The Association for Computing Machinery (ACM) U.S. Technology Policy Committee (USTPC) released a statement on 30 June calling for "an immediate suspension of the current and future private and governmental use of FR [facial recognition] technologies in all circumstances known or reasonably foreseeable to be prejudicial to established human and legal rights." The Committee concludes that, when rigorously evaluated, the technology too often produces results demonstrating clear bias based on ethnic, racial, gender, and other human characteristics recognizable by computer systems. The consequences of such bias, USTPC notes, frequently can and do extend well beyond inconvenience to profound injury, particularly to the lives, livelihoods and fundamental rights of individuals in specific demographic groups, including some of the most vulnerable populations in our society. Such bias and its effects are scientifically and socially unacceptable. The USTPC find that, at present, facial recognition technology is not sufficiently mature and reliable to be used fairly and safely.

The UK's Competition and Markets Authority (CMA) has called on the UK government to create "a new pro-competition regulatory regime" that can control Facebook, Google and other technology companies that are primarily funded by digital advertising. The non-ministerial department has completed a study announced last July and concluded that "existing laws are not suitable for effective regulation." To combat the problem, it's recommending that a new Digital Markets Unit be set up with major oversight and powers. The Unit was first proposed in a report published by the Digital Competition Expert Panel (DCEP) -- a group chaired by Professor Jason Furman, a former chief economist when Barack Obama was president -- in March 2019. The CMA believes it should have a code of conduct that ensures Facebook and Google don't veer into "exploitative or exclusionary practices," or do anything that is likely to reduce public trust and transparency.

Smart IT systems are now calculating claims costs and attributing fault for accidents without any human involvement, speeding up the resolution of claims. Technology is set to transform motor insurance in the next five to 10 years, revolutionising both the claims process and repair. Artificial intelligence (AI) is enabling insurers to evaluate vehicle damage at the scene of a collision, without the need for a claims handler or loss adjustor. By analysing millions of photos of vehicle damage and cross-referencing them with actual repairs, programmers have been able to create algorithms that can assess the scale of the damage and create a full estimate including recommended repair, paint, parts costs and labour hours. The system can determine, for example, whether body panels can be repaired or need replacing, and in worse case scenarios it ensures that no total losses are sent to bodyshops.

Organising ethical debates has long been an efficient way for industry to delay and avoid hard regulation. Europe now needs strong, enforceable rights for its citizens, writes Green MEP Alexandra Geese. If the rules are too weak, there is a too great a risk that our rights and freedoms will be undermined: This currently applies to all applications of artificial intelligence, which up to now have only been based on non-binding ethical principles and values. In this legislation, Europe has the chance to adopt a legal framework for AI with clear rules. We need strong instruments to protect our fundamental rights and democracy.

Earlier this month an article in the Financial Times by John Thornhill, the paper's innovation editor, caught my attention. Thornhill was relaying an intriguing set of ideas expressed by the authors of a new book, What To Do When Machines Do Everything? Before discussing the future impact of today's unfolding industrial innovations such as driverless cars, robotic surgery, precision agriculture, or automated beer service (as in the photo above), the three authors – Malcolm Frank, Paul Roehrig, and Ben Pring – make their first key point, citing the example of an early 19th century innovation that enabled an entire industry that generates $620bn. in annual revenues today. What could this invention have been – The steam engine? Theoretically, you might expect not be too far off with any one of these answers, but in fact the invention in question was … the lawnmower.

Recently, Microsoft announced that it was terminating dozens of journalists and editorial workers at its Microsoft News and MSN organizations. Instead, the company said, it will rely on artificial intelligence to curate and edit news and content that is presented on MSN.com, inside Microsoft's Edge browser, and in the company's Microsoft News apps. Explaining the decision, Microsoft issued a statement to the Verge. The statement reads: "Like all companies, we evaluate our business on a regular basis. This can result in increased investment in some places and, from time to time, re-deployment in others. These decisions are not the result of the current pandemic."

Turn around in almost any city, and you're likely to see a bank or lender or brokerage on the corner. In fact, in my family's small town, we have two financial institutions by the same name on either side of a two-lane street. And, while I love the personal experience I get from visiting my hometown banker, I also appreciate being able to conduct my business after the bankers have gone home to dinner, and knowing that my fraud protection never sleeps. Financial services institutions (FSIs) of all sizes recognize that they are in fierce competition to deliver differentiated services while meeting stringent regulatory and compliance requirements. Among the earliest adopters of digital transformation, FSIs satisfy these requirements with a range of emerging technologies, including artificial intelligence (AI).

First, the European Union's General Data Protection Regulation (GDPR) provides that people have a right to "meaningful information" about the logic behind automated decisions using their data. This law, in an interesting and potentially radical way, seems to mandate that any automated decision-making that people are subject to should be explainable to the person affected. That got me wondering: What does that mean? How do we implement that? And what does explanation really mean here?

Black Lives Matter is reverberating around the world, triggering a fresh reckoning with the racist global history of colonialism and slavery. While Confederate statues began to tumble across the American South, in Bristol, England, a diverse group felled a statue of a slave trader that has long provoked offense. Statues of colonial conquerors of Africa and South Asia have followed, along with a robust discussion of the ways in which such actions make history rather than erase it. These movements abroad are not merely echoes of BLM; BLM itself is global. The shared impetus is a common opposition to racism, of which anti-Black racism has been the most lethal and traumatic.

There is an interesting appeal listed to be heard in the Patents Court in July. Professor Ryan Abbott of Surrey University wants the patent system to acknowledge machines are inventors. As part of the Artificial Inventor Project, he is seeking patents for inventions made by DABUS (pronounced'DA-BUS'). DABUS, a'creativity machine', is a series of neural networks and was created and is owned by Dr Stephen Thaler. DABUS can be provided information on a particular topic in order to independently create inventions.