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Bobby Schnabel

Artificial intelligence, particularly with the introduction of generative AI, is fundamentally impacting a huge spectrum of human experience. This ranges from the vast majority of jobs and professions to education, healthcare, media and entertainment, and much more. These rapid advances have been accompanied by a huge array of fundamental and greatly impactful ethical challenges, including bias and inaccuracy in AI systems, where AI systems should and shouldn’t replace humans, and potential benefits and harms from various types of autonomous systems, and existential issues including the future of human work and the possibility of AI systems whose intelligence exceeds that of humans.

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Artificial intelligence, particularly with the introduction of generative AI, is fundamentally impacting a huge spectrum of human experience. This ranges from the vast majority of jobs and professions to education, healthcare, media and entertainment, and much more. These rapid advances have been accompanied by a huge array of fundamental and greatly impactful ethical challenges, including bias and inaccuracy in AI systems, where AI systems should and shouldn’t replace humans, and potential benefits and harms from various types of autonomous systems, and existential issues including the future of human work and the possibility of AI systems whose intelligence exceeds that of humans.

This course provides students with a broad exposure to the ethical issues arising from AI, along with the experience and ethical tools to analyze them. It is intended to help students recognize and deal with these issues in their professional careers and their lives. It is based on a combination of current media articles and recent research papers, and is designed so that it can be kept current by refreshing the references it is based on.

This course can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS) and the Master of Science in Artificial Intelligence (MS-AI) degrees offered on the Coursera platform. This fully accredited graduate degree offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more:

MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder

MS in Artificial Intelligence: https://www.coursera.org/degrees/ms-artificial-intelligence-boulder

