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Brent Summers
In this course, we will explore fundamental issues of fairness and bias in machine learning. As predictive models begin making important decisions, from college admission to loan decisions, it becomes paramount to keep models from making unfair predictions. From human bias to dataset awareness, we will explore many aspects of building more ethical models.
Register for this course and see more details by visiting:
OpenCourser.com/course/obvbtw/artificial
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What's inside
Syllabus
Fairness and protections in machine learning
Welcome to the course! In week one, we will be discussing what fairness means in the context of machine learning and what true parity means in different scenarios
Read more
Syllabus
Fairness and protections in machine learning
Welcome to the course! In week one, we will be discussing what fairness means in the context of machine learning and what true parity means in different scenarios
Read more
Building fair models: theory and practice
This week we will take action against unfairness. Now that we have an understanding of fairness issues, how do we build models that do not violate them?
Human factors: minimizing bias in data
This week, we will tackle the human biases that enter the data collection and attribute selection processes. The goal? Removing bias before the model is built
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines an emerging topic of ethical awareness in machine learning
Taught by an industry-recognized expert
Multi-modal with hands-on exercises
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Reviews summary
Data fairness for ai models
According to students, this course is a great and interesting introduction to data fairness and bias in artificial intelligence. They particularly appreciate the engaging content and relevant reading materials. Many reviewers emphasize the importance of understanding the impact of bias on AI models.
Learners should note that some students mention occasional technical inaccuracies in the section on machine learning and the tricky phrasing of some review questions. Additionally, a few students express a desire for more written materials beyond the video transcripts.
Relevant and informative content
"Really great discussion of algorithms and how their designs make them susceptible to bias. "
"Some aspects of fairness and bias discussed in the session are good. I hope there is still a scope to improve the content."
"En lo personal me pareció un curso muy bueno, tal y como dice el titulo es lo que enseñan, y lo enseñan de una muy buena manera hay cosas con las que me quede con duda pero las resolveré próximamente en los foros"
Relatively short course
"A relatively short and interesting course on data fairness and bias impacting AI models."
Occasional technical inaccuracies
"Well-presented but some technical inaccuracies in the Machine Learning portions"
Tricky phrasing of review questions
"The review questions to test the knowledge are tricky and make us focus on the content."
Limited written materials
"While the course content is very interesting and good, there needs to be some actual notes, beyond the video transcript available to students"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Artificial Intelligence Data Fairness and Bias with these
activities:
Review of statistical models and techniques
Show steps
Review fundamental statistical models and techniques to strengthen your understanding of machine learning algorithms.
Browse courses on
Statistical Models
Show steps
-
Revisit the basics of probability and statistics, including distributions, hypothesis testing, and confidence intervals.
-
Review different types of regression models, such as linear, logistic, and decision trees.
-
Refresh your knowledge of supervised and unsupervised learning algorithms.
Reading: The Elements of Statistical Learning
Show steps
Expand your knowledge of statistical learning techniques by reading and analyzing a seminal text in the field, helping you develop a deeper understanding.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
-
Read and study the assigned chapters thoroughly.
-
Attend book club discussions to engage with peers and clarify concepts.
Data exploration and cleaning exercises
Show steps
Gain proficiency in data exploration and cleaning through hands-on exercises, enhancing your ability to work with real-world datasets.
Browse courses on
Data Exploration
Show steps
-
Practice identifying outliers, missing values, and inconsistencies within datasets.
-
Perform data transformations, such as normalization, standardization, and feature scaling.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Discussion forum participation
Show steps
Engage with classmates and seek insights by actively participating in discussion forums, fostering a collaborative learning environment.
Show steps
-
Read and analyze discussion topics thoroughly.
-
Share your thoughts and insights, providing evidence to support your claims.
-
Respond to classmates' posts, asking clarifying questions or offering alternative perspectives.
Peer mentoring
Show steps
Strengthen your understanding of machine learning concepts by mentoring a fellow student, providing guidance and support.
Browse courses on
Mentoring
Show steps
-
Identify a peer who could benefit from your guidance.
-
Establish regular meetings to provide support and answer questions.
-
Share your knowledge and experiences, while also learning from your mentee's unique perspectives.
Data analysis and visualization project
Show steps
Demonstrate your understanding of data analysis and visualization techniques by completing a hands-on project, allowing you to apply your skills to a real-world problem.
Browse courses on
Data Analysis
Show steps
-
Choose a dataset that aligns with your interests.
-
Perform exploratory data analysis to identify patterns and insights.
-
Create data visualizations to convey your findings effectively.
Contribute to an open-source machine learning project
Show steps
Gain practical experience and contribute to the broader machine learning community by participating in an open-source project.
Browse courses on
Machine Learning
Show steps
-
Identify open-source projects that align with your interests and skill level.
-
Review the project documentation and contribute to issues or feature requests.
