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⇱ Big Data, Artificial Intelligence, and Ethics | Coursera


Big Data, Artificial Intelligence, and Ethics

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Big Data, Artificial Intelligence, and Ethics

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Gain insight into a topic and learn the fundamentals.
4.6

755 reviews

Beginner level
No prior experience required
Flexible schedule
9 hours to complete
Learn at your own pace
93%
Most learners liked this course

Gain insight into a topic and learn the fundamentals.
4.6

755 reviews

Beginner level
No prior experience required
Flexible schedule
9 hours to complete
Learn at your own pace
93%
Most learners liked this course

What you'll learn

  • Define and discuss big data opportunities and limitations.

  • Work with IBM Watson and analyze a personality through Natural Language Programming (NLP).

  • Examine how AI is used through case studies.

  • Examine and discuss the roles ethics play in AI and big data.

Details to know

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Assessments

6 assignmentsΒΉ

AI Graded see disclaimer
Taught in English

Build your subject-matter expertise

This course is part of the Computational Social Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 3 modules in this course

This course gives you context and first-hand experience with the two major catalyzers of the computational science revolution: big data and artificial intelligence. With more than 99% of all mediated information in digital format and with 98% of the world population using digital technology, humanity produces an impressive digital footprint. In theory, this provides unprecedented opportunities to understand and shape society. In practice, the only way this information deluge can be processed is through using the same digital technologies that produced it. Data is the fuel, but machine learning it the motor to extract remarkable new knowledge from vasts amounts of data. Since an important part of this data is about ourselves, using algorithms in order to learn more about ourselves naturally leads to ethical questions. Therefore, we cannot finish this course without also talking about research ethics and about some of the old and new lines computational social scientists have to keep in mind. As hands-on labs, you will use IBM Watson’s artificial intelligence to extract the personality of people from their digital text traces, and you will experience the power and limitations of machine learning by teaching two teachable machines from Google yourself.

In this module, you will discover the history of artificial intelligence (AI) and its fields of study. You'll be able to examine how AI is used through case studies. You will be able to discuss the application of AI and you will use AI to create a unique artifact through a hands-on exercise.

What's included

18 videos2 readings2 assignments

18 videosβ€’Total 114 minutes
  • Course Introductionβ€’4 minutes
  • Introduction to the Good, the Bad, and the... Hopeful!β€’9 minutes
  • Educationβ€’5 minutes
  • Technologyβ€’4 minutes
  • Businessβ€’4 minutes
  • AI Agents & Multi-Agent Systemsβ€’9 minutes
  • Creativityβ€’9 minutes
  • Healthβ€’6 minutes
  • Politics & Democracyβ€’8 minutes
  • Loveβ€’3 minutes
  • The Godfather of AIβ€’7 minutes
  • The Machine Learning Paradigmβ€’6 minutes
  • Virtue Ethics: dataβ€’3 minutes
  • Deontology: WTF?β€’9 minutes
  • Consequentialism - Part 1β€’4 minutes
  • Consequentialism - Part 2β€’7 minutes
  • Consequentialism - Part 3β€’6 minutes
  • Final Reflections on AI & Ethicsβ€’9 minutes
2 readingsβ€’Total 11 minutes
  • About UCCSSβ€’10 minutes
  • Course Navigation and Safety Statementβ€’1 minute
2 assignmentsβ€’Total 35 minutes
  • Natural Language Processing (NLP)β€’30 minutes
  • Natural Language Processing (NLP) Assignment Task β€’5 minutes

In this module, you will be able to define the idea of big data and digital footprint. You will be able to discuss how big data is represented in social science and identify the opportunities of big data. You will also be able to explain the limitations of big data. You will work with an AI interface, and discover how AI can identify personality through Natural Language Processing (NLP).

