Carnegie Mellon University researchers are challenging a long-held assumption that there is a trade-off between accuracy and fairness when using machine learning to make public policy decisions. As the use of machine learning has increased in areas such as criminal justice, hiring, health care delivery and social service interventions, concerns have grown over whether such applications introduce new or amplify existing inequities, especially among racial minorities and people with economic disadvantages. To guard against this bias, adjustments are made to the data, labels, model training, scoring systems and other aspects of the machine learning system. The underlying theoretical assumption is that these adjustments make the system less accurate. A CMU team aims to dispel that assumption in a new study, recently published in Nature Machine Intelligence.

Like transformative technologies that came before, the ethics of AI is coming under increased scrutiny, giving birth to regulations and policies constraining the scope of its application. Artificial Intelligence is a dynamic technology sector, powering a broad spectrum of emerging applications in the fields of industrial robotics and robotic process automation (RPA). Like transformative technologies that came before, the ethics of AI is coming under increased scrutiny, giving birth to regulations and policies constraining the scope of its application. What are the risks and what's being done to promote an ethical application of one of today's most empowering technologies? What exactly is AI and what role does it play in robotic technologies?

The government is undertaking research to explore how AI R&D is successfully commercialised and brought to market. The Department for Digital, Culture, Media and Sport (DCMS), along with the Office for Artificial Intelligence and Digital Standards and Internet Governance (DSIG), are leading the research project. Research consultants Oxford Insights and Cambridge Econometrics have been commissioned with exploring the ways'technology transfer' happens for AI, and are seeking to conduct interviews with those with knowledge of the industry. Oxford Insights and Cambridge Econometrics would like to speak individuals with experience and knowledge of the AI development ecosystem, Innovate UK and other funding programmes, Standards Developing Organisations (SDOs), AI patents, AI R&D in the public and private sectors, AI funding and Venture Capital, and AI policy. Our interviews will take approximately 45 mins -1 hour; however, we are happy to accommodate if time doesn't permit this length of interview.

New York City has launched its first artificial intelligence (AI) strategy, with an emphasis on digital ethics. It aims to help the city establish a shared understanding of AI and capitalise on the benefits while managing the risks. The 116-page AI Strategy focuses on how to use AI to better serve residents; building AI know-how within government; modernising data infrastructure; city governance and policy around AI; developing partnerships with external organisations; and promoting equitable access to opportunities. It is the latest in a series of initiatives that aim to make New York City'future-ready', following on from the IoT Strategy and the Internet Master Plan. "As a global epicentre of innovation, New York City has a key role to play in shaping the future of AI," said New York City Chief Technology Officer, John Paul Farmer.

Governments and agencies are struggling to coordinate effective disaster relief programs. Artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) can help. Natural disasters wreak havoc around the world every year. But it's hard to appreciate the scale of this damage. And that doesn't include the Northern California Wildfires or the biggest hurricanes.

As AI makes dramatic inroads in enterprises, the U.S. government has quietly started to regulate the use of AI in the consumer credit industry and other areas by banning the use of biased and unexplainable algorithms in decisions that affect consumers. In recent years, the Federal Trade Commission has tried to regulate AI in lending with laws that are already in place, chief among them the Fair Credit Reporting Act (FCRA). The federal agency has also included AI regulation under the FTC Act and the Equal Credit Opportunity Act (ECOA). While the federal laws don't contain explicit language regulating AI, the FTC, which enforces the laws, has issued guidance over the last two years stipulating that under the FCRA, lenders can't use biased or unexplainable algorithms for not only consumer credit, but also employment, housing, insurance and other benefits. The FTC has also clarified that the sale or use of racially biased algorithms, for example, is a deceptive practice banned by the FTC Act.

In this 21st century, AI is set to protect astronaut health from cosmic radiation through the recent collaboration of NASA Frontier Development Lab (FDL) and Intel known as Intel AI Mentor. It has developed the first-ever AI algorithm that needs a combination of radiation exposure data from humans as well as mice for providing biomarkers. Biomarkers are known for measuring the different stages of cancer progression efficiently and effectively. NASA FDL needed to formalize how casual machine learning models work on datasets in different environments and locations without physically moving the datasets. Intel AI Mentor can utilize custom AI algorithms for a better understanding of astronaut health and multiple ways of further improvement.

After years of complaints that there were no rules to determine what constitutes a "sustainable" investment, investors are now fretting that there will soon be too many to navigate easily. More than 30 taxonomies outlining what is and isn't a green investment are being compiled by governments across Asia, Europe and Latin America, each one reflecting national economic idiosyncrasies that can jar with a global capital market that has seen trillions pour into sustainable funds. The European Union will introduce its green investment taxonomy, or common framework, in January to help asset managers inside the bloc and make green activities more visible and attractive to investors. The rules also aim to stamp out "green washing," whereby organizations overstate their environmental credentials. The U.K., which hosts the COP26 climate change conference from Oct. 31, is set to finalize its own taxonomy next year but has already signaled it will not just replicate what is drawn up across the channel.

Yahoo Japan Corp. said Tuesday that it has introduced a function to automatically shut comment sections of its news article site when defamatory posts are detected. Using artificial intelligence, the unit of Z Holdings Corp. will make it impossible for users to view or submit new comments based on the number of posts violating the site's policies and other criteria. The new function was introduced to coincide with the start of the official campaign period for the Oct. 31 Lower House election. A message urging users to take care not to make illegal posts on political news articles will be displayed during the campaign period. So far, Yahoo Japan has deleted the posts it has judged to be defamatory and suspended posts by users who repeatedly violated the site's comment policy.

A hallmark of artificial intelligence is its ability to learn from the past. As researchers advance and refine AI applications, it could increasingly become part of routine research, too--the type of work that supported the advances toward tackling this pandemic and can support the response to a future one, too. Finding meaning in a sea of messy or incomplete data is precisely what data scientists at Pacific Northwest National Laboratory (PNNL) do. With expertise in applying graph-based machine learning, detailed molecular modeling, and explainable AI to questions of national security and basic science, PNNL researchers are now turning their artificial intelligence tools to the study of fundamental questions about treatments for COVID. What they are learning sharpens the tools available in the computational toolbox for responding quickly to a future pandemic.