Contribute to an open-source machine learning library
Show steps
Gain hands-on experience in machine learning algorithms and contribute to the development of a widely used library.
Browse courses on
Machine Learning
Show steps
-
Identify open-source libraries that align with your interests and skill level.
-
Review the library documentation and identify areas where you can contribute.
-
Submit pull requests with bug fixes, feature enhancements, or documentation improvements.
Review of statistical models and techniques
Show steps
Review fundamental statistical models and techniques to strengthen your understanding of machine learning algorithms.
Browse courses on
Statistical Models
Show steps
Show steps
Show steps
- Revisit the basics of probability and statistics, including distributions, hypothesis testing, and confidence intervals.
- Review different types of regression models, such as linear, logistic, and decision trees.
- Refresh your knowledge of supervised and unsupervised learning algorithms.
Reading: The Elements of Statistical Learning
Show steps
Expand your knowledge of statistical learning techniques by reading and analyzing a seminal text in the field, helping you develop a deeper understanding.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
Show steps
Show steps
- Read and study the assigned chapters thoroughly.
- Attend book club discussions to engage with peers and clarify concepts.
Data exploration and cleaning exercises
Show steps
Gain proficiency in data exploration and cleaning through hands-on exercises, enhancing your ability to work with real-world datasets.
Browse courses on
Data Exploration
Show steps
Show steps
Show steps
- Practice identifying outliers, missing values, and inconsistencies within datasets.
- Perform data transformations, such as normalization, standardization, and feature scaling.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Discussion forum participation
Show steps
Engage with classmates and seek insights by actively participating in discussion forums, fostering a collaborative learning environment.
Show steps
Show steps
Show steps
- Read and analyze discussion topics thoroughly.
- Share your thoughts and insights, providing evidence to support your claims.
- Respond to classmates' posts, asking clarifying questions or offering alternative perspectives.
Peer mentoring
Show steps
Strengthen your understanding of machine learning concepts by mentoring a fellow student, providing guidance and support.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify a peer who could benefit from your guidance.
- Establish regular meetings to provide support and answer questions.
- Share your knowledge and experiences, while also learning from your mentee's unique perspectives.
Data analysis and visualization project
Show steps
Demonstrate your understanding of data analysis and visualization techniques by completing a hands-on project, allowing you to apply your skills to a real-world problem.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Choose a dataset that aligns with your interests.
- Perform exploratory data analysis to identify patterns and insights.
- Create data visualizations to convey your findings effectively.
Contribute to an open-source machine learning project
Show steps
Gain practical experience and contribute to the broader machine learning community by participating in an open-source project.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify open-source projects that align with your interests and skill level.
- Review the project documentation and contribute to issues or feature requests.
Contribute to an open-source machine learning library
Show steps
Gain hands-on experience in machine learning algorithms and contribute to the development of a widely used library.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify open-source libraries that align with your interests and skill level.
- Review the library documentation and identify areas where you can contribute.
- Submit pull requests with bug fixes, feature enhancements, or documentation improvements.
Career center
Learners who complete Artificial Intelligence Data Fairness and Bias will develop knowledge and skills
that may be useful to these careers:
Machine Learning Researcher
Machine Learning Researcher
The course Artificial Intelligence Data Fairness and Bias is highly recommended for individuals seeking a career as a Machine Learning Researcher. The course provides a comprehensive understanding of fairness and bias in machine learning, enabling researchers to develop and evaluate machine learning algorithms that are fair and unbiased.
Machine Learning Engineer
Machine Learning Engineer
Those pursuing a career as a Machine Learning Engineer are highly encouraged to consider this course, Artificial Intelligence Data Fairness and Bias, as it dives deep into a topic that is foundational to this field. Machine Learning Engineers must ensure that the models they build are fair and unbiased, and this course provides valuable insights into how to approach this challenge.
Quantitative Analyst
Quantitative Analyst
The course Artificial Intelligence Data Fairness and Bias can be highly relevant for individuals pursuing a career as a Quantitative Analyst. Understanding fairness and bias in data and models is crucial in the financial industry, where Quantitative Analysts rely on data to make critical investment decisions.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Individuals interested in pursuing a career as an Artificial Intelligence Engineer should highly consider the course Artificial Intelligence Data Fairness and Bias. This course explores the intricacies of AI, a field deeply rooted in fairness and bias concerns. By understanding these issues, AI Engineers can build AI systems that are equitable and responsible.
Data Governance Specialist
Data Governance Specialist
Those aspiring towards a career as a Data Governance Specialist should consider taking the course Artificial Intelligence Data Fairness and Bias. This course explores the importance of fairness and bias in data governance, providing individuals with the knowledge and skills to implement and manage data governance frameworks that promote fairness and mitigate bias.
