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There are 5 modules in this course
Computing systems and technologies fundamentally impact the lives of most people in the world, including how we communicate, get information, socialize, and receive healthcare. This course is the second of a three course sequence that examines ethical issues in the design and implementation of computing systems and technologies, and reflects upon the broad implication of computing on our society. It covers algorithmic bias in machine learning methods, professional ethics, and issues in the tech workplace.
This course can be taken for academic credit as part of CU Boulder’s MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees 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
In this introductory week, you will delve into the fascinating world of computing, ethics, and society. You will explore the fundamental concepts of ethics and ethical frameworks, providing a solid foundation for the entire course. You will gain insights into key ethical theories, including Kantianism, Virtue Ethics, Utilitarianism, and Social Contract Theory. Through interactive discussions and engaging resources, you will understand how these theories shape our moral decision-making processes and their significance in the context of computing technologies.
What's included
6 videos14 readings2 assignments1 peer review
Show info about module content
6 videos•Total 66 minutes
Course Overview: Part 1•11 minutes
Course Overview: Part 2•13 minutes
Kantianism and Virtue Ethics: Part 1•8 minutes
Kantianism and Virtue Ethics: Part 2•9 minutes
Utilitarianism and Social Contract Theory: Part 1•12 minutes
Utilitarianism and Social Contract Theory: Part 2•12 minutes
14 readings•Total 166 minutes
Course Updates and Accessibility Support•1 minute
Earn Academic Credit for your Work!•10 minutes
Assessment Expectations•5 minutes
AI Citation and Acknowledgement •10 minutes
Course Support•10 minutes
Introduction to Ethics•10 minutes
Applying an Ethical Framework•10 minutes
"The Trolley Problem" - Video•10 minutes
Kantianism/Deontology and Virtue Ethics•15 minutes
Optional Readings on Virtue Ethics and Ethical Theories•30 minutes
Utilitarianism and Social Contract Theory•15 minutes
Should Batman Kill the Joker? •15 minutes
ACM Code of Ethics and Professional Conduct Studies (Malware)•10 minutes
Optional Reading on Utilitarianism •15 minutes
2 assignments•Total 20 minutes
AI Policy Quiz•5 minutes
Ethical Theories•15 minutes
1 peer review•Total 60 minutes
Professional Ethics Case Study•60 minutes
Algorithmic Bias
Module 2•11 hours to complete
Module details
This week, you'll explore algorithmic bias, focusing on the ethical dimensions of facial recognition technology. The beginning of the week will be dedicated to an overview of algorithmic bias, its prevalence, and its consequences. Then, you'll examine real-world instances of algorithmic bias. Towards the end of the week, you'll focus on facial recognition technology by exploring its mechanics, addressing the concerning of racial bias, and the legal and regulatory challenges.
Algorithmic Bias and Other Ethical Issues in Facial Recognition: Part 1•15 minutes
Algorithmic Bias and Other Ethical Issues in Facial Recognition: Part 2•18 minutes
Legal and Regulatory Issues in Facial Recognition: Part 1•14 minutes
Legal and Regulatory Issues in Facial Recognition: Part 2•13 minutes
15 readings•Total 349 minutes
How Problematic is Algorithmic Bias?•25 minutes
How I'm fighting bias in algorithms - TED Talk•9 minutes
The Defense for Algorithmic Bias•20 minutes
A Call for Greater AI Transparency in NYC•10 minutes
Amazon Bot Fires Long-Time Employee Over Nothing•15 minutes
Gender and Race Bias in AI•75 minutes
New NYC AI Tool Goes Live•15 minutes
Optional: AI Makes Robots Racist and Sexist•15 minutes
How Does Facial Recognition Work?•10 minutes
Racial Bias in Facial Recognition •45 minutes
Europe's Reaction to Facial Recognition Technology•20 minutes
Optional: More About How Facial Recognition Technology Works•25 minutes
Limiting the Use of Facial Recognition •30 minutes
The Relationship Between AI and Law Enforcement•20 minutes
Optional: AI Police Use on the Rise/First Regulation Act on AI•15 minutes
1 peer review•Total 90 minutes
Algorithmic Bias•90 minutes
2 discussion prompts•Total 90 minutes
Brookings Institution Study •45 minutes
Potential Uses of Facial Recognition •45 minutes
Gender and Race in Computing
Module 3•6 hours to complete
Module details
This week you'll explore the intersections of gender, race, and algorithms. In the beginning of the week, you'll revisit algorithmic bias focusing bias related to gender and race. You'll explore the advantages and disadvantages of employing AI in hiring processes and unravel the complexities of predictive policing with AI, shedding light on its drawbacks and ethical implications. Then, you'll narrow focus to Gender and Race in Algorithms, specifically addressing the pervasive issue of racial bias in AI systems. Throughout this week, you'll gain a comprehensive understanding of the ethical challenges posed by AI in hiring and policing, as well as the broader implications of gender and race biases in algorithms, empowering you to critically assess and navigate these critical topics in the realm of technology and ethics.
