Dr. Axel Spies is a German attorney (Rechtsanwalt) in Washington, DC, and co-publisher of the German journals Multi-Media-Recht (MMR) and Zeitschrift für Datenschutz (ZD). The impact of the Artificial Intelligence Act (AIA) proposed by the European Commission, and currently debated at the European Parliament (EP), has been underestimated in the United States. With approximately 3,000 amendments that must be reconciled, the AIA represents the first attempt to regulate artificial intelligence (AI) by a uniform law from cradle to grave. The AIA focuses on the providers of AI services that put them on the market or use them for their own purposes. Germany is actively contributing to the debate: AI is mentioned in the federal government's Coalition Treaty as a "digital key technology" and a European AIA is generally supported.

'Gutfeld!' panelists weigh in on a new survey claiming almost half of recent college graduates aren't'emotionally' prepared for a 9-5 job. Student senator Mike Abel at the University of Houston successfully challenged the Student Government Association's (SGA) "Respect for Pronouns" bill that would have mandated the use of others members' preferred pronouns. The bill also calls for "Name tags containing the proper pronouns will be given to every member of the Student Government Association" and it "strongly recommend[ed]" to list pronouns during Zoom meetings. The SGA's Supreme Court is poised to rule in favor of Abel, determining that such legislation constituted a violation of the students' first amendment rights, Campus Reform reported Friday. Specifically, the SGA Supreme Court is said to have found that the last sentence of the legislation, which would have compelled speech from the organization's members, was a violation.

Robust quantification of forest carbon stocks and their dynamics is important for climate change mitigation and adaptation strategies [FAO and UNEP, 2020]. The Paris Agreement [United Nations / Framework Convention on Climate Change, 2015] and the IPCC [Shukla et al., 2019] acknowledge that climate change mitigation goals cannot be achieved without a substantial contribution from forests. Spatial details in the carbon budget of forests are necessary to encourage transformational actions towards a sustainable forest sector [Harris et al., 2021, 2012]. Currently, many countries do not have nationally specific forest carbon accumulation rates but rather rely on default rates from the IPCC 2018 [Masson-Delmotte et al., 2019, Requena Suarez et al., 2019]), without accounting for finer-scale variations of carbon stocks [Cook-Patton et al., 2020]. Precise spatio-temporal monitoring of forest carbon dynamics at large scales has proven to be challenging [Erb et al., 2018, Griscom et al., 2017]. This is due to the complex structure of forests, topographic features, and land management practices [Tubiello et al., 2021, Lewis et al., 2019]. Technological developments in remote sensing and the concurrent increased availability of field-based measurements have led to an improvement in estimating carbon stocks using remote sensing observations of forest attributes that serve as proxy for above-ground biomass (AGB) [Knapp et al., 2018, Bouvier et al., 2015, Pan et al., 2013]. Currently, three remote sensing techniques are applied to collect data for AGB estimates: i) passive optical imagery, ii) synthetic aperture radar (SAR), and iii) light detection and ranging (LiDAR).

Abstract: An Artificially Intelligent system (an AI) has debatable personhood if it's epistemically possible either that the AI is a person or that it falls far short of personhood. Debatable personhood is a likely outcome of AI development and might arise soon. Debatable AI personhood throws us into a catastrophic moral dilemma: Either treat the systems as moral persons and risk sacrificing real human interests for the sake of entities without interests worth the sacrifice, or don't treat the systems as moral persons and risk perpetrating grievous moral wrongs against them. The moral issues become even more perplexing if we consider cases of possibly conscious AI that are subhuman, superhuman, or highly divergent from us in their morally relevant properties. We might soon build artificially intelligent entities - AIs - of debatable personhood. Our systems and habits of ethical thinking are currently as unprepared for this decision as medieval physics was for space flight.

