Newly emerging artificial intelligence (AI) technologies could hold a promising solution to streamlining certain employment practices and processes of hiring applicants in a number of different industries. Historically, both federal courts and regulatory enforcement agencies have been opposed to the overall usage of AI tools, having scrutinized them heavily under local, state and federal anti-discrimination laws. In what was a welcome piece of news for New York-based employers, the New York City Department of Consumer and Worker Protection recently published a set of proposed rules that could drastically reshape the process of hiring and employment-based legislation still awaiting approval. For city employers who heavily utilize automated employment decision tools (AEDT) for hiring, these proposed rules will provide some initial guidance on the laws surrounding artificial intelligence, with hopes of clarifying the ambiguous AI law the city enacted back in 2021. The law, which won't fully go into effect until January 1, 2023, prohibits employers from using any form of AEDT unless a bias audit is completed by an independently sourced auditor and notice requirements are fully met.

Incivility remains a major challenge for online discussion platforms, to such an extent that even conversations between well-intentioned users can often derail into uncivil behavior. Traditionally, platforms have relied on moderators to -- with or without algorithmic assistance -- take corrective actions such as removing comments or banning users. In this work we propose a complementary paradigm that directly empowers users by proactively enhancing their awareness about existing tension in the conversation they are engaging in and actively guides them as they are drafting their replies to avoid further escalation. As a proof of concept for this paradigm, we design an algorithmic tool that provides such proactive information directly to users, and conduct a user study in a popular discussion platform. Through a mixed methods approach combining surveys with a randomized controlled experiment, we uncover qualitative and quantitative insights regarding how the participants utilize and react to this information. Most participants report finding this proactive paradigm valuable, noting that it helps them to identify tension that they may have otherwise missed and prompts them to further reflect on their own replies and to revise them. These effects are corroborated by a comparison of how the participants draft their reply when our tool warns them that their conversation is at risk of derailing into uncivil behavior versus in a control condition where the tool is disabled. These preliminary findings highlight the potential of this user-centered paradigm and point to concrete directions for future implementations.

The phrase "mission creep" entered the popular discourse in the early to mid-1990s. The trick is to do this without inviting what a senior official called "mission creep" -- the expansion of the role to include, for example, raiding neighborhoods controlled by General Aidid and searching for weapons. Like countless military and sports terms before it, we now understand it in a broader context. It's one of those phrases that perfectly encapsulates a commonly understood experience -- projects whose size, scope and focus shift so gradually you hardly even notice. I bring this up in the context of an op-ed the Electronic Frontier Foundation published last year.

Important From release 22.05: 'master' branch has been replaced with'main' following our inclusive language update, more information here. Important From release 22.08: armv7a with Android build will no longer be tested or maintained. The Compute Library is a collection of low-level machine learning functions optimized for Arm Cortex -A, Arm Neoverse and Arm Mali GPUs architectures. The library provides superior performance to other open source alternatives and immediate support for new Arm technologies e.g. Note: The documentation includes the reference API, changelogs, build guide, contribution guide, errata, etc.

On the other hand, the Beijing model for AI governance is a hybrid approach treating science and technology as embedded in national laws to propel the growth of the national economy. The idea is to aggregate data for AI by encouraging the population to engage in the digital transformation. Besides initiatives like Made in China 2025, the Internet Plus Initiative, and The New Generation AI Development Plan that set AI commercialisation and marketplace goals, China has established a centralised but multistakeholder-oriented body. Like in other countries, China also regulates AI by data protection legislation.

