In a Cornell China Center (CCC) webinar held on May 27, legal scholars based in China, Switzerland, and the United States surveyed artificial intelligence (AI) regulation across the world, identifying strategic similarities and local distinctions. The event brought together more than 150 attendees across time zones for a conversation spanning intellectual property, disability rights, and global regulation benchmarks. "We all face some common challenges," said Rui Guo (Renmin University of China). Guo, a law professor whose research focuses on stereotypes and AI fairness, was one of four panelists addressing the complex challenges that AI introduces within societies at both the local and global levels. "Some of the more local problems, like stereotypes in one society, may be intensified in a new technological context that may need the local to be responding more actively," said Guo. "I think both local and global regulation are needed to deal with the bigger challenges."

In modern societies, law is one of the main tools to regulate human activities. These activities are constantly changing, and law co-evolves with them. In the past decades, human activities have become increasingly differentiated and intertwined, e.g., in developments described as globalization or digitization. Consequently, legal rules, too, have grown more complex, and statutes and regulations have increased in volume, interconnectivity, and hierarchical structure (Katz et al. 2020; Coupette et al. 2021a). A similar trend can be observed in software engineering, albeit on a much shorter time scale.

Microsoft has pledged to clamp down on access to AI tools designed to predict emotions, gender, and age from images, and will restrict the usage of its facial recognition and generative audio models in Azure. The Windows giant made the promise on Tuesday while also sharing its so-called Responsible AI Standard, a document [PDF] in which the US corporation vowed to minimize any harm inflicted by its machine-learning software. This pledge included assurances that the biz will assess the impact of its technologies, document models' data and capabilities, and enforce stricter use guidelines. This is needed because – and let's just check the notes here – there are apparently not enough laws yet regulating machine-learning technology use. Thus, in the absence of this legislation, Microsoft will just have to force itself to do the right thing.

Researchers have warned against possible harms from AI that processes and generates text.Credit: Getty An international team of around 1,000 largely academic volunteers has tried to break big tech's stranglehold on natural-language processing and reduce its harms. Trained with US$7-million-worth of publicly funded computing time, the BLOOM language model will rival in scale those made by firms Google and OpenAI, but will be open-source. BLOOM will also be the first model of its scale to be multilingual. The collaboration, called BigScience, launched an early version of the model on 17 June, and hopes that it will ultimately help to reduce harmful outputs of artificial intelligence (AI) language systems. Models that recognize and generate language are increasingly used by big tech firms in applications from chat bots to translators, and can sound so eerily human that a Google engineer this month claimed that the firm's AI model was sentient (Google strongly denies that the AI possesses sentience).

Silicon Valley is abuzz about artificial intelligence - software programs that can draw or illustrate or chat almost like a person. One Google engineer actually thought a computer program had gained sentience. A lot of AI experts, though, say there is no ghost in the machine. But what if it were true? That would introduce many legal and ethical questions.

Much has changed in the AI/ML world but the concept of'garbage in; garbage out' remains stoic. Any algorithm is only as good as its training data. And, no training data is without bias, not even the ones generated through automation. In the past, many machine learning algorithms have been unfair to certain religions, races, genders, ethnicities, and economical statuses, among others. The Watson supercomputer from IBM that gave suggestions to doctors using a dataset of medical research papers was found to favor reputable studies only. Amazon's recruiting algorithm was found to favor men over women.

A new study claims robots exhibit racist and sexist stereotyping when the artificial intelligence (AI) that powers them is modeled on data from the internet. The study, which researchers say is the first to prove the concept, was led by Johns Hopkins University, the Georgia Institute of Technology, and the University of Washington, and published by the Association for Computing Machinery (ACM). Researchers will be initially presenting their findings at the 2022 Conference on Fairness, Accountability, and Transparency, which is being held in South Korea. This isn't the first time exposure to the internet has left AI with bigoted views. Back in 2016, Microsoft launched an AI named Tay.

The US Supreme Court on Tuesday declined an appeal from Bayer-owned Monsanto that aimed to challenge thousands of lawsuits claiming its weedkiller Roundup causes cancer -- a potentially costly ruling. The high court did not explain its decision not to take the case, which left intact a $25 million ruling in favor of a California man who alleged he developed cancer after using the chemical for years. The decision marks a major blow to the German conglomerate's legal fight against some 31,000 Roundup-related cases. "Bayer respectfully disagrees with the Supreme Court's decision," the company said in a statement. "The company believes that the decision undermines the ability of companies to rely on official actions taken by expert regulatory agencies," it added, referring to a 2020 federal finding that Roundup's active ingredient is not risky.

The work, led by Johns Hopkins University, Georgia Institute of Technology, and University of Washington researchers, is believed to be the first to show that robots loaded with an accepted and widely-used model operate with significant gender and racial biases. The work is set to be presented and published this week at the 2022 Conference on Fairness, Accountability, and Transparency (ACM FAccT). "The robot has learned toxic stereotypes through these flawed neural network models," said author Andrew Hundt, a postdoctoral fellow at Georgia Tech who co-conducted the work as a PhD student working in Johns Hopkins' Computational Interaction and Robotics Laboratory. "We're at risk of creating a generation of racist and sexist robots but people and organizations have decided it's OK to create these products without addressing the issues." Those building artificial intelligence models to recognize humans and objects often turn to vast datasets available for free on the Internet.

Meta Platforms Inc. will change its ad delivery system to address concerns that it violates the Fair Housing Act by discriminating against users, as part of a settlement with a federal regulator. The accord resolves a lawsuit by the US Department of Housing and Urban Development alleging that the algorithms used in Meta's advertising systems allowed marketers to violate fair housing laws by limiting or blocking certain groups of people from seeing housing ads on the service. "Because of this ground-breaking lawsuit, Meta will--for the first time--change its ad delivery system to address algorithmic discrimination," Manhattan US Attorney Damian Williams said in a statement. Meta said Tuesday that it built machine learning technology to ensure that ads reach people that reflect the overall potential audience for a particular ad, and not just a subset of that group. In a blog post, Meta wrote that it will "work to ensure the age, gender and estimated race or ethnicity of a housing ad's overall audience matches the age, gender, and estimated race or ethnicity mix of the population eligible to see that ad."