Late last year, White House Office of Science and Technology Policy released the Blueprint for an AI Bill of Rights, instantly elevating the topic of responsible AI to the top of leadership agendas across executive branch agencies. While the themes of the blueprint are not entirely new--building on prior work including the AI in Government Act of 2020, a December 2020 executive order on trustworthy AI, and the Federal Privacy Council's Fair Information Practice Principles--the report brings new urgency to ongoing agency efforts to leverage data in ways consistent with our democratic ideals. With a stated goal of supporting "the development of policies and practices that protect civil rights and promote democratic values in the building, deployment and governance of automated systems," the blueprint is rooted in five principles: safe and effective systems; algorithmic discrimination protections; data privacy; notice and explanation; and human alternatives, consideration and fallback. The Blueprint also includes notes on applying the principles and a technical companion to support operationalization. Some agencies that are less mature in their data capabilities might consider the blueprint to be of limited relevance.

Brain tumors are one of the life-threatening forms of cancer. Previous studies have classified brain tumors using deep neural networks. In this paper, we perform the later task using a collaborative deep learning technique, more specifically split learning. Split learning allows collaborative learning via neural networks splitting into two (or more) parts, a client-side network and a server-side network. The client-side is trained to a certain layer called the cut layer. Then, the rest of the training is resumed on the server-side network. Vertical distribution, a method for distributing data among organizations, was implemented where several hospitals hold different attributes of information for the same set of patients. To the best of our knowledge this paper will be the first paper to implement both split learning and vertical distribution for brain tumor classification. Using both techniques, we were able to achieve train and test accuracy greater than 90\% and 70\%, respectively.

How best to incorporate historical data to "warm start" bandit algorithms is an open question: naively initializing reward estimates using all historical samples can suffer from spurious data and imbalanced data coverage, leading to computational and storage issues $\unicode{x2014}$ particularly salient in continuous action spaces. We propose Artificial Replay, a meta-algorithm for incorporating historical data into any arbitrary base bandit algorithm. Artificial Replay uses only a fraction of the historical data compared to a full warm-start approach, while still achieving identical regret for base algorithms that satisfy independence of irrelevant data (IIData), a novel and broadly applicable property that we introduce. We complement these theoretical results with experiments on $K$-armed and continuous combinatorial bandit algorithms, including a green security domain using real poaching data. We show the practical benefits of Artificial Replay, including for base algorithms that do not satisfy IIData.

New York Attorney General Letitia James is asking Madison Square Garden Entertainment Corp. for information related to its alleged use of facial recognition technology to prevent certain ticket holders from entering its venues. The state attorney general's office said Wednesday the company, which operates Madison Square Garden and Radio City Music Hall in New York City, has reportedly used the technology to bar lawyers from firms who are suing the company over unrelated matters from seeing sporting events or concerts.

A picture may be worth a thousand words. But what about a picture generated entirely by a machine? That is the question scholars, advocates, and internet users have been considering lately, as art generated by artificial intelligence (AI) has exploded in popularity. Some commentators have asked who regulates this digitally created art and whether the courts can prevent theft of creative ideas and techniques in the process of its generation. Toward the end of last year, popular use of the Lensa AI app, which generates stylized portraits based on users' uploaded selfies, spurred the latest round of controversy over the ethics of AI-generated art.

More than 280 workers at TCGplayer, a marketplace for trading card games like Magic: The Gathering and Pokémon, are trying to unionize. A supermajority of the workers have filed for a union representation election with the National Labor Relations Board. If their efforts are successful, they'll form the first union at eBay, which bought TCGplayer in 2022 in a deal worth up to $295 million. Employees of several card and tabletop companies have unionized, including Card Kingdom, Bellevue Mox Boarding House, Noble Knight Games and Paizo. The TCGplayer workers are similarly trying to organize with the Communications Workers of America ( CWA), which has also worked with several video game studios in their unionization attempts.

