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 Applied AI: Instructional Materials


Navigating AI to Unpack Youth Privacy Concerns: An In-Depth Exploration and Systematic Review

arXiv.org Artificial Intelligence

This systematic literature review investigates perceptions, concerns, and expectations of young digital citizens regarding privacy in artificial intelligence (AI) systems, focusing on social media platforms, educational technology, gaming systems, and recommendation algorithms. Using a rigorous methodology, the review started with 2,000 papers, narrowed down to 552 after initial screening, and finally refined to 108 for detailed analysis. Data extraction focused on privacy concerns, data-sharing practices, the balance between privacy and utility, trust factors in AI, transparency expectations, and strategies to enhance user control over personal data. Findings reveal significant privacy concerns among young users, including a perceived lack of control over personal information, potential misuse of data by AI, and fears of data breaches and unauthorized access. These issues are worsened by unclear data collection practices and insufficient transparency in AI applications. The intention to share data is closely associated with perceived benefits and data protection assurances. The study also highlights the role of parental mediation and the need for comprehensive education on data privacy. Balancing privacy and utility in AI applications is crucial, as young digital citizens value personalized services but remain wary of privacy risks. Trust in AI is significantly influenced by transparency, reliability, predictable behavior, and clear communication about data usage. Strategies to improve user control over personal data include access to and correction of data, clear consent mechanisms, and robust data protection assurances. The review identifies research gaps and suggests future directions, such as longitudinal studies, multicultural comparisons, and the development of ethical AI frameworks.


The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation

arXiv.org Artificial Intelligence

This report surveys the landscape of potential security threats from malicious uses of AI, and proposes ways to better forecast, prevent, and mitigate these threats. After analyzing the ways in which AI may influence the threat landscape in the digital, physical, and political domains, we make four high-level recommendations for AI researchers and other stakeholders. We also suggest several promising areas for further research that could expand the portfolio of defenses, or make attacks less effective or harder to execute. Finally, we discuss, but do not conclusively resolve, the long-term equilibrium of attackers and defenders.


Introduction to AI Safety, Ethics, and Society

arXiv.org Artificial Intelligence

Artificial Intelligence is rapidly embedding itself within militaries, economies, and societies, reshaping their very foundations. Given the depth and breadth of its consequences, it has never been more pressing to understand how to ensure that AI systems are safe, ethical, and have a positive societal impact. This book aims to provide a comprehensive approach to understanding AI risk. Our primary goals include consolidating fragmented knowledge on AI risk, increasing the precision of core ideas, and reducing barriers to entry by making content simpler and more comprehensible. The book has been designed to be accessible to readers from diverse backgrounds. You do not need to have studied AI, philosophy, or other such topics. The content is skimmable and somewhat modular, so that you can choose which chapters to read. We introduce mathematical formulas in a few places to specify claims more precisely, but readers should be able to understand the main points without these.


From chalkboards to chatbots: SELAR assists teachers in embracing AI in the curriculum

arXiv.org Artificial Intelligence

This paper introduces SELAR, a framework designed to effectively help teachers integrate artificial intelligence (AI) into their curriculum. The framework was designed by running workshops organized to gather lecturers' feedback. In this paper, we assess the effectiveness of the framework through additional workshops organized with lecturers from the Hague University of Applied Sciences. The workshops tested the application of the framework to adapt existing courses to leverage generative AI technology. Each participant was tasked to apply SELAR to one of their learning goals in order to evaluate AI integration potential and, if successful, to update the teaching methods accordingly. Findings show that teachers were able to effectively use the SELAR to integrate generative AI into their courses. Future work will focus on providing additional guidance and examples to use the framework more effectively.


Strategic AI Governance: Insights from Leading Nations

arXiv.org Artificial Intelligence

Artificial Intelligence (AI) has the potential to revolutionize various sectors, yet its adoption is often hindered by concerns about data privacy, security, and the understanding of AI capabilities. This paper synthesizes AI governance approaches, strategic themes, and enablers and challenges for AI adoption by reviewing national AI strategies from leading nations. The key contribution is the development of an EPIC (Education, Partnership, Infrastructure, Community) framework, which maps AI implementation requirements to fully realize social impacts and public good from successful and sustained AI deployment. Through a multi-perspective content analysis of the latest AI strategy documents, this paper provides a structured comparison of AI governance strategies across nations. The findings offer valuable insights for governments, academics, industries, and communities to enable responsible and trustworthy AI deployments. Future work should focus on incorporating specific requirements for developing countries and applying the strategies to specific AI applications, industries, and the public sector.


Here's how ed-tech companies are pitching AI to teachers

MIT Technology Review

But this year, more and more educational technology companies are pitching schools on a different use of AI. Rather than scrambling to tamp down the use of it in the classroom, these companies are coaching teachers how to use AI tools to cut down on time they spend on tasks like grading, providing feedback to students, or planning lessons. One company, called Magic School, says its AI tools like quiz generators and text summarizers are used by 2.5 million educators. Khan Academy offers a digital tutor called Khanmigo, which it bills to teachers as "your free, AI-powered teaching assistant." Teachers can use it to assist students in subjects ranging from coding to humanities.


FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applications

arXiv.org Artificial Intelligence

The integration of Artificial Intelligence (AI) into education has transformative potential, providing tailored learning experiences and creative instructional approaches. However, the inherent biases in AI algorithms hinder this improvement by unintentionally perpetuating prejudice against specific demographics, especially in human-centered applications like education. This survey delves deeply into the developing topic of algorithmic fairness in educational contexts, providing a comprehensive evaluation of the diverse literature on fairness, bias, and ethics in AI-driven educational applications. It identifies the common forms of biases, such as data-related, algorithmic, and user-interaction, that fundamentally undermine the accomplishment of fairness in AI teaching aids. By outlining existing techniques for mitigating these biases, ranging from varied data gathering to algorithmic fairness interventions, the survey emphasizes the critical role of ethical considerations and legal frameworks in shaping a more equitable educational environment. Furthermore, it guides readers through the complexities of fairness measurements, methods, and datasets, shedding light on the way to bias reduction. Despite these gains, this survey highlights long-standing issues, such as achieving a balance between fairness and accuracy, as well as the need for diverse datasets. Overcoming these challenges and ensuring the ethical and fair use of AI's promise in education call for a collaborative, interdisciplinary approach.


Promoting AI Competencies for Medical Students: A Scoping Review on Frameworks, Programs, and Tools

arXiv.org Artificial Intelligence

As more clinical workflows continue to be augmented by artificial intelligence (AI), AI literacy among physicians will become a critical requirement for ensuring safe and ethical AI-enabled patient care. Despite the evolving importance of AI in healthcare, the extent to which it has been adopted into traditional and often-overloaded medical curricula is currently unknown. In a scoping review of 1,699 articles published between January 2016 and June 2024, we identified 18 studies which propose guiding frameworks, and 11 studies documenting real-world instruction, centered around the integration of AI into medical education. We found that comprehensive guidelines will require greater clinical relevance and personalization to suit medical student interests and career trajectories. Current efforts highlight discrepancies in the teaching guidelines, emphasizing AI evaluation and ethics over technical topics such as data science and coding. Additionally, we identified several challenges associated with integrating AI training into the medical education program, including a lack of guidelines to define medical students AI literacy, a perceived lack of proven clinical value, and a scarcity of qualified instructors. With this knowledge, we propose an AI literacy framework to define competencies for medical students. To prioritize relevant and personalized AI education, we categorize literacy into four dimensions: Foundational, Practical, Experimental, and Ethical, with tailored learning objectives to the pre-clinical, clinical, and clinical research stages of medical education. This review provides a road map for developing practical and relevant education strategies for building an AI-competent healthcare workforce.


The Rise of Artificial Intelligence in Educational Measurement: Opportunities and Ethical Challenges

arXiv.org Artificial Intelligence

The integration of artificial intelligence (AI) in educational measurement has revolutionized assessment methods, enabling automated scoring, rapid content analysis, and personalized feedback through machine learning and natural language processing. These advancements provide timely, consistent feedback and valuable insights into student performance, thereby enhancing the assessment experience. However, the deployment of AI in education also raises significant ethical concerns regarding validity, reliability, transparency, fairness, and equity. Issues such as algorithmic bias and the opacity of AI decision-making processes pose risks of perpetuating inequalities and affecting assessment outcomes. Responding to these concerns, various stakeholders, including educators, policymakers, and organizations, have developed guidelines to ensure ethical AI use in education. The National Council of Measurement in Education's Special Interest Group on AI in Measurement and Education (AIME) also focuses on establishing ethical standards and advancing research in this area. In this paper, a diverse group of AIME members examines the ethical implications of AI-powered tools in educational measurement, explores significant challenges such as automation bias and environmental impact, and proposes solutions to ensure AI's responsible and effective use in education.


Challenging the Machine: Contestability in Government AI Systems

arXiv.org Artificial Intelligence

In an October 2023 executive order (EO), President Biden issued a detailed but largely aspirational road map for the safe and responsible development and use of artificial intelligence (AI). The challenge for the January 24-25, 2024 workshop was to transform those aspirations regarding one specific but crucial issue -- the ability of individuals to challenge government decisions made about themselves -- into actionable guidance enabling agencies to develop, procure, and use genuinely contestable advanced automated decision-making systems. While the Administration has taken important steps since the October 2023 EO, the insights garnered from our workshop remain highly relevant, as the requirements for contestability of advanced decision-making systems are not yet fully defined or implemented. The workshop brought together technologists, members of government agencies and civil society organizations, litigators, and researchers in an intensive two-day meeting that examined the challenges that users, developers, and agencies faced in enabling contestability in light of advanced automated decision-making systems. To ensure a free and open flow of discussion, the meeting was held under a modified version of the Chatham House rule. Participants were free to use any information or details that they learned, but they may not attribute any remarks made at the meeting by the identity or the affiliation of the speaker. Thus, the workshop summary that follows anonymizes speakers and their affiliation. Where an identification of an agency, company, or organization is made, it is done from a public, identified resource and does not necessarily reflect statements made by participants at the workshop. This document is a report of that workshop, along with recommendations and explanatory material.