, and that is how I decide what is and isn’t a person.” He concluded LaMDA was a person in his capacity as a priest, not a scientist, and then tried to conduct experiments to prove it, he said.

The Google engineer who thinks the company’s AI has come to life

The actual Turing Test has been met for quite some time, though it didn’t lead to a pronouncement of artificial sentience in the way envisioned by Alan Turing himself.

But maybe we are now at the uncanny valley of sentience: it looks similar enough to make you feel uneasy.

Physical neural networks are very fast

CHARLES Q. CHOI writing for IEEE Spectrum:

In a new study, researchers have developed a photonic deep neural network that can directly analyze images without the need for a clock, sensor, or large memory modules. It can classify an image in less than 570 picoseconds, which is comparable with a single clock cycle in state-of-the-art microchips.

“It can classify nearly 2 billion images per second,” says study senior author Firooz Aflatouni, an electrical engineer at the University of Pennsylvania, in Philadelphia.

Photonic Chip Performs Image Recognition at the Speed of Light

There appears to be an increasing amount of research activity in the field of physical neural networks.

And it’s not just optics. Researchers are using vibrations, voltages, lasers, etc.

A future for “prompt engineers”?

Two Minute Papers highlights the incredible achievements of OpenAI’s DALL-E 2 in a video that includes a “photograph of Darth Vader as a robot ant”:

Crafting the right prompt can yield remarkable results:

Two Minute Papers points out this could be a new field of “prompt engineering”:

This is an AI where a vast body of knowledge lies within, but it only emerges if we can bring it out with properly written prompts. It almost feels like a new kind of programming that is open to everyone, even people without any programming or technical knowledge. This is prompt engineering if you will. Perhaps a new kind of job that is just coming into existence.

OpenAI’s DALL-E 2: Even More Beautiful Results! 🤯 at 5:50