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Syllabus

Course and Ethics Overview
In the first module of this course introduces you to the topic of current ethical issues in AI from several perspectives. After providing an overview of the course including the learning objectives and what work is required of you, you’ll gain insights into key ethical theories that will underlie discussions throughout the course. These include Kantianism, Virtue Ethics, Utilitarianism, and Social Contract Theory. Then we’ll further motivate the course by looking at key ethical concerns that have been identified related to the uses of AI, and the range and pace of AI development and use.
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Learners who complete Current Issues in Ethics and AI will develop knowledge and skills that may be useful to these careers:
AI Ethicist
An AI Ethicist guides the responsible development and deployment of artificial intelligence systems, proactively identifying, analyzing, and mitigating potential ethical risks to ensure fairness, transparency, and accountability. The "Current Issues in Ethics and AI" course is exceptionally well-suited for aspiring AI Ethicists. It provides broad exposure to the vast array of ethical challenges, from bias in large language models to the societal impacts of artificial general intelligence and the future of human work. By engaging with ethical theories like Kantianism, alongside discussions on specific sectors such as healthcare and criminal justice, learners acquire essential analytical tools. The course’s focus on current media and research ensures an up-to-date, practical understanding, indispensable for navigating AI's complex ethical landscape in a professional career.
AI Trust and Safety Specialist
An AI Trust and Safety Specialist ensures that AI systems are deployed in a safe, fair, and trustworthy manner for users and society. This involves developing policies, implementing safeguards, and investigating incidents related to harmful AI outputs, such as misinformation, bias, or privacy breaches. The "Current Issues in Ethics and AI" course is exceptionally relevant for an AI Trust and Safety Specialist. It provides broad exposure to the ethical issues arising from AI, with specific modules dedicated to fairness, bias, and accuracy of algorithms, including identity-related bias and misinformation in large language models. The course also explores ethical issues in critical sectors like healthcare. By equipping learners with ethical tools to analyze these issues, the course directly prepares them to recognize and deal with the complex challenges involved in maintaining trust and safety in AI systems.
Chief Ethics Officer AI
A Chief Ethics Officer AI is a senior executive responsible for establishing and overseeing an organization's ethical AI policies, ensuring compliance, and fostering a culture of responsible innovation. This strategic role requires a comprehensive understanding of AI's ethical landscape. The "Current Issues in Ethics and AI" course provides an exceptional foundation for a Chief Ethics Officer AI, offering broad exposure to the ethical issues arising from AI, from fundamental concerns like bias and privacy to existential issues such as AGI and the future of human work. By exploring ethical theories and applying them to diverse sectors, the course helps build the strategic perspective and analytical tools needed to lead an organization's ethical AI agenda, recognize and deal with complex challenges, and shape its ethical future in the AI era. This role typically requires an advanced degree.
Applied Research Scientist AI Ethics
An Applied Research Scientist AI Ethics conducts research to develop practical methods, tools, and frameworks for ensuring the ethical and responsible deployment of artificial intelligence. This role bridges theoretical ethics with real-world AI challenges. This course, "Current Issues in Ethics and AI," is highly relevant for an Applied Research Scientist AI Ethics. It provides broad exposure to the ethical issues arising from AI, grounded in current media articles and recent research papers, ideal for a research-oriented role. The course delves into key ethical theories (Kantianism, Utilitarianism) and specific applications like bias in large language models, ethical issues in healthcare AI, and the societal impacts of AGI. This comprehensive understanding equips learners with analytical tools and foundational knowledge to effectively identify research gaps and contribute to the rapidly evolving field of AI ethics. This role typically requires an advanced degree.
Responsible AI Leader
