A key question in a number of court cases is whether a speaker on an audio recording is a particular known speaker, for example, whether a speaker on a recording of an intercepted telephone call is the defendant. In most English-speaking countries, expert testimony is only admissible in a court of law if it will potentially assist the judge or the jury to make a decision. If the judge or the jury's speaker identification were equally accurate or more accurate than a forensic scientist's forensic voice comparison, then the forensic-voice-comparison testimony would not be admissible. In a research paper published in the journal Forensic Science International, a multidisciplinary international team of researchers has reported the first set of results from a comprehensive study that compares the accuracy of speaker-identification by individual listeners (like judges or jury members) with the accuracy of a forensic-voice-comparison system that is based on state-of-the-art automatic-speaker-recognition technology, and that does so using recordings that reflect the conditions of an actual case. The questioned-speaker recording was of a telephone call with background office noise, and the known-speaker recording was of a police interview conducted in echoey room with background ventilation-system noise.

Artificial intelligence was a thing, but not the thing, when Eric Schmidt became CEO of Google in 2001. Sixteen years later, when he stepped down from his post as executive chairman of Google's parent company, Alphabet, the world had changed. Speaking at Princeton University that year, Schmidt declared that we are in "the AI century." Schmidt, who recently chaired the National Security Commission on Artificial Intelligence, and MIT computer science professor Aleksander Madry discussed how this transition should be managed and its broader implications at the 2022 MIT AI Policy Forum Summit. Their conversation came at a moment when AI is ascendant in both public and private imaginations.

Dr. Steven Gustafson is Noonum's CTO and an AI scientist, passionate about solving hard problems while having fun and building great teams. With AI, it's important for business leaders to maintain focus on core issues and not get distracted by hype and hyperbole. Unfortunately, most interactions with consultants, startups or academics about AI involve understanding where new AI technology can fit into the business. This is what is often called "finding a nail for the hammer," with AI being the hammer. It flips the typical business process from understanding a customer need and delivering a solution to trying to find a home for AI technology in the business.

The program, known as Copilot, was created in collaboration with GitHub Inc. and Microsoft Corp., both also named as defendants in the complaint filed Thursday in the US District Court for the Northern District of California. Copilot uses AI models to generate code that replicates publicly available code, but doesn't follow open-source licensing guidelines like attribution to the author, a notice of … The program, known as Copilot, was created in collaboration with GitHub Inc. and Microsoft Corp., both also named as defendants in the complaint filed Thursday in the US District Court for the Northern District of California. Copilot uses AI models to generate code that replicates publicly available code, but doesn't follow open-source licensing guidelines like attribution to the author, a notice of …

Former U.S. President Donald Trump's stolen-election falsehoods have thrust America's voting machine suppliers into a national struggle to protect their businesses. Industry leaders Dominion Voting Systems and Election Systems & Software are waging a political and public relations ground war to beat back threats to their state and local government contracts, rooted in bogus conspiracy theories about vote manipulation. Dominion has also turned to the courts, filing eight defamation lawsuits against Trump allies and media outlets including Fox News. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

Fairness and environmental impact are important research directions for the sustainable development of artificial intelligence. However, while each topic is an active research area in natural language processing (NLP), there is a surprising lack of research on the interplay between the two fields. This lacuna is highly problematic, since there is increasing evidence that an exclusive focus on fairness can actually hinder environmental sustainability, and vice versa. In this work, we shed light on this crucial intersection in NLP by (1) investigating the efficiency of current fairness approaches through surveying example methods for reducing unfair stereotypical bias from the literature, and (2) evaluating a common technique to reduce energy consumption (and thus environmental impact) of English NLP models, knowledge distillation (KD), for its impact on fairness. In this case study, we evaluate the effect of important KD factors, including layer and dimensionality reduction, with respect to: (a) performance on the distillation task (natural language inference and semantic similarity prediction), and (b) multiple measures and dimensions of stereotypical bias (e.g., gender bias measured via the Word Embedding Association Test). Our results lead us to clarify current assumptions regarding the effect of KD on unfair bias: contrary to other findings, we show that KD can actually decrease model fairness.

Industries must follow government rules and regulations around the world to classify products when assessing duties and taxes for international shipment. Harmonized System (HS) is the most standardized numerical method of classifying traded products among industry classification systems. A hierarchical ensemble model comprising of Bert-transformer, NER, distance-based approaches, and knowledge-graphs have been developed to address scalability, coverage, ability to capture nuances, automation and auditing requirements when classifying unknown text-descriptions as per HS method.

Governments, industry, and academia have undertaken efforts to identify and mitigate harms in ML-driven systems, with a particular focus on social and ethical risks of ML components in complex sociotechnical systems. However, existing approaches are largely disjointed, ad-hoc and of unknown effectiveness. Systems safety engineering is a well established discipline with a track record of identifying and managing risks in many complex sociotechnical domains. We adopt the natural hypothesis that tools from this domain could serve to enhance risk analyses of ML in its context of use. To test this hypothesis, we apply a "best of breed" systems safety analysis, Systems Theoretic Process Analysis (STPA), to a specific high-consequence system with an important ML-driven component, namely the Prescription Drug Monitoring Programs (PDMPs) operated by many US States, several of which rely on an ML-derived risk score. We focus in particular on how this analysis can extend to identifying social and ethical risks and developing concrete design-level controls to mitigate them.

New technologies and the availability of geospatial data have drawn attention to spatio-temporal biases present in society. For example: the COVID-19 pandemic highlighted disparities in the availability of broadband service and its role in the digital divide; the environmental justice movement in the United States has raised awareness to health implications for minority populations stemming from historical redlining practices; and studies have found varying quality and coverage in the collection and sharing of open-source geospatial data. Despite the extensive literature on machine learning (ML) fairness, few algorithmic strategies have been proposed to mitigate such biases. In this paper we highlight the unique challenges for quantifying and addressing spatio-temporal biases, through the lens of use cases presented in the scientific literature and media. We envision a roadmap of ML strategies that need to be developed or adapted to quantify and overcome these challenges -- including transfer learning, active learning, and reinforcement learning techniques. Further, we discuss the potential role of ML in providing guidance to policy makers on issues related to spatial fairness.

Laws and their interpretations, legal arguments and agreements\ are typically expressed in writing, leading to the production of vast corpora of legal text. Their analysis, which is at the center of legal practice, becomes increasingly elaborate as these collections grow in size. Natural language understanding (NLU) technologies can be a valuable tool to support legal practitioners in these endeavors. Their usefulness, however, largely depends on whether current state-of-the-art models can generalize across various tasks in the legal domain. To answer this currently open question, we introduce the Legal General Language Understanding Evaluation (LexGLUE) benchmark, a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks in a standardized way. We also provide an evaluation and analysis of several generic and legal-oriented models demonstrating that the latter consistently offer performance improvements across multiple tasks.