Data science has become increasingly essential for the production of official statistics, as it enables the automated collection, processing, and analysis of large amounts of data. With such data science practices in place, it enables more timely, more insightful and more flexible reporting. However, the quality and integrity of data-science-driven statistics rely on the accuracy and reliability of the data sources and the machine learning techniques that support them. In particular, changes in data sources are inevitable to occur and pose significant risks that are crucial to address in the context of machine learning for official statistics. This paper gives an overview of the main risks, liabilities, and uncertainties associated with changing data sources in the context of machine learning for official statistics. We provide a checklist of the most prevalent origins and causes of changing data sources; not only on a technical level but also regarding ownership, ethics, regulation, and public perception. Next, we highlight the repercussions of changing data sources on statistical reporting. These include technical effects such as concept drift, bias, availability, validity, accuracy and completeness, but also the neutrality and potential discontinuation of the statistical offering. We offer a few important precautionary measures, such as enhancing robustness in both data sourcing and statistical techniques, and thorough monitoring. In doing so, machine learning-based official statistics can maintain integrity, reliability, consistency, and relevance in policy-making, decision-making, and public discourse.

Available corpora for Argument Mining differ along several axes, and one of the key differences is the presence (or absence) of discourse markers to signal argumentative content. Exploring effective ways to use discourse markers has received wide attention in various discourse parsing tasks, from which it is well-known that discourse markers are strong indicators of discourse relations. To improve the robustness of Argument Mining systems across different genres, we propose to automatically augment a given text with discourse markers such that all relations are explicitly signaled. Our analysis unveils that popular language models taken out-of-the-box fail on this task; however, when fine-tuned on a new heterogeneous dataset that we construct (including synthetic and real examples), they perform considerably better. We demonstrate the impact of our approach on an Argument Mining downstream task, evaluated on different corpora, showing that language models can be trained to automatically fill in discourse markers across different corpora, improving the performance of a downstream model in some, but not all, cases. Our proposed approach can further be employed as an assistive tool for better discourse understanding.

Verbal deception has been studied in psychology, forensics, and computational linguistics for a variety of reasons, like understanding behaviour patterns, identifying false testimonies, and detecting deception in online communication. Varying motivations across research fields lead to differences in the domain choices to study and in the conceptualization of deception, making it hard to compare models and build robust deception detection systems for a given language. With this paper, we improve this situation by surveying available English deception datasets which include domains like social media reviews, court testimonials, opinion statements on specific topics, and deceptive dialogues from online strategy games. We consolidate these datasets into a single unified corpus. Based on this resource, we conduct a correlation analysis of linguistic cues of deception across datasets to understand the differences and perform cross-corpus modeling experiments which show that a cross-domain generalization is challenging to achieve. The unified deception corpus (UNIDECOR) can be obtained from https://www.ims.uni-stuttgart.de/data/unidecor.

Integrating ethical practices into the AI development process for artificial intelligence (AI) is essential to ensure safe, fair, and responsible operation. AI ethics involves applying ethical principles to the entire life cycle of AI systems. This is essential to mitigate potential risks and harms associated with AI, such as algorithm biases. To achieve this goal, responsible design patterns (RDPs) are critical for Machine Learning (ML) pipelines to guarantee ethical and fair outcomes. In this paper, we propose a comprehensive framework incorporating RDPs into ML pipelines to mitigate risks and ensure the ethical development of AI systems. Our framework comprises new responsible AI design patterns for ML pipelines identified through a survey of AI ethics and data management experts and validated through real-world scenarios with expert feedback. The framework guides AI developers, data scientists, and policy-makers to implement ethical practices in AI development and deploy responsible AI systems in production.

Learning a fair predictive model is crucial to mitigate biased decisions against minority groups in high-stakes applications. A common approach to learn such a model involves solving an optimization problem that maximizes the predictive power of the model under an appropriate group fairness constraint. However, in practice, sensitive attributes are often missing or noisy resulting in uncertainty. We demonstrate that solely enforcing fairness constraints on uncertain sensitive attributes can fall significantly short in achieving the level of fairness of models trained without uncertainty. To overcome this limitation, we propose a bootstrap-based algorithm that achieves the target level of fairness despite the uncertainty in sensitive attributes. The algorithm is guided by a Gaussian analysis for the independence notion of fairness where we propose a robust quadratically constrained quadratic problem to ensure a strict fairness guarantee with uncertain sensitive attributes. Our algorithm is applicable to both discrete and continuous sensitive attributes and is effective in real-world classification and regression tasks for various group fairness notions, e.g., independence and separation.