A Responsible AI Leader champions the integration of ethical principles throughout an organization's AI lifecycle, from conception to deployment, mitigating risks like algorithmic bias, privacy violations, and misuse of autonomous systems. The "Current Issues in Ethics and AI" course offers an ideal foundation for this career, providing broad exposure to the ethical issues arising from AI and equipping learners with the tools to analyze them. By exploring topics such as fairness in algorithms, ethical issues in healthcare AI, and the regulation of AI, the course helps build the strategic perspective needed to guide teams in developing AI responsibly. This specialized knowledge is crucial for anyone aspiring to lead ethical AI initiatives across various industries, enabling them to recognize and deal with these issues in their professional career.
Data Ethicist
A Data Ethicist focuses on the ethical implications of data collection, usage, and algorithmic decision-making, ensuring practices are fair, transparent, and protect privacy. This role often works closely with data scientists. This course, "Current Issues in Ethics and AI," is highly relevant for a Data Ethicist. It provides broad exposure to core ethical issues such as fairness, bias, and accuracy of algorithms, particularly addressing identity-related bias and misinformation in large language models. The course also delves into privacy issues, which are central to data ethics. By exploring general ethical issues in AI and applying ethical tools to analyze them, learners gain a deep understanding of how to recognize and deal with these challenges in professional careers, directly preparing them to advocate for responsible data governance.
Legal Counsel Technology Law
A Legal Counsel Technology Law advises organizations on the legal implications of developing and deploying advanced technologies, including artificial intelligence. This field requires expertise in data privacy, intellectual property, regulatory compliance, and liability related to autonomous systems. The "Current Issues in Ethics and AI" course offers a strong foundation for a Legal Counsel Technology Law professional by providing broad exposure to the ethical issues arising from AI, including discussions on how AI should be regulated. Modules covering privacy issues in large language models, bias, and dilemmas concerning autonomous systems in criminal justice directly inform legal analysis. The course equips learners with ethical tools to analyze complex situations, invaluable for advising on the legal landscape of AI. This role typically requires an advanced degree in law.
AI Policy Analyst
An AI Policy Analyst examines the societal implications of artificial intelligence, helping shape regulations and standards for its ethical development and use. This involves researching emerging AI technologies, assessing their risks, and recommending policy interventions. The "Current Issues in Ethics and AI" course provides a robust framework for an AI Policy Analyst, offering broad exposure to key ethical challenges like bias, privacy, and the future of human work. Detailed modules on AGI, AI regulation, and energy impact directly address core policy concerns. By analyzing current media articles and research papers, learners develop the analytical tools necessary to understand ethical issues and translate them into actionable policy recommendations, which is particularly helpful for those seeking to influence the legislative landscape of AI.
AI Consultant
An AI Consultant advises organizations on strategies for implementing artificial intelligence solutions, navigating complex technical, business, and ethical considerations. They help clients identify opportunities, manage risks, and ensure responsible AI adoption. This course, "Current Issues in Ethics and AI," is highly beneficial for an AI Consultant. It provides broad exposure to the ethical issues arising from AI across multiple sectors, including healthcare, criminal justice, and the future of human work. By engaging with ethical theories and analyzing specific challenges like bias, autonomous systems, and regulation, learners develop the analytical tools to assess complex situations. This course helps consultants recognize and deal with these issues in their professional careers, enabling them to provide comprehensive and ethically sound advice to diverse clients seeking to leverage AI technologies responsibly.
Product Manager AI Focused
A Product Manager AI Focused guides the development of artificial intelligence products, ensuring they meet user needs while adhering to ethical design principles. This involves translating complex ethical considerations, such as algorithmic bias or data privacy, into actionable product requirements. The "Current Issues in Ethics and AI" course may be useful for a Product Manager AI Focused by providing broad exposure to the ethical issues impacting AI in various sectors, from healthcare to media. Understanding core ethical theories and specific challenges like identity-related bias in large language models helps in proactively addressing potential harms during product conceptualization and development, fostering a responsible approach to AI product innovation that resonates with societal expectations.