New York Attorney General Letitia James has sent a letter to MSG Entertainment, the owner and operator of Madison Square Garden and Radio City Music Hall, asking for information about its use of facial recognition to deny entry to attorneys at firms representing its legal opponents. James's letter warns that the Orwellian policy may violate local, state and federal human rights laws, including those prohibiting retaliation. MSG Entertainment's facial recognition has been identifying and denying entry to lawyers from firms representing clients suing the company -- whether or not those attorneys are directly involved in the cases. The company, led by CEO James Dolan (who also owns the New York Knicks and Rangers), has defended the policy, framing it as an attempt to prevent evidence collection "outside proper litigation discovery channels." However, lawyers have called that rationale "ludicrous," criticizing the ban as a "transparent effort" to punish attorneys for suing them.

Artificial intelligence company DoNotPay has dropped plans to deploy AI as a lawyer in a real court case for the first time after reportedly being threatened with legal action. The trial on 22 February was to be the first time an AI advised a defendant in court, which the firm's founder, Joshua Browder, told New Scientist would be "technically within the rules, but I don't think it's in the spirit of the rules". The location of the US court hadn't been …

Rep. Brian Fitzpatrick, R-Penn., and Rep. Josh Gottheimer, D-N.J., discuss the latest news emerging from the classified documents seized from President Biden on'Fox News Sunday.' This is a rush transcript of'Fox News Sunday' from January 22nd, 2022. This copy may not be in its final form and may be updated. A new round of classified items found in the president's home and new concerns about financial fallouts as the U.S. hits the debt limit again. JIM CLYBURN (D-SC): We've had these games before and it should not be done. KARINE JEAN-PIERRE, WHITE HOUSE PRESS SECRETARY: The president has been clear on this. It should not be used as a political weapon. BREAM: Swing district, moderate Republicans are calling for the president to drop the take it or leave it approach and come to the table. We'll sit down for a bipartisan conversation with two co-chairs from the Problem Solvers Caucus. Republican Brian Fitzpatrick and Democrat Josh Gottheimer join me to talk about how to find consensus on the debt limit, immigration and more. Then -- thousands of pro-life advocates come to the nation's capital for the first March for Life since the Supreme Court overturned Roe v. Wade. We'll look at the legal state of play now that abortion laws are up to the states, and sit down for a conversation with prominent voices from both sides. And eight months after the unprecedented leak of a draft Supreme Court ruling, there are still no answers from the high court about the leaker. JIM JORDAN (R-OH): The only way you're going to stop this in the future is to make sure you find out who did it and hold them accountable. BREAM: We'll ask our Sunday panel if we will ever find out who did it. Breaking overnight, at least ten people are dead, another ten injured after a mass shooting near Los Angeles. It happened late last night at a dance club in Monterey Park, California, close to where a lunar New York celebration had been taking place. Authorities say they believe the shooter is male and at this time it appears that person is not in custody. Deputies say they are reviewing security video in that area. Monterey Park is about ten miles east of Los Angeles. We'll keep you updated on any developments we get in from there. Also breaking this morning, the Justice Department seized more classified documents from the president's private residence just this week. The news comes as President Biden prepares to speak in person with House Speaker Kevin McCarthy to discuss the new Congress, a range of challenges there, where they disagree. And that, of course, includes the debt limit. Congress is facing a deadline to strike a deal or risk a financial crisis as the Treasury department steps in to avoid a government default.

Social media platforms struggle to protect users from harmful content through content moderation. These platforms have recently leveraged machine learning models to cope with the vast amount of user-generated content daily. Since moderation policies vary depending on countries and types of products, it is common to train and deploy the models per policy. However, this approach is highly inefficient, especially when the policies change, requiring dataset re-labeling and model re-training on the shifted data distribution. To alleviate this cost inefficiency, social media platforms often employ third-party content moderation services that provide prediction scores of multiple subtasks, such as predicting the existence of underage personnel, rude gestures, or weapons, instead of directly providing final moderation decisions. However, making a reliable automated moderation decision from the prediction scores of the multiple subtasks for a specific target policy has not been widely explored yet. In this study, we formulate real-world scenarios of content moderation and introduce a simple yet effective threshold optimization method that searches the optimal thresholds of the multiple subtasks to make a reliable moderation decision in a cost-effective way. Extensive experiments demonstrate that our approach shows better performance in content moderation compared to existing threshold optimization methods and heuristics.