Machine learning models that convert user-written text descriptions into images are now widely available online and used by millions of users to generate millions of images a day. We investigate the potential for these models to amplify dangerous and complex stereotypes. We find a broad range of ordinary prompts produce stereotypes, including prompts simply mentioning traits, descriptors, occupations, or objects. For example, we find cases of prompting for basic traits or social roles resulting in images reinforcing whiteness as ideal, prompting for occupations resulting in amplification of racial and gender disparities, and prompting for objects resulting in reification of American norms. Stereotypes are present regardless of whether prompts explicitly mention identity and demographic language or avoid such language. Moreover, stereotypes persist despite mitigation strategies; neither user attempts to counter stereotypes by requesting images with specific counter-stereotypes nor institutional attempts to add system ``guardrails'' have prevented the perpetuation of stereotypes. Our analysis justifies concerns regarding the impacts of today's models, presenting striking exemplars, and connecting these findings with deep insights into harms drawn from social scientific and humanist disciplines. This work contributes to the effort to shed light on the uniquely complex biases in language-vision models and demonstrates the ways that the mass deployment of text-to-image generation models results in mass dissemination of stereotypes and resulting harms.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Rapid advances in artificial intelligence (AI) such as Microsoft-backed OpenAI's ChatGPT are complicating governments' efforts to agree laws governing the use of the technology. The government is consulting Australia's main science advisory body and considering next steps, a spokesperson for the industry and science minister said in April. The Financial Conduct Authority, one of several state regulators that has been tasked with drawing up new guidelines covering AI, is consulting with the Alan Turing Institute and other legal and academic institutions to improve its understanding of the technology, a spokesperson told Reuters.

Center for AI Safety Director Dan Hendrycks explains concerns about how the rapid growth of artificial intelligence could impact society. Judges are likely to take concerns over artificial intelligence into their own hands and create their own rules for the tech in courtrooms, experts say. U.S. District Judge Brantley Starr of the Northern District of Texas may have been a pioneer last week when he required lawyers who appear in his courtroom to certify they did not use artificial intelligence programs, such as ChatGPT, to draft their filings without a human checking for accuracy. "We're at least putting lawyers on notice, who might not otherwise be on notice, that they can't just trust those databases," Starr, a Trump appointed judge, told Reuters. "They've got to actually verify it themselves through a traditional database."

LGBTQ advocate Heather Hester scolded Target's "rainbow capitalism" after the retailer dialed back Pride displays (Reuters) Subscribe now to get Fox News First in your email. And here's what you need to know to start your day ... EYE ON THE TARGET - Retailer's $15B loss in'stunning collapse' should serve as warning to CEOs, 'Shark Tank' star says. 'UNDENIABLE FACTS': - Tim Scott earns praise after leaving liberal'View' host'speechless.' TARMAC TROUBLE - Deputies remove handcuff and remove unruly passenger from Southwest plane before takeoff. SCIENTOLOGY SPOTLIGHT - Danny Masterson, Tom Cruise and Leah Remini illuminate Hollywood church drama.

Harvey Castro talks about how AI cold be used in cold cases and the symbiotic relationship between AI and a detective. A new bill introduced in the House of Representatives on Monday is aimed at making sure American consumers know the difference between fantasy and reality online by cracking down on generative artificial intelligence technology. Rep. Ritchie Torres, D-N.Y., is leading the effort on the AI Disclosure Act of 2023, which would force AI-generated content to include the disclaimer, "Disclaimer: this output has been generated by artificial intelligence." In a statement announcing the bill, Torres predicted that "regulatory framework for managing the existential risks of AI will be one of the central challenges confronting Congress in the years and decades to come." He noted risks in going too far with policing AI as well as not regulating it enough.