Algorithmic Bias Related to Gender and Race: Part 1•20 minutes
Algorithmic Bias Related to Gender and Race: Part 2•17 minutes
Gender and Race in Algorithms: Part 1•9 minutes
Gender and Race in Algorithms: Part 2•12 minutes
5 readings•Total 145 minutes
The Pros and Cons of Using AI to Hire People•35 minutes
The Drawbacks of Predictive Policing with AI•25 minutes
Facebook Advertisements Infused with Racial Bias•20 minutes
Racial Bias in Speech, Hospital, and Fitness Algorithms •40 minutes
Optional: Companies Using Facebook to Isolate Older Workers•25 minutes
1 assignment•Total 15 minutes
Facial Recognition Data•15 minutes
1 peer review•Total 90 minutes
Gender and Race in Computing •90 minutes
1 discussion prompt•Total 45 minutes
Algorithmic Hiring•45 minutes
Professional Ethics, including Gender and Race in the Tech Workforce
Module 4•8 hours to complete
Module details
This week is dedicated to professional ethics in computing and considerations of gender and race in the tech workforce. The first lesson will lay the foundational principles of ethical conduct in the computing industry, examining the ACM code of ethics as a guiding framework. Then, you'll explore the critical need for diversity in the workforce and the ethical considerations surrounding it. Finally, we'll spotlight real-world examples, including diversity and gender biases at tech giant Google, and delve into the compelling story of Timnit Gebru, shedding light on the challenges and opportunities in building a more inclusive and ethical computing workforce. Throughout this week, you'll gain a comprehensive understanding of the ethical dimensions of technology and the importance of diversity and inclusion in shaping the future of computing.
Professional Ethics in Computing: Part 1•12 minutes
Professional Ethics in Computing: Part 2•12 minutes
Gender and Race in the Computing Workforce: Part 1•17 minutes
Gender and Race in the Computing Workforce: Part 2•15 minutes
Diversity in the Computing Workforce: Part 1•13 minutes
Diversity in the Computing Workforce: Part 2•13 minutes
Professional Interview Reports: Part 1•11 minutes
Professional Interview Reports: Part 2•10 minutes
8 readings•Total 245 minutes
ACM Code of Ethics•30 minutes
Ethical Considerations in the Apple-FBI Case•5 minutes
A Shift in the Tech Industry•60 minutes
Bad Behavior at Uber•30 minutes
Optional: Gender-Balanced Teams in the Tech Industry •30 minutes
Debating About Diversity and Gender Biases at Google•35 minutes
The Story of Timnit Gebru•50 minutes
Leader of Apple Activist Movement Fired by the Company •5 minutes
1 assignment•Total 10 minutes
Gender Diversity in Tech •10 minutes
1 peer review•Total 90 minutes
Professional Interview Report •90 minutes
1 discussion prompt•Total 45 minutes
Article Discussion•45 minutes
Generative AI and the Future of AI
Module 5•7 hours to complete
Module details
In this final week you'll explore the ethical dimensions of artificial intelligence. The beginning of the week will venture into the captivating yet challenging world of generative AI, unraveling the potential dangers of its applications while demystifying what generative AI truly entails. Then you'll look to the future of AI, where you'll navigate the complex ethical terrain that emerges as AI technologies continue to advance. Throughout this week, you will develop a profound understanding of the ethical concerns that accompany AI's evolution, equipping you with the knowledge to engage thoughtfully and responsibly with this transformative technology.
Ethical Issues Related to Generative AI: Part 1•18 minutes
Ethical Issues Related to Generative AI: Part 2•11 minutes
Ethical Issues Related to the Future of AI: Part 1•12 minutes
Ethical Issues Related to the Future of AI: Part 2•15 minutes
6 readings•Total 282 minutes
ChatGPT: Bans and Controversies•40 minutes
The Danger of Using AI•30 minutes
Optional: What is Generative AI? •120 minutes
TED Talk: What happens when our computers get smarter than we are?•17 minutes
Superintelligence: What's the Big Idea?•45 minutes
Optional: Can Humans and AI Co-exist? •30 minutes
1 assignment•Total 10 minutes
AI Regulations•10 minutes
2 discussion prompts•Total 90 minutes
Article Discussion •45 minutes
Generative AI Concerns•45 minutes
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This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
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Build toward a degree
This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
¹Successful application and enrollment are required. Eligibility requirements apply. Each institution determines the number of credits recognized by completing this content that may count towards degree requirements, considering any existing credits you may have. Click on a specific course for more information.
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