A.I. IN THE NEWS
U.K. data privacy watchdog considers fining Clearview AI. The U.K. Information Commissioner's Office put out a statement on Monday saying it was considering fining the controversial New York-based facial recognition software company £17 million ($22.6 million) over its alleged harvesting of vast troves of facial images, including those of U.K. citizens, from social media without consent. “I have significant concerns that personal data was processed in a way that nobody in the UK will have expected,” Elizabeth Denham, the Information Commissioner, said in a statement. Clearview's lawyers told tech publication The Register that Denham's "assertions are factually and legally incorrect.”
Contract lawyers, monitored at home by A.I. software, complain about its faults. Law firms are using A.I. facial recognition and other computer vision software to safeguard sensitive documents and monitor the conduct of contract lawyers. These lawyers frequently work from home, especially since the COVID-19 pandemic. So law firms have tried to find ways to remotely ensure they are doing what they say and that they are handling confidential client documents securely. But The Washington Post spoke to dozens of contract lawyers across the U.S. who said the A.I. software was faulty, failing to correctly register their face, especially when the contract attorney was Black, and that this often prevented them from working. Even when the software worked correctly, it was "dehumanizing" to be constantly surveilled, the lawyers told the newspaper.
Dark-skinned people are poorly represented in image datasets used to train skin cancer identification A.I. That was the conclusion of a study published in Lancet Digital Health earlier this month. Of more than 38 open access information sources the researchers examined, the vast majority included no information about the ethnicity of the patients from which the skin lesion images had been taken. Only 1.3% of the images had ethnicity data and only 2.1% had skin type information. Of the 2,436 images where skin type was listed, just 10 images were from individuals with brown skin and only a single image was from someone with dark brown skin. In the images listing ethnicity, there were no images from people of African, Afro-Caribbean, or South Asian background, the study found. This matters because A.I. systems tend to perform far worse when faced with examples that were not well-represented in their training data.
Alarm about autonomous weapons is growing. Stuart Russell, a computer scientist who is among the leading thinkers on how to mitigate the risks associated with more powerful A.I., will use a series of prestigious public lectures, broadcast on BBC Radio, to argue against the growing development of autonomous weapons systems, The Financial Times reported. Campaigners who are hoping to secure a United Nations ban on such weapons are accelerating their efforts ahead of a key UN meeting in Geneva next month, at which international regulation of A.I. weapons systems will be debated. There are also growing indications that these weapons are not only being developed, but are being deployed in actual combat. Russell told the newspaper, however, that the U.S., Russia, the U.K., Israel, and Australia all continue to oppose a ban.
EYE ON A.I. TALENT
Kubient, a New York-based digital advertising software company, has hired Mitchell Berg to be its chief technology officer, the company said in a statement. Berg had been CTO at ad tech company Koddi.
Finitive, the New York-based credit marketplace, has hired Steve Yampolsky as head of engineering, the company said in a statement. He was previously at BNY Mellon. The company also said it was hiring Chris Benjamin as principal software architect. Benjamin was previously at CRE Simple.
EYE ON A.I. RESEARCH
A new foundational computer vision model from Microsoft. A large team of researchers from Microsoft have created a massive new multimodal language understanding and computer vision system, which they call Florence, that believe can be "foundational"—the idea of a single system that can underpin a wide variety of complex tasks without much additional training.
While other A.I. research groups have also recently developed these kind of massive, foundational multimodal combined language and image algorithms (OpenAI has CLIP, Google has ALIGN, and the Beijing Academy of Artificial Intelligence has Wu Dao), Microsoft says in a paper, published on the non-peer reviewed research repository arxiv.org, that Florence does a few things these others cannot: it can identify individual objects in an image or video, not just understand the overall scene; it can analyze and understand video, not just still images; and it works with captions and three-dimensional context in images, not just pixel-level two-dimensional image understanding.
The system was trained on 900 million image-text pairs and the neural network takes in some 893 million different parameters. That sounds like a lot, but is considerably smaller than many comparable foundational systems. The smaller model should make it easier and less expensive to train. The researchers said Florence achieves top marks on "the majority" of 44 different computer vision benchmark tests.
FORTUNE ON A.I.
Who is Parag Agrawal, Twitter’s new CEO?—by Felicia Hou
An autonomous Mayflower aims to prove A.I.’s captain skills by sailing in the Pilgrims’ wake—by Jeremy Kahn
Rise of the (fast food) robots: How labor shortages are accelerating automation—Commentary by Michael Joseph
The U.K.'s new algorithmic transparency standard looks great on paper, but the test will be how it works in practice.
This past week the British government debuted a new standard on algorithmic transparency, joining France and the Netherlands in becoming one of the first countries in the world to do so. The standard will be piloted by a few government departments and then be rolled out more broadly across the U.K. public sector in 2022. While the rules only apply to the government, they may serve as a model for future private sector regulation too. And, as nascent U.S. efforts to regulate A.I. have often taken inspiration from examples in Europe, it is worth looking at what the new standard says.
The standard requires government departments deploying algorithms to make publicly available a lot of information about that system: what kind of algorithm is it; how is it intended to work (and critically, what is the algorithm not intended to do); and what data has been used to train or validate it. It asks the department to list whether it has carried out impact assessments for data protection, the effect of the algorithm itself, the ethics of deploying the system and its impact on equality. Most importantly, it asks departments to list what it sees as the risks associated with the use of that algorithm and what steps have been taken to mitigate those risks.
The new standard is being deployed after the U.K. experienced some significant snafus with algorithmic decision-making. Perhaps the biggest fiasco was the use of an algorithm to award “A-level grades” (a crucial exam that is used for university admissions) to all of the nation’s high school students based on their “predicted results,” after the COVID-19 pandemic forced the cancellation of the actual exams. That algorithm was highly opaque—at least initially, the government provided very little information about how it functioned. The grades the algorithm provided wound up exacerbating social and economic disparities by placing a heavy weight on the historical average grades that a students’ school had produced. After a public outcry, the government voided all the grades, leading to yet more criticism and recrimination.
Developed in connection with the U.K. Center for Data Ethics and Innovation, the new standard certainly seems like an important step towards algorithmic accountability in the public sphere. My only concern in looking at the template form that government has helpfully provided to help agencies comply with the new standard is that, without oversight, complying with the standard could simply become a “check-the-box” exercise, providing little meaningful public ability to assess and question how algorithms are being developed and deployed. We’ll see what happens when the rule comes into practice. Watch this space.