While research has focused on surfacing and auditing algorithmic bias to ensure equitable AI development, less is known about how NLP practitioners - those directly involved in dataset development, annotation, and deployment - perceive and navigate issues of NLP data equity. This study is among the first to center practitioners' perspectives, linking their experiences to a multi-scalar AI governance framework and advancing participatory recommendations that bridge technical, policy, and community domains. Drawing on a 2024 questionnaire and focus group, we examine how U.S.-based NLP data practitioners conceptualize fairness, contend with organizational and systemic constraints, and engage emerging governance efforts such as the U.S. AI Bill of Rights. Findings reveal persistent tensions between commercial objectives and equity commitments, alongside calls for more participatory and accountable data workflows. We critically engage debates on data diversity and diversity washing, arguing that improving NLP equity requires structural governance reforms that support practitioner agency and community consent.

Through a survey of 300 health care executives and a program of interviews with industry experts, startup leaders, and academic researchers, this report explores the best practices for success when implementing integrated digital solutions into health care, and how these can support decision-makers in a range of settings, including laboratories and hospitals. Health care is primed for digital adoption. The global pandemic underscored the benefits of value-based care and accelerated the adoption of digital and AI-powered technologies in health care. Overwhelmingly, 96% of the survey respondents say they are "ready and resourced" to use digital health, while one in four say they are "very ready." However, 91% of executives agree interoperability is a challenge, with a majority (59%) saying it will be "tough" to solve.

Large Language Models (LLMs) are capable of generating opinions and propagating bias unknowingly, originating from unrepresentative and non-diverse data collection. Prior research has analysed these opinions with respect to the West, particularly the United States. However, insights thus produced may not be generalized in non-Western populations. With the widespread usage of LLM systems by users across several different walks of life, the cultural sensitivity of each generated output is of crucial interest. Our work proposes a novel method that quantitatively analyzes the opinions generated by LLMs, improving on previous work with regards to extracting the social demographics of the models. Our method measures the distance from an LLM's response to survey respondents, through Hamming Distance, to infer the demographic characteristics reflected in the model's outputs. We evaluate modern, open LLMs such as Llama and Mistral on surveys conducted in various global south countries, with a focus on India and other Asian nations, specifically assessing the model's performance on surveys related to religious tolerance and identity. Our analysis reveals that most open LLMs match a single homogeneous profile, varying across different countries/territories, which in turn raises questions about the risks of LLMs promoting a hegemonic worldview, and undermining perspectives of different minorities. Our framework may also be useful for future research investigating the complex intersection between training data, model architecture, and the resulting biases reflected in LLM outputs, particularly concerning sensitive topics like religious tolerance and identity.

This study explores the effectiveness of AI tools in enhancing student learning, specifically in improving study habits, time management, and feedback mechanisms. The research focuses on how AI tools can support personalized learning, adaptive test adjustments, and provide real-time classroom analysis. Student feedback revealed strong support for these features, and the study found a significant reduction in study hours alongside an increase in GPA, suggesting positive academic outcomes. Despite these benefits, challenges such as over-reliance on AI and difficulties in integrating AI with traditional teaching methods were also identified, emphasizing the need for AI tools to complement conventional educational strategies rather than replace them. Data were collected through a survey with a Likert scale and follow-up interviews, providing both quantitative and qualitative insights. The analysis involved descriptive statistics to summarize demographic data, AI usage patterns, and perceived effectiveness, as well as inferential statistics (T-tests, ANOVA) to examine the impact of demographic factors on AI adoption. Regression analysis identified predictors of AI adoption, and qualitative responses were thematically analyzed to understand students' perspectives on the future of AI in education. This mixed-methods approach provided a comprehensive view of AI's role in education and highlighted the importance of privacy, transparency, and continuous refinement of AI features to maximize their educational benefits.

Preparing an organization's data for AI, however, unlocks a new set of challenges and opportunities. This MIT Technology Review Insights survey report investigates whether companies' data foundations are ready to garner benefits from generative AI, as well as the challenges of building the necessary data infrastructure for this technology. In doing so, it draws on insights from a survey of 300 C-suite executives and senior technology leaders, as well on in-depth interviews with four leading experts. Data integration is the leading priority for AI readiness. In our survey, 82% of C-suite and other senior executives agree that "scaling AI or generative AI use cases to create business value is a top priority for our organization."

But true data intelligence is about more than establishing the right data foundation. Organizations are also wrestling with how to overcome dependence on highly technical staff and create frameworks for data privacy and organizational control when using generative AI. Specifically, they are looking to enable all employees to use natural language to glean actionable insight from the company's own data; to leverage that data at scale to train, build, deploy, and tune their own secure large language models (LLMs); and to infuse intelligence about the company's data into every business process. In this next frontier of data intelligence, organizations will maximize value by democratizing AI while differentiating through their people, processes, and technology within their industry context. Based on a global, cross-industry survey of 600 technology leaders as well as in-depth interviews with technology leaders, this report explores the foundations being built and leveraged across industries to democratize data and AI.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. In early November, Bentley University and Gallup released the results of its 2023 Bentley-Gallup Business and Society Report, which among other topics, focuses a portion of its study on surveying Americans on their opinions of how businesses will use artificial intelligence (AI) technologies in the future. When asked "In general, how much do you trust businesses to use artificial intelligence responsibly?", What is particularly telling, is that across education levels, ethnic background, age groups, and political party, the range of those trusting AI a "lot/some" was only between 17% and 28%.

We present WinoQueer: a benchmark specifically designed to measure whether large language models (LLMs) encode biases that are harmful to the LGBTQ+ community. The benchmark is community-sourced, via application of a novel method that generates a bias benchmark from a community survey. We apply our benchmark to several popular LLMs and find that off-the-shelf models generally do exhibit considerable anti-queer bias. Finally, we show that LLM bias against a marginalized community can be somewhat mitigated by finetuning on data written about or by members of that community, and that social media text written by community members is more effective than news text written about the community by non-members. Our method for community-in-the-loop benchmark development provides a blueprint for future researchers to develop community-driven, harms-grounded LLM benchmarks for other marginalized communities.

I'll update this content periodically: see'Updated' links at the end of this article.] While attention on and interest in artificial intelligence as a tool to aid and amplify people's creativity continues in a non-stop fashion with ideas, opinions, announcements and more appearing in unequal measure on a daily basis, AI is not catching on everywhere. During much of 2022, all the attention was on creating AI-generated images with Midjourney, Stable Diffusion and DALLE-2 as the primary trio of tools that anyone could use to translate imaginations into reality, even winning an art contest. So far in this year 2023, all the talk has been about chatbots and specifically ChatGPT that have profoundly captured imaginations in the mainstream with the simple formula of writing a question or instruction for the AI to create a text result from the prompt. Excitement and hype has been endless.

Two-thirds of the respondents (67%) said they were still learning how AI works and exploring use cases and technologies. Just 15% of the respondents reported that they had achieved wide-scale implementation of AI. When asked how they would classify their understanding of AI terminology and capabilities, 45% of the respondents rated their level of understanding as beginner, 43% said intermediate, and only 12% said advanced. In addition, only 29% of the respondents said they are highly confident or very highly confident in their ability to evaluate AI-powered marketing technologies. The research found that marketers recognize the importance of AI and expect its use to grow significantly in the near future.