Today, the increasing use of fossils and the increase in greenhouse gases (GHGs) have caused climate change. Unfortunately, climate change has become one of the direst crises of our time, causing natural disasters that often result in the eviction of large groups of people, called'climate migrants.' These migrants move to city and state borders to escape the devastating impacts of climate change on their homes and communities. As per the United Nations International Organization for Migration, around one billion people will become climate migrants in the coming next three decades. This number might extend to 1.2 billion by 2050 and 1.4 billion by 2060.

The U.S. Equal Employment Opportunity Commission (EEOC) recently released a draft of its new Strategic Enforcement Plan (SEP), outlining its priorities in tackling workplace discrimination over the next four years. The playbook, published in the Federal Register in January, indicates that the agency will be on the lookout for discrimination caused by artificial intelligence (AI) tools. "The EEOC is signaling in its draft SEP that it intends to enforce federal nondiscrimination laws equally, whether the discrimination takes place through traditional recruiting or through the use of modern and automated tools," said Andrew M. Gordon, an attorney with the law firm Hinshaw & Culbertson LLP in Fort Lauderdale, Fla. Over the last decade, AI use in the workplace has skyrocketed. Nearly 1 in 4 organizations uses AI to support HR-related activities, according to a 2022 survey by the Society for Human Resource Management (SHRM).

The rise of artificial intelligence (AI) technology has significant implications for state and local governments. One of the main implications is the potential for AI to improve the efficiency and effectiveness of government services. For example, AI-powered chatbots can provide 24/7 customer service for citizens, while machine learning algorithms can analyze large amounts of data to identify patterns and insights that can inform decision-making. Additionally, AI can be used to automate routine tasks, such as processing paperwork and data entry, freeing up government employees to focus on more complex and value-added tasks. However, there are also concerns about the impact of AI on jobs and privacy, and governments will need to consider these issues as they implement AI-based solutions.

A few weeks after the launch of ChatGPT, a powerful artificial intelligence (AI) chatbot, Darren Hick said he caught one of his students cheating by submitting a robot-generated essay. The new technology, released by OpenAI and openly available to the public, can pretty much do anything. Type a request and it can write a persuasive school essay, compose a song or even crack a silly knock-knock joke within a few seconds. Although it can write convincingly, it doesn't mean that what it says is true. That's what set off alarm bells for Hick, an assistant professor of philosophy at Furman University, in the US state of South Carolina.

Data Science is the hot favorite job option among IT professionals. Salary estimates are based on 24.8k latest salaries received from Data Scientists. Who is a Data Scientist? Data Science is defined as a data-driven approach to modern-day data problems using a combination of fields including Statistics, Mathematics, Programming, and Machine Learning. Data Science needs research-level knowledge and how the above disciplines can be applied to modern-day data problems. A Data Scientist derives a mathematical model using Machine Learning algorithms.

Cornell engineers have developed machine learning models to simplify and reinforce models to calculate the fine particulate matter (PM2.5) Described in a paper in the journal Transportation Research Part D: Transport and Environment, the modeling approach has low data requirements and is computationally efficient. Previous methods to gauge air pollution were cumbersome and reliant on extraordinary amounts of data points. Older models to calculate particulate matter were computationally and mechanically consuming and complex. But if you develop an easily accessible data model, with the help of artificial intelligence filling in some of the blanks, you can have an accurate model at a local scale.

It is an established fact that China uses civil military fusion to develop intelligence. It is often reported that Chinese mobile phones which are freely available in India pose a threat to national security. It is said that the mobile phones provide the big data needed to develop AI based predictive models. However it is not largely understood how all this happens. For most of us AI, big data, data theft, intelligence collection et al are imaginary issues.

Artificial intelligence (AI), which enables machines to learn to perform a task by training on diverse datasets, is one of the most revolutionary developments in scientific history. Although AI and especially deep learning is relatively new, it has already had transformative impact on medicine, biology, transportation, entertainment, and beyond. As AI changes our daily lives at an increasingly fast pace, we are challenged with preparing our society for an AI-driven future. To this end, a critical step is to ensure an AI-ready workforce through education. Advocates of beginning instruction of AI basics at the K-12 level typically note benefits to the workforce, economy, and national security. In this complementary perspective, we discuss why learning AI is beneficial for motivating students and promoting creative thinking, and how to develop a module-based approach that optimizes learning outcomes. We hope to excite and engage more members of the education community to join the effort to advance K-12 AI education in the USA and worldwide.

Human mobility estimation is crucial during the COVID-19 pandemic due to its significant guidance for policymakers to make non-pharmaceutical interventions. While deep learning approaches outperform conventional estimation techniques on tasks with abundant training data, the continuously evolving pandemic poses a significant challenge to solving this problem due to data nonstationarity, limited observations, and complex social contexts. Prior works on mobility estimation either focus on a single city or lack the ability to model the spatio-temporal dependencies across cities and time periods. To address these issues, we make the first attempt to tackle the cross-city human mobility estimation problem through a deep meta-generative framework. We propose a Spatio-Temporal Meta-Generative Adversarial Network (STORM-GAN) model that estimates dynamic human mobility responses under a set of social and policy conditions related to COVID-19. Facilitated by a novel spatio-temporal task-based graph (STTG) embedding, STORM-GAN is capable of learning shared knowledge from a spatio-temporal distribution of estimation tasks and quickly adapting to new cities and time periods with limited training samples. The STTG embedding component is designed to capture the similarities among cities to mitigate cross-task heterogeneity. Experimental results on real-world data show that the proposed approach can greatly improve estimation performance and out-perform baselines.

This dissertation focuses on modern causal inference under uncertainty and data restrictions, with applications to neoadjuvant clinical trials, distributed data networks, and robust individualized decision making. In the first project, we propose a method under the principal stratification framework to identify and estimate the average treatment effects on a binary outcome, conditional on the counterfactual status of a post-treatment intermediate response. Under mild assumptions, the treatment effect of interest can be identified. We extend the approach to address censored outcome data. The proposed method is applied to a neoadjuvant clinical trial and its performance is evaluated via simulation studies. In the second project, we propose a tree-based model averaging approach to improve the estimation accuracy of conditional average treatment effects at a target site by leveraging models derived from other potentially heterogeneous sites, without them sharing subject-level data. The performance of this approach is demonstrated by a study of the causal effects of oxygen therapy on hospital survival rates and backed up by comprehensive simulations. In the third project, we propose a robust individualized decision learning framework with sensitive variables to improve the worst-case outcomes of individuals caused by sensitive variables that are unavailable at the time of decision. Unlike most existing work that uses mean-optimal objectives, we propose a robust learning framework by finding a newly defined quantile- or infimum-optimal decision rule. From a causal perspective, we also generalize the classic notion of (average) fairness to conditional fairness for individual subjects. The reliable performance of the proposed method is demonstrated through synthetic experiments and three real-data applications.