Monitoring microbiological behaviors in water is crucial to manage public health risk from waterborne pathogens, although quantifying the concentrations of microbiological organisms in water is still challenging because concentrations of many pathogens in water samples may often be below the quantification limit, producing censoring data. To enable statistical analysis based on quantitative values, the true values of non-detected measurements are required to be estimated with high precision. Tobit model is a well-known linear regression model for analyzing censored data. One drawback of the Tobit model is that only the target variable is allowed to be censored. In this study, we devised a novel extension of the classical Tobit model, called the \emph{multi-target Tobit model}, to handle multiple censored variables simultaneously by introducing multiple target variables. For fitting the new model, a numerical stable optimization algorithm was developed based on elaborate theories. Experiments conducted using several real-world water quality datasets provided an evidence that estimating multiple columns jointly gains a great advantage over estimating them separately.

Recent advances in the capacity of large language models to generate human-like text have resulted in their increased adoption in user-facing settings. In parallel, these improvements have prompted a heated discourse around the risks of societal harms they introduce, whether inadvertent or malicious. Several studies have explored these harms and called for their mitigation via development of safer, fairer models. Going beyond enumerating the risks of harms, this work provides a survey of practical methods for addressing potential threats and societal harms from language generation models. We draw on several prior works' taxonomies of language model risks to present a structured overview of strategies for detecting and ameliorating different kinds of risks/harms of language generators. Bridging diverse strands of research, this survey aims Figure 1: Overview of Intervention Strategies. A typical to serve as a practical guide for both LM researchers ML/NLP model development process involves data and practitioners, with explanations collection/curation, model training and design, inference, of different mitigation strategies' motivations, and finally application deployment.

In recent years, there has been a growing interest in the development of language models capable of generating text with controllable attributes. While several approaches have been proposed, many of these methods require condition-specific data or significant computational resources. In this study, we propose a novel method called Gamma Sampling, which enables controllable language generation without the need for any training data and maintains a fast generation speed. Gamma Sampling incorporates attribute-related information into the sampling process, effectively guiding the language model to produce text with desired attributes. Our experimental results demonstrate that Gamma Sampling, when applied to GPT2, outperforms representative baselines in terms of diversity, attribute relevance, and overall quality of the generated samples.

Recent years have seen growing interest among both researchers and practitioners in user-engaged approaches to algorithm auditing, which directly engage users in detecting problematic behaviors in algorithmic systems. However, we know little about industry practitioners' current practices and challenges around user-engaged auditing, nor what opportunities exist for them to better leverage such approaches in practice. To investigate, we conducted a series of interviews and iterative co-design activities with practitioners who employ user-engaged auditing approaches in their work. Our findings reveal several challenges practitioners face in appropriately recruiting and incentivizing user auditors, scaffolding user audits, and deriving actionable insights from user-engaged audit reports. Furthermore, practitioners shared organizational obstacles to user-engaged auditing, surfacing a complex relationship between practitioners and user auditors. Based on these findings, we discuss opportunities for future HCI research to help realize the potential (and the mitigate risks) of user-engaged auditing in industry practice.

San Francisco's board of supervisors recently voted to let their police deploy robots equipped with lethal explosives – before backtracking several weeks later. In America, the vote sparked a fierce debate on the militarisation of the police, but it raises fundamental questions for us all about the role of robots and AI in fighting crime, how policing decisions are made and, indeed, the very purpose of our criminal justice systems. In the UK, officers operate under the principle of "policing by consent" rather than by force. But according to the 2020 Crime Survey for England and Wales, public confidence in the police has fallen from 62% in 2017 to 55%. One recent poll asked Londoners if the Met was institutionally sexist and racist.

Over 60 countries, including the U.S. and China, have held the first international summit on the use of AI in the military and warfare at The Hague, where they've signed a'call to action' for the responsible use of the technology. This hopefully means that creating AI soldier bots capable of wiping out countless people isn't at the top of the list when developing new AI-based military platforms. Unfortunately, it wasn't a formal or legally binding agreement, simply a pledge to develop and use AI with "international legal obligations and in a way that does not undermine international security, stability, and accountability." With the rise of AI platforms like ChatGPT and AI-assisted targeting systems and facial recognition being developed for military use, not to mention the issue of drones as a tool for warfare, it was a relatively light affair for what is a hot topic right now. Regarding attendance at REAIM (Responsible AI in the Military), Russia and Ukraine did not attend the summit, with Israel being there but not signing the statement.