Social Impact Manager AI
A Social Impact Manager AI focuses on understanding and mitigating the broader societal consequences of artificial intelligence, working to ensure AI development serves public good and addresses issues such as equity, access, and workforce displacement. The "Current Issues in Ethics and AI" course is highly relevant for a Social Impact Manager AI by providing broad exposure to the ethical issues arising from AI and its fundamental impact on human experience. Modules specifically cover the societal impacts of AI, including the future of human work and employment, and discussions on how AI should be regulated. By exploring these topics and engaging with ethical tools to analyze them, learners are well-prepared to recognize and deal with the complex ethical challenges in their professional careers, enabling them to strategically guide AI initiatives towards positive social outcomes.
Technical Program Manager AI
A Technical Program Manager AI oversees complex artificial intelligence projects, coordinating engineering, research, and product teams. This role demands an understanding of ethical considerations to ensure AI development integrates responsible practices, manages risks, and complies with emerging standards. The "Current Issues in Ethics and AI" course may be useful for a Technical Program Manager AI by providing broad exposure to the ethical issues arising from generative AI and its impacts on various aspects of human experience. Understanding topics like bias in large language models, privacy concerns, and the implications of autonomous systems helps in guiding development teams to proactively address these challenges, fostering a mindset crucial for successfully managing AI initiatives that are both innovative and ethically sound.
Machine Learning Engineer Ethical AI
A Machine Learning Engineer Ethical AI designs, develops, and deploys models with a deliberate focus on ethical considerations, such as fairness, transparency, and accountability. This involves implementing techniques to mitigate bias and ensure data privacy. The "Current Issues in Ethics and AI" course may be useful for a Machine Learning Engineer Ethical AI by providing broad exposure to the ethical issues inherent in AI systems, particularly focusing on fairness, bias, and accuracy of algorithms. Modules specifically address identity-related bias in large language models and privacy concerns, providing crucial context for technical solutions. While the course does not teach coding, it helps build the analytical tools and ethical frameworks necessary to understand the "why" behind ethical AI engineering principles, enabling engineers to proactively incorporate responsible practices into their technical work.
User Experience Researcher AI
A User Experience Researcher AI investigates how users interact with artificial intelligence products and systems, focusing on usability, accessibility, and ethical implications. This involves identifying potential harms, biases, or privacy concerns that users might encounter. This course, "Current Issues in Ethics and AI," may be useful for a User Experience Researcher AI by providing broad exposure to the ethical issues arising from AI, particularly concerning bias and misinformation in large language models. Understanding how AI impacts human experience across sectors like media directly informs research into user perception and ethical interaction design. The course helps build the analytical tools needed to identify and deal with these issues, enabling researchers to uncover and articulate user-centered ethical challenges. This perspective is vital for designing AI that is both effective and trustworthy.
Communications Specialist AI
A Communications Specialist AI crafts and disseminates clear, accurate, and ethically informed messages about artificial intelligence technologies and their implications. This involves translating complex technical and ethical concepts for diverse audiences, addressing public concerns, and managing organizational reputation related to AI. The "Current Issues in Ethics and AI" course may be useful for a Communications Specialist AI, providing broad exposure to the ethical issues arising from generative AI and its impact on areas like media. Understanding topics such as misinformation, bias in large language models, and the societal impacts of AI helps in framing responsible and transparent communications. The course equips learners with ethical tools to analyze these issues, crucial for articulating an organization's stance on AI ethics, addressing public anxieties, and building trust through informed and nuanced messaging.

