Tijuana, Mexico – Standing in a common area of the Casa del Migrante shelter in the Mexican border city of Tijuana, Maria taps her phone screen but can't get the app she is using to work. Maria and her family fled their native Haiti to Venezuela years ago. But recent Venezuelan economic and political instability forced them to leave that country, too, and she said they are now hoping to apply for asylum in the United States. But she and her husband and daughter have tried every day for the last month to get a US immigration appointment through the country's new CBP One app -- to no avail. And without a CBP One appointment, the family faces steep consequences should they try to cross the border irregularly, including being deported back to Haiti and barred from entering the US for up to five years.

Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology's impact on society. This episode we're talking with Dr Marie Oldfield on definitions of AI, the education and communication gaps with AI, explainable models, ethics in education, problems with audits and legislation, AI accreditation, importance of interdisciplinary teams, when to use AI or not, and harms from algorithms. Marie Oldfield (CStat, CSci, FIScT) is the CEO of Oldfield Consultancy and Kuinua Coaching. She is also a Senior Lecturer in Practice for the London School of Economics. With a background in mathematics and philosophy, she is a trusted advisor to government, defence, and the legal sector, amongst others.

Doctors believe Artificial Intelligence is now saving lives, after a major advancement in breast cancer screenings. A.I. is detecting early signs of the disease, in some cases years before doctors would find the cancer on a traditional scan. For every patient visit, physicians spend an average of 16 minutes and 14 seconds using electronic health records to review data and make notes, according to a 2020 study in the Annals of Internal Medicine. Navina, a New York-based medical tech company, has created an artificial intelligence tool to help doctors reclaim some of that time -- and ensure that important data doesn't get missed. The platform, which is also called Navina, uses generative AI to transform how data informs the physician-patient interaction, explained Ronen Lavi, the company's Israel-based CEO.

"We sleep safely at night because rough men stand ready to visit violence on those who would harm us." American military recruiting is in dire straits. The Government Accountability Office (GAO) recently determined the Defense Department confronts its most challenging recruitment environment in 50 years, reporting the department "does not have sufficient plans, goals, and strategies to guide its recruitment and retention efforts." Recruiters for the armed services face a daunting task, especially amid a hot job market and polarized cultural landscape. Existing AI tools have the potential to streamline and focus their efforts, pre-identifying those most qualified and most likely to want to serve.

Google Bard, the search giant's ChatGPT rival, is already available in 180 countries and territories. But even though it's been widely available for months and was the centerpiece of Google's recent I/O event, it's missing one big region. The 450 million people living in the European Union are still unable to access Bard, or any of the company's other generative AI technologies. It's a move that has surprised lawmakers, and even Google won't say why it's holding back. Brando Benifei, the MEP leading the negotiations on Europe's new artificial intelligence rules, is not sure why the bloc had been excluded, describing the omission of the EU from Bard's rollout as a "big issue."

PsychoGenics CEO Emer Leahy of Paramus, New Jersey, explains how the first potential AI-discovered treatment for schizophrenia was developed through machine learning. Fox News Digital spoke with her. AI experts and tech-inclined political scientists are sounding the alarm on the unregulated use of AI tools going into an election season. Generative AI can not only rapidly produce targeted campaign emails, texts or videos, it also could be used to mislead voters, impersonate candidates and undermine elections on a scale and at a speed not yet seen. A booth is ready for a voter, Feb. 24, 2020, at City Hall in Cambridge, Mass., on the first morning of early voting in the state.

Despite recent advances in detecting fake news generated by neural models, their results are not readily applicable to effective detection of human-written disinformation. What limits the successful transfer between them is the sizable gap between machine-generated fake news and human-authored ones, including the notable differences in terms of style and underlying intent. With this in mind, we propose a novel framework for generating training examples that are informed by the known styles and strategies of human-authored propaganda. Specifically, we perform self-critical sequence training guided by natural language inference to ensure the validity of the generated articles, while also incorporating propaganda techniques, such as appeal to authority and loaded language. In particular, we create a new training dataset, PropaNews, with 2,256 examples, which we release for future use. Our experimental results show that fake news detectors trained on PropaNews are better at detecting human-written disinformation by 3.62 - 7.69% F1 score on two public datasets.

NLP in the legal domain has seen increasing success with the emergence of Transformer-based Pre-trained Language Models (PLMs) pre-trained on legal text. PLMs trained over European and US legal text are available publicly; however, legal text from other domains (countries), such as India, have a lot of distinguishing characteristics. With the rapidly increasing volume of Legal NLP applications in various countries, it has become necessary to pre-train such LMs over legal text of other countries as well. In this work, we attempt to investigate pre-training in the Indian legal domain. We re-train (continue pre-training) two popular legal PLMs, LegalBERT and CaseLawBERT, on Indian legal data, as well as train a model from scratch with a vocabulary based on Indian legal text. We apply these PLMs over three benchmark legal NLP tasks -- Legal Statute Identification from facts, Semantic Segmentation of Court Judgment Documents, and Court Appeal Judgment Prediction -- over both Indian and non-Indian (EU, UK) datasets. We observe that our approach not only enhances performance on the new domain (Indian texts) but also over the original domain (European and UK texts). We also conduct explainability experiments for a qualitative comparison of all these different PLMs.

Trust is an important factor in people's interactions with AI systems. However, there is a lack of empirical studies examining how real end-users trust or distrust the AI system they interact with. Most research investigates one aspect of trust in lab settings with hypothetical end-users. In this paper, we provide a holistic and nuanced understanding of trust in AI through a qualitative case study of a real-world computer vision application. We report findings from interviews with 20 end-users of a popular, AI-based bird identification app where we inquired about their trust in the app from many angles. We find participants perceived the app as trustworthy and trusted it, but selectively accepted app outputs after engaging in verification behaviors, and decided against app adoption in certain high-stakes scenarios. We also find domain knowledge and context are important factors for trust-related assessment and decision-making. We discuss the implications of our findings and provide recommendations for future research on trust in AI.

In-context learning, where pre-trained language models learn to perform tasks from task examples and instructions in their contexts, has attracted much attention in the NLP community. However, the ability of in-context learning is not fully exploited because language models are not explicitly trained to learn in context. To this end, we propose PICL (Pre-training for In-Context Learning), a framework to enhance the language models' in-context learning ability by pre-training the model on a large collection of "intrinsic tasks" in the general plain-text corpus using the simple language modeling objective. PICL encourages the model to infer and perform tasks by conditioning on the contexts while maintaining task generalization of pre-trained models. We evaluate the in-context learning performance of the model trained with PICL on seven widely-used text classification datasets and the Super-NaturalInstrctions benchmark, which contains 100+ NLP tasks formulated to text generation. Our experiments show that PICL is more effective and task-generalizable than a range of baselines, outperforming larger language models with nearly 4x parameters. The code is publicly available at https://github.com/thu-coai/PICL.