What's included

15 videos1 reading3 assignments

15 videosβ€’Total 118 minutes
  • Big Data Overviewβ€’2 minutes
  • What is "Big Data"?β€’14 minutes
  • Digital Footprintβ€’6 minutes
  • Political Data-fusion and No-Sampling (Part 1)β€’14 minutes
  • Political Data-fusion and No-Sampling (Part 2)β€’4 minutes
  • Real-timeβ€’12 minutes
  • Machine Learningβ€’6 minutes
  • Machine Learning Recommender Systemsβ€’11 minutes
  • Introduction to Big Data Limitationsβ€’2 minutes
  • Footprint β‰  Representativenessβ€’10 minutes
  • Data β‰  Realityβ€’6 minutes
  • Meaning β‰  Meaningfulβ€’5 minutes
  • Discrimination β‰  Personalizationβ€’9 minutes
  • Correlation β‰  Causationβ€’7 minutes
  • Past β‰  Futureβ€’11 minutes
1 readingβ€’Total 10 minutes
  • Optional/Complementaryβ€’10 minutes
3 assignmentsβ€’Total 90 minutes
  • Big Data Opportunities Quiz β€’30 minutes
  • Big Data Limitations Quiz β€’30 minutes
  • Playing With Creative AIβ€’30 minutes

In this module, you will be able to define the term ethics. You will be able to examine the role ethics plays in social media. You will be able to discuss how ethics is applied when using AI and big data.

What's included

11 videos1 reading1 assignment

11 videosβ€’Total 96 minutes
  • Human Downgradingβ€’6 minutes
  • Attention Economyβ€’8 minutes
  • Exploiting Cognitive Biasesβ€’8 minutes
  • Persuasive Tech is Everywhereβ€’9 minutes
  • Digital Exit Strategy - Part 1β€’8 minutes
  • Digital Exit Strategy - Part 2β€’3 minutes
  • Digital Exit Strategy - Part 3β€’5 minutes
  • Digital Exit Strategy - Part 4β€’13 minutes
  • Walker on Ethics (Complementary)β€’10 minutes
  • Shelton on Ethics (Complementary)β€’8 minutes
  • What is this Specialization About? (intro to 5-course Specialization)β€’18 minutes
1 readingβ€’Total 8 minutes
  • Slaughterbots (Complementary)β€’8 minutes
1 assignmentβ€’Total 30 minutes
  • Module 3 Assignmentβ€’30 minutes

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Instructor

Instructor ratings
4.8 (257 ratings)
University of California, Davis
10 Coursesβ€’97,892 learners

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Showing 3 of 755

RS
Β·

Reviewed on Aug 15, 2023

VERY KNOWLEDGEABLE,INFORMATIVE,WELL PREPARED AND WELL PRESENTED COURSERA COURSE ON BIG DATA,AI AND ETHICS. THE COURSE IS ALSO WELL PRESENTED BY INSTRUCTORS.

AG
Β·

Reviewed on Mar 4, 2025

Eye opening. I expected something more grounded and technical, but it was really interested, though not exactly what I expected.

AG
Β·

Reviewed on Jun 11, 2020

This is a very comprehensive course that gives a very good understanding of what we mean by Big Data, how it is utilized and relevance to the data and AI in our lufe.

Frequently asked questions

These are some of the reflections shared by students who have worked through the content of the Specialization on Computational Social Science:

  • "Highly enjoyable and most importantly, giving me exceptionally important skills to fulfill my job requirements at a new position in Munich. You may be interested to know the impact of your course on salary and in my case, the knowledge and certification gained adds about another Euro 20.000 on the annual salary (taking it to about Euro 120.000 p.a.)."

  • "My overall impression of this was: I can't wait to use this for other stuff!!"

  • "Best course I have taken. I wish more online courses structured like this would be offered."

  • "The fact that these tools are so easily usable and attainable is incredible in my mind. Not only do we have access to them like we have access to things like Facebook and Twitter, but they're FREE."

  • "I absolutely think that these tools could be used in my future jobs, or even as a personal reflection. If you scrape and analyze the comments/reactions that your business gets on Youtube, Twitter, Instagram, etc., what does their language use say about how they interact with your brand β€” or what your brand brings out in them?"