The vast potential of Artificial Intelligence has hit a bump in the road following the refusal by several countries to patent inventions generated by an AI machine, says Macquarie Law School's Dr Rita Matulionyte. Macquarie Law School's Dr Rita Matulionyte, an international expert in intellectual property law, says the decisions – which have been challenged in overseas courts – signal a need for reform to ensure current law does not stifle innovation in an'immensely promising' sector. "This is the first case where an applicant is trying to patent AI-generated inventions and indicate AI as the inventor, whereas previously, patents granted over such AI inventions mentioned human beings as the inventors," Matulionyte says. The economic contribution of AI is potentially huge: according to Australia's AI roadmap, digital technologies including AI are potentially worth $A315 billion to Australia's economy by 2028, while AI alone could be worth $A22.17 Used by companies such as Coca-Cola and KFC to protect their secret recipes, trade secrets is another type of intellectual property law that could be available to AI inventions, Matulionyte says.

NextGen Healthcare, a leading provider of innovative cloud-based healthcare technology solutions, announced it has signed a definitive agreement to acquire TSI Healthcare, a privately held value-added reseller located in Chapel Hill, NC. The acquisition shall be deemed effective 11:59 p.m. on November 30, 2022. The consideration is comprised of an upfront amount of $68 million, which will be paid in cash with contingent consideration of up to $22 million in cash in the form of an earnout, subject to achieving certain financial targets through March 31, 2025. The acquisition is expected to contribute approximately $10 to 12 million of revenue in the remaining four months of fiscal 2023 and will be accretive to adjusted EBITDA and cash flow within a year. The company plans to update guidance when it reports its fiscal 2023 third quarter results.

While artificial intelligence (AI) promises faster and smarter decision making, the Actuaries Institute and the Australian Human Rights Commission (AHRC) worry about potential discrimination and highlight the need to prevent this. To address the issue, they created a Guidance Resource designed to help insurers and actuaries to comply with the federal anti-discrimination legislation when AI is used in pricing or underwriting insurance products. The guidance was developed after a 2021 report by the AHRC that looked at the human rights impacts of new and emerging technologies, including AI-informed decision making. The Actuaries Institute strongly supported the report's recommendations to develop a set guidelines for use by the government and non-government organisations on complying with federal antidiscrimination laws when AI has been used in decision making. It approached the AHRC with a collaboration offer and together they developed these guidelines.

News Image Captioning requires describing an image by leveraging additional context from a news article. Previous works only coarsely leverage the article to extract the necessary context, which makes it challenging for models to identify relevant events and named entities. In our paper, we first demonstrate that by combining more fine-grained context that captures the key named entities (obtained via an oracle) and the global context that summarizes the news, we can dramatically improve the model's ability to generate accurate news captions. This begs the question, how to automatically extract such key entities from an image? We propose to use the pre-trained vision and language retrieval model CLIP to localize the visually grounded entities in the news article and then capture the non-visual entities via an open relation extraction model. Our experiments demonstrate that by simply selecting a better context from the article, we can significantly improve the performance of existing models and achieve new state-of-the-art performance on multiple benchmarks.

The essential task of urban planning is to generate the optimal land-use configuration of a target area. However, traditional urban planning is time-consuming and labor-intensive. Deep generative learning gives us hope that we can automate this planning process and come up with the ideal urban plans. While remarkable achievements have been obtained, they have exhibited limitations in lacking awareness of: 1) the hierarchical dependencies between functional zones and spatial grids; 2) the peer dependencies among functional zones; and 3) human regulations to ensure the usability of generated configurations. To address these limitations, we develop a novel human-instructed deep hierarchical generative model. We rethink the urban planning generative task from a unique functionality perspective, where we summarize planning requirements into different functionality projections for better urban plan generation. To this end, we develop a three-stage generation process from a target area to zones to grids. The first stage is to label the grids of a target area with latent functionalities to discover functional zones. The second stage is to perceive the planning requirements to form urban functionality projections. We propose a novel module: functionalizer to project the embedding of human instructions and geospatial contexts to the zone-level plan to obtain such projections. Each projection includes the information of land-use portfolios and the structural dependencies across spatial grids in terms of a specific urban function. The third stage is to leverage multi-attentions to model the zone-zone peer dependencies of the functionality projections to generate grid-level land-use configurations. Finally, we present extensive experiments to demonstrate the effectiveness of our framework.