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Current Issues in Ethics and AI.
Provides a groundbreaking analysis of the surveillance capitalism that is shaping our world. It argues that surveillance capitalism new form of capitalism that is based on the exploitation of personal data.
Provides a clear and concise overview of the major ethical theories, from utilitarianism to Kantianism. It is an excellent starting point for anyone who wants to learn more about ethics.
This recent book directly addresses the contemporary topic of remote and hybrid work models, examining the challenges and opportunities they present for both employees and employers. It provides practical insights and analysis on a highly relevant aspect of the future of work.
Reframes AI as a 'prediction machine,' explaining its economic implications in a clear and accessible way. It's excellent for deepening the understanding of AI's impact on business strategy and decision-making, which is crucial for comprehending the future of work in an AI-driven world.
Classic work on the right to privacy. It provides a comprehensive overview of the history of privacy law and the challenges posed by new technologies.
Offers a comprehensive overview of LLMs, covering their theoretical foundations, practical applications, and future directions.
Provides a practical guide to data privacy and security for IT professionals. It covers the technical and legal aspects of data privacy, as well as best practices for implementing a data privacy program.
Provides a comprehensive overview of data protection law. It covers the legal frameworks for data protection in different jurisdictions, as well as the key legal concepts and principles.
Provides a practical guide to the role of the data protection officer (DPO). It covers the legal requirements for DPOs, as well as best practices for implementing a data protection program.
Provides a comprehensive overview of the legal and ethical issues surrounding data privacy, including the collection, use, and disclosure of personal information. It is an essential read for anyone interested in understanding the legal and ethical implications of data privacy.
Provides a practical guide to using generative AI, covering the different techniques and tools available. It is written by two leading experts in the field, Josh Patterson and Adam Gibson.
Offers a comprehensive and critical examination of the entire lifecycle of AI, from the mining of raw materials to the labor involved and the environmental impact. It reveals the hidden costs and power structures behind AI, providing a broader context for understanding how bias is embedded in the very foundation of these systems. This book is excellent for gaining a deep and critical understanding of AI and its societal implications.
Explores the mathematical and algorithmic foundations of fairness and bias in AI systems, providing practical guidance for designing more ethical algorithms.
Provides a deep historical analysis of the relationship between technology and work, offering valuable context for understanding the current wave of automation. It's excellent for those seeking a more academic and in-depth understanding of the long-term trends shaping the future of work.
While not exclusively about AI, this book provides extensive evidence of how a lack of sex-disaggregated data and a male-centric default in design lead to bias and discrimination in various systems, including those that inform AI. It's a crucial read for understanding the importance of inclusive data collection and its impact on fairness in AI.
This report from the AI Now Institute provides a comprehensive overview of the issue of bias in AI systems, exploring its causes and consequences.
A concise precursor to 'The Second Machine Age,' this book offers a quick yet insightful overview of the initial impacts of technological acceleration on employment and inequality. It's valuable for gaining a broad understanding and serves as good prerequisite reading before diving into more detailed analyses.
Delves into the technical aspects of designing algorithms that are socially aware and fair. It's more technical than some of the other recommendations, making it suitable for those with a stronger technical background who want to understand the computational challenges and solutions related to AI bias. This book can serve as a useful reference tool for practitioners and researchers.
This seminal work examines how companies are profiting from the pervasive collection and use of personal data, a phenomenon with significant implications for the nature of work, privacy, and power dynamics in the digital age. It provides a critical perspective on the technological future.
Based on research from Accenture, this book provides a framework for how businesses can integrate AI and human talent to create new forms of work and achieve greater innovation. It's a practical guide for professionals and organizations adapting to AI.
Is considered a foundational text on how computer technology is reshaping the job market and the types of skills that are becoming more valuable. It provides essential background for understanding the historical context of the future of work.
Drawing lessons from the military, this book presents a new model for organizational structure and leadership in a complex and rapidly changing world. It's highly relevant for understanding how teams and organizations need to adapt for the future of work.
Examines the increasing power of algorithms and their lack of transparency in areas like finance, healthcare, and search engines. It discusses how these opaque systems can lead to unfairness and discrimination. This book valuable resource for understanding the broader societal implications of algorithmic decision-making.
Explores the complex challenge of aligning advanced AI systems with human values. While not solely focused on bias, it provides essential context for understanding the broader issues of control, safety, and ethical considerations in the development of powerful AI. It thought-provoking read for those considering the long-term implications of AI.
Explores the rapid convergence of various exponential technologies and their potential to disrupt industries and transform daily life, including work. It offers a forward-looking perspective on the scale and speed of change we can anticipate.
Examines how bias is built into the technology products and systems we use every day, with a focus on the impact on women and other marginalized groups. It provides relatable examples of how biased algorithms can lead to harmful outcomes. This book offers a practical and accessible entry point into understanding bias in technology.
Advocates for a framework of design justice, where technology and design processes are led by communities who are directly impacted by them. It offers practical guidance for creating more equitable and inclusive technologies, which is highly relevant to mitigating bias in AI development.
Offers a feminist perspective on data science, highlighting how power and inequality are embedded in data and algorithms. It provides a set of principles for more ethical and equitable data practices, which are directly applicable to addressing AI bias. This book is valuable for understanding the social and political dimensions of data and AI.
Explores the safety and security challenges posed by AI technologies. It discusses the potential for AI systems to cause harm, and it proposes a number of measures that can be taken to mitigate these risks.
Offers a broad overview of how robots, AI, and automation are impacting the job market and society. It explores policy implications and potential societal responses, making it useful for a general understanding of the challenges and opportunities presented by the future of work.
This handbook provides a comprehensive overview of the legal, ethical, and policy implications of AI. It covers a wide range of topics related to AI governance, including bias and discrimination. valuable reference for researchers, policymakers, and advanced students interested in the regulatory landscape of AI.
Identifies twelve technological forces that will shape the next thirty years, many of which have direct implications for the future of work, such as AI, ubiquitous access, and new forms of interaction. It helps in understanding the broader technological landscape.
Uses case studies to explore ethical challenges in AI, providing a practical approach to understanding and addressing issues like bias, privacy, and accountability. It's a valuable resource for gaining insight into real-world ethical dilemmas in AI development and deployment.
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Challenges the conventional wisdom of extreme specialization, arguing that a breadth of experience and interdisciplinary thinking is increasingly valuable in a complex world. It offers a relevant perspective on the skills needed for the future of work.
Explores new organizational paradigms beyond traditional hierarchical structures, offering insights into more agile, self-managing, and purpose-driven ways of working. It provides a deeper understanding of potential future organizational models.
Examines the legal implications of AI-based systems. It discusses specific case studies and situations in which AI algorithms have led to controversy, unfairness and/or bias. It proposes solutions to address these problems.
This comprehensive handbook includes a chapter on LLMs, providing a thorough overview of their history, evolution, and applications.

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