  • "Wow, this is cool and fun stuff. Even though I may not pursue anything social-science related in the near future, it is still nice to learn and get to experience all of these tools that computational social science offers and benefits in all kinds of careers and fields of study."

  • "I particularly enjoyed the web-scraping for some reason. It feels very advanced although its very easy. ...It seems to be a very fast and efficient way of grabbing data."

  • "I enjoyed playing around with machine learning! ...It was also amazing to me how quickly it was able to grasp and learn our input in seconds. It makes me wonder how much more technology will advance in these next few years... It's scary but fascinating."

  • "The most interesting aspect was the fact that these tools are all free and online. In the past, only researchers at well-funded universities had access to programs like the ones we used in all of our labs. But now, even someone without much technical knowledge on complex software can use these tools."

  • "I am so surprised that these tools are available to anyone through a simple download, and even more so that they are very user friendly and easy to learn how to navigate. I plan on starting a clothing line company in the future and I think it will be really helpful for me to be able to analyze so much online data."

  • "As an Environmental Policy Analysis and Planning major, I was fascinated to learn that there is a feasible way to simulate policy implementation and impact multiple times within a short span of time."

  • "UCCSS has allowed me to feel more confident in my abilities with a computer and to better understand companies like Facebook or Twitter. ...these tools really are powerful but also dangerous. ...It allows powerful individuals to manipulate ideas."

  • "Throughout the course, the content was challenging, but when it was finally applied to the labs at the end of each module, it was really rewarding to see everything play out. It was even more rewarding when it made sense too! ... I'm really glad I took this course! It was definitely a challenge, but I'm glad I got to experience and learn about so many topics I never knew even existed."

  • "It was fun seeing the results of the code that I made, and I never thought that I would be doing something like this in my life. The results also showed me what the society would look like.... Social network analysis and web scraping could be the tools that I use in my future job as all the internship that I'm looking now all related to social media or digital media."

  • "My career aspiration is to be a digital marketing expert. These computational tools have enormous implications for the field."

  • "I really really loved that this class let me learn hands-on and gave me experience with tools that have real world application and combine STEM & social science. I think that a lot of these tools are useful far beyond homework activities."

  • "I did my MA in Social Work in India. I am trying to make a come-back in my field after a long career break. I had been hearing Big Data and Data Science everywhere and wondered if there is a link between these and Social Sciences. This specialization gave me needed answers and is helping me to gain very useful skills... Thank you so much for bringing this specialization. You are a very good instructor and made these courses are a smooth sail."

This Specialization on Computational Social Science is the result of a collective effort with contributions from Professors from all 10 campuses of the University of California. It is coordinated by Martin Hilbert, from UC Davis, and counts with lectures from:

1) UC Berkeley: Joshua Blumenstock, Prof. iSchool; Stuart Russell, Professor of Computer Science and Engineering.

2) UC Davis: Martin Hilbert, Prof., Dpt. of Communication & Seth Frey, Prof., Dpt. of Communication & Cynthia Gates, Director of the IRB.

3) UC Irvine: Lisa Pearl, Prof. Cognitive Sciences.

4) UC Los Angeles: PJ Lamberson, Assistant Prof. Communication Studies.

5) UC Merced: Paul Smaldino, Prof. Cognitive and Information Sciences.

6) UC Riverside: Christian Shelton, Prof. Computer Science.

7) UC San Diego: James Fowler, Prof. Global Public Health and Political Science.

8) UC San Francisco: Maria Glymour, Associate Prof. School of Medicine, Social Epidemiology & Biostatistics.

9) UC Santa Barbara: RenΓ© Weber, Prof. Dpt. of Communication & Media Neuroscience Lab (with Frederic Hopp).

10) UC Santa Cruz: Marilyn Walker, Prof. Computer Science, Director, Computational Media.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,

ΒΉ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.