Educating a Responsible AI Workforce: Piloting a Curricular Module on AI Policy in a Graduate Machine Learning Course
- URL: http://arxiv.org/abs/2502.07931v1
- Date: Tue, 11 Feb 2025 20:16:56 GMT
- Title: Educating a Responsible AI Workforce: Piloting a Curricular Module on AI Policy in a Graduate Machine Learning Course
- Authors: James Weichert, Hoda Eldardiry,
- Abstract summary: This paper describes a two-lecture 'AI policy module' that was piloted in a graduate-level introductory machine learning course in 2024.
We find that the module is successful in engaging otherwise technically-oriented students on the topic of AI policy.
- Score: 2.117841684082203
- License:
- Abstract: As artificial intelligence (AI) technologies begin to permeate diverse fields-from healthcare to education-consumers, researchers and policymakers are increasingly raising concerns about whether and how AI is regulated. It is therefore reasonable to anticipate that alignment with principles of 'ethical' or 'responsible' AI, as well as compliance with law and policy, will form an increasingly important part of AI development. Yet, for the most part, the conventional computer science curriculum is ill-equipped to prepare students for these challenges. To this end, we seek to explore how new educational content related to AI ethics and AI policy can be integrated into both ethics- and technical-focused courses. This paper describes a two-lecture 'AI policy module' that was piloted in a graduate-level introductory machine learning course in 2024. The module, which includes an in-class active learning game, is evaluated using data from student surveys before and after the lectures, and pedagogical motivations and considerations are discussed. We find that the module is successful in engaging otherwise technically-oriented students on the topic of AI policy, increasing student awareness of the social impacts of a variety of AI technologies and developing student interest in the field of AI regulation.
Related papers
- Generative AI Literacy: Twelve Defining Competencies [48.90506360377104]
This paper introduces a competency-based model for generative artificial intelligence (AI) literacy covering essential skills and knowledge areas necessary to interact with generative AI.
The competencies range from foundational AI literacy to prompt engineering and programming skills, including ethical and legal considerations.
These twelve competencies offer a framework for individuals, policymakers, government officials, and educators looking to navigate and take advantage of the potential of generative AI responsibly.
arXiv Detail & Related papers (2024-11-29T14:55:15Z) - AI Literacy for All: Adjustable Interdisciplinary Socio-technical Curriculum [0.8879149917735942]
This paper presents a curriculum, "AI Literacy for All," to promote an interdisciplinary understanding of AI.
The paper presents four pillars of AI literacy: understanding the scope and technical dimensions of AI, learning how to interact with Gen-AI in an informed and responsible way, the socio-technical issues of ethical and responsible AI, and the social and future implications of AI.
arXiv Detail & Related papers (2024-09-02T13:13:53Z) - Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions [101.67121669727354]
Recent advancements in AI have highlighted the importance of guiding AI systems towards the intended goals, ethical principles, and values of individuals and groups, a concept broadly recognized as alignment.
The lack of clarified definitions and scopes of human-AI alignment poses a significant obstacle, hampering collaborative efforts across research domains to achieve this alignment.
We introduce a systematic review of over 400 papers published between 2019 and January 2024, spanning multiple domains such as Human-Computer Interaction (HCI), Natural Language Processing (NLP), Machine Learning (ML)
arXiv Detail & Related papers (2024-06-13T16:03:25Z) - Visions of a Discipline: Analyzing Introductory AI Courses on YouTube [11.209406323898019]
We analyze the 20 most watched introductory AI courses on YouTube.
Introductory AI courses do not meaningfully engage with ethical or societal challenges of AI.
We recommend that introductory AI courses should highlight ethical challenges of AI to present a more balanced perspective.
arXiv Detail & Related papers (2024-05-31T01:48:42Z) - Responsible AI: Portraits with Intelligent Bibliometrics [30.51687434548628]
This study defined responsible AI and identified its core principles.
Empirically, this study investigated 17,799 research articles contributed by the AI community since 2015.
An analysis of a core cohort comprising 380 articles from multiple disciplines captures the most recent advancements in responsible AI.
arXiv Detail & Related papers (2024-05-05T08:40:22Z) - From Algorithm Worship to the Art of Human Learning: Insights from 50-year journey of AI in Education [0.0]
Current discourse surrounding Artificial Intelligence (AI) oscillates between hope and apprehension.
This paper delves into the complexities of AI's role in Education, addressing the mixed messages that have both enthused and alarmed educators.
It explores the promises that AI holds for enhancing learning through personalisation at scale, against the backdrop of concerns about ethical implications.
arXiv Detail & Related papers (2024-02-05T16:12:14Z) - Report of the 1st Workshop on Generative AI and Law [78.62063815165968]
This report presents the takeaways of the inaugural Workshop on Generative AI and Law (GenLaw)
A cross-disciplinary group of practitioners and scholars from computer science and law convened to discuss the technical, doctrinal, and policy challenges presented by law for Generative AI.
arXiv Detail & Related papers (2023-11-11T04:13:37Z) - Is AI Changing the Rules of Academic Misconduct? An In-depth Look at
Students' Perceptions of 'AI-giarism' [0.0]
This study explores students' perceptions of AI-giarism, an emergent form of academic dishonesty involving AI and plagiarism.
The findings portray a complex landscape of understanding, with clear disapproval for direct AI content generation.
The study provides pivotal insights for academia, policy-making, and the broader integration of AI technology in education.
arXiv Detail & Related papers (2023-06-06T02:22:08Z) - Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and
Stir" [76.44130385507894]
This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices.
Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
arXiv Detail & Related papers (2021-11-01T17:57:04Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.