The world is heading for a'wild west' free for all in space that could lead to disaster as firms fight for every scrap of resource in low Earth orbit, an expert has warned. Paul Kostek, a space policy specialist from the Institute of Electrical and Electronics Engineers (IEEE), says global agreement is'highly unlikely' anytime soon. As the commercial space sector continues to grow at a rapid rate, firms are vying to launch constellations of satellites and new experimental craft into low Earth orbit. This has left astronomers frustrated and struggling due to'blots in the sky' that make observations harder and less accurate, as well as concerns over space junk. The need for regulation has become more pressing after satellites from OneWeb and SpaceX Starlink came close to hitting each other last week.

Williams's wrongful arrest, which was first reported by the New York Times in August 2020, was based on a bad match from the Detroit Police Department's facial recognition system. Two more instances of false arrests have since been made public. Both are also Black men, and both have taken legal action to try rectifying the situation. Now Williams is following in their path and going further--not only by suing the Detroit Police for his wrongful arrest, but by trying to get the technology banned. On Tuesday, the ACLU and the University of Michigan Law School's Civil Rights Litigation Initiative filed a lawsuit on behalf of Williams, alleging that his arrest violated Williams's Fourth Amendment rights and was in defiance of Michigan's civil rights law.

Google has worked for years to position itself as a responsible steward of AI. Its research lab hires respected academics, publishes groundbreaking papers, and steers the agenda at the field's biggest conferences. But now its reputation has been badly, perhaps irreversibly damaged, just as the company is struggling to put a politically palatable face on its empire of data. The company's decision to fire Timnit Gebru and Margaret Mitchell -- two of its top AI ethics researchers, who happened to be examining the downsides of technology integral to Google's search products -- has triggered waves of protest. Academics have registered their discontent in various ways.

This paper develops a natural-language agent-based model of argumentation (ABMA). Its artificial deliberative agents (ADAs) are constructed with the help of so-called neural language models recently developed in AI and computational linguistics. ADAs are equipped with a minimalist belief system and may generate and submit novel contributions to a conversation. The natural-language ABMA allows us to simulate collective deliberation in English, i.e. with arguments, reasons, and claims themselves -- rather than with their mathematical representations (as in formal models). This paper uses the natural-language ABMA to test the robustness of formal reason-balancing models of argumentation [Maes & Flache 2013, Singer et al. 2019]: First of all, as long as ADAs remain passive, confirmation bias and homophily updating trigger polarization, which is consistent with results from formal models. However, once ADAs start to actively generate new contributions, the evolution of a conservation is dominated by properties of the agents *as authors*. This suggests that the creation of new arguments, reasons, and claims critically affects a conversation and is of pivotal importance for understanding the dynamics of collective deliberation. The paper closes by pointing out further fruitful applications of the model and challenges for future research.

Abuse on the Internet is an important societal problem of our time. Millions of Internet users face harassment, racism, personal attacks, and other types of abuse across various platforms. The psychological effects of abuse on individuals can be profound and lasting. Consequently, over the past few years, there has been a substantial research effort towards automated abusive language detection in the field of NLP. In this position paper, we discuss the role that modeling of users and online communities plays in abuse detection. Specifically, we review and analyze the state of the art methods that leverage user or community information to enhance the understanding and detection of abusive language. We then explore the ethical challenges of incorporating user and community information, laying out considerations to guide future research. Finally, we address the topic of explainability in abusive language detection, proposing properties that an explainable method should aim to exhibit. We describe how user and community information can facilitate the realization of these properties and discuss the effective operationalization of explainability in view of the properties.

The Detroit department is also among hundreds of police agencies that have used Clearview AI, a facial recognition tool that searches through a large database of photos taken from across the Internet, according to a BuzzFeed News report earlier this month based on data from a confidential source. Neither the Detroit police nor Clearview have confirmed the report, and it does not appear Clearview was used in Williams's case.

Several European artificial intelligence projects rely on race without explicitly saying so. In February, El Confidencial revealed that Renfe, the Spanish railways operator, published a public tender for a system of cameras that could automatically analyze the behavior of passengers on train platforms. One characteristic that the system should be able to assess was "ethnic origin". Ethnic origin can mean many things. But in the context of an automated system that assigns a category to people based on their appearance captured by camera the term is misleading.

Government regulations and new privacy rules instituted by Apple Inc. and Alphabet Inc.'s Google will limit the ability of advertisers, publishers and tech companies to track users and target ads based on identifiable data. Going forward, marketers will have to revamp their data strategies, while adtech vendors and other companies are hurrying to find alternative ways to reach consumers. One such alternative is contextual advertising: targeting ads by placing them close to relevant content. GumGum uses computer-vision and natural-language processing technologies to scan and analyze text, images, video and audio to help match ads to content. CMO Today delivers the most important news of the day for media and marketing professionals.

"Investors for the most part are familiar with whistleblowing systems and protections and really prize whistleblower protections because, at the end of the day, they do protect long-term investors like Trillium," said Jonas Kron, the firm's chief advocacy officer. The Boston-based firm's proposal, which was submitted in December and could be voted on during Alphabet's shareholder meeting this year, cited several examples of alleged retaliation against workers, saying the incidents were red flags about potential internal problems related to culture, ethics and human rights. Trillium's proposal calls for the company to make the report public. Our Morning Risk Report features insights and news on governance, risk and compliance. A spokeswoman for Alphabet declined to comment.

When Jerrel Gantt was released from prison after three years, he was handed a pamphlet about healthcare and nothing else. He began searching for employment, a deep source of anxiety for him, and secured housing through a ministry in New York City. He later enrolled in school part-time. As he settled into life outside of prison and developed a support system, Gantt began going on dates with people he met on apps like Tinder. The process has not been without challenges – revealing that he is formerly incarcerated usually comes up early in the dating process for Gantt.