Data Engineer
Data Engineer
Those seeking a career as a Data Engineer may find the course Artificial Intelligence Data Fairness and Bias to be beneficial. The course explores the importance of fairness and bias in machine learning, allowing Data Engineers to build data pipelines and systems that are fair and unbiased.
Risk Analyst
Risk Analyst
For those interested in a career as a Risk Analyst, the course Artificial Intelligence Data Fairness and Bias is highly recommended. Risk Analysts must assess and mitigate risks, and understanding fairness and bias in data and models is essential for making accurate and informed risk assessments.
Computer Scientist
Computer Scientist
The course Artificial Intelligence Data Fairness and Bias can be valuable for those pursuing a career as a Computer Scientist. As computer scientists increasingly work with data and machine learning, they need to understand and address fairness and bias issues. This course provides a solid foundation in these critical areas.
Actuary
Actuary
The course Artificial Intelligence Data Fairness and Bias may be useful for individuals pursuing a career as an Actuary. Actuaries analyze and manage risks, and understanding fairness and bias in data and models is essential for accurate risk assessments and financial projections.
Statistician
Statistician
For individuals considering a career as a Statistician, the course Artificial Intelligence Data Fairness and Bias may be highly relevant. The course delves into the complexities of fairness and bias in machine learning, issues that Statisticians must address when analyzing and interpreting data.
Data Analyst
Data Analyst
For those aspiring towards a career as a Data Analyst, the course Artificial Intelligence Data Fairness and Bias may prove beneficial. The course grapples with fairness and bias in machine learning, concepts that Data Analysts must grapple with to ensure the accuracy and reliability of data-driven insights and decision-making.
Policy Analyst
Policy Analyst
The course Artificial Intelligence Data Fairness and Bias may be useful for those looking to become Policy Analysts. As policies are increasingly shaped by data and machine learning, Policy Analysts must understand the potential biases and fairness issues associated with these technologies to develop and evaluate effective policies.
Business Analyst
Business Analyst
The course Artificial Intelligence Data Fairness and Bias may be useful for those aiming for a career as a Business Analyst. As businesses rely more on data-driven decision-making, Business Analysts must be able to identify and address biases and fairness issues in data and models to ensure accurate and ethical analysis.
Software Engineer
Software Engineer
The course Artificial Intelligence Data Fairness and Bias may be useful for individuals pursuing a career as a Software Engineer. In the realm of software development, bias and fairness play a crucial role in building ethical and responsible systems. This course empowers Software Engineers with the knowledge to address these challenges effectively.
Data Scientist
Data Scientist
This course, Artificial Intelligence Data Fairness and Bias, may be useful for those looking to build a career as a Data Scientist. This course delves into the complexities of fairness and bias in machine learning, issues that Data Scientists regularly encounter and address in their work.
For more career information including salaries, visit:
OpenCourser.com/course/obvbtw/artificial
Reading list
We've selected 11 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
Artificial Intelligence Data Fairness and Bias .
Provides a comprehensive overview of the ethical and legal issues surrounding fairness in machine learning. It valuable resource for anyone interested in understanding the challenges and opportunities of using machine learning to make fair and equitable decisions.
Explores the ethical challenges of developing and deploying AI systems. It provides a framework for thinking about fairness, accountability, and transparency in AI.
Save
Provides a comprehensive overview of deep learning techniques. It valuable resource for anyone interested in learning about the theory and practice of deep learning.
Explores the economic implications of AI. It provides a framework for thinking about the impact of AI on jobs, wages, and inequality.
Explores the potential risks of AI. It provides a framework for thinking about how to develop AI systems that are safe and beneficial for humanity.
Save
Explores the potential risks and benefits of superintelligence. It provides a comprehensive overview of the challenges and opportunities of developing AI systems that are more intelligent than humans.
Save
Explores the history and future of AI. It provides a comprehensive overview of the different approaches to AI and the potential impact of AI on society.
Save
Explores the potential impact of AI on society. It provides a comprehensive overview of the challenges and opportunities of AI and offers a vision for a future where AI and humans can coexist harmoniously.
Explores the ethical and social implications of AI. It provides a comprehensive overview of the different ways that AI is being used and the potential impact of AI on our lives.
Save
Explores the potential future of humanity. It provides a comprehensive overview of the different ways that AI is being used and the potential impact of AI on our lives.
Save
Explores the potential future of humanity. It provides a comprehensive overview of the different ways that AI is being used and the potential impact of AI on our lives.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/obvbtw/artificial
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Level
Introductory
Via
Coursera
Institution
LearnQuest
Instructor
Brent Summers
Language
English
Transcripts
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This course belongs to a
collection
called
Ethics in the Age of AI. It's comprised of four courses:
- Artificial Intelligence Algorithms Models and Limitations
- Artificial Intelligence Data Fairness and Bias (viewing)
- Artificial Intelligence Privacy and Convenience
- Artificial Intelligence Ethics in Action
Good to know
Examines an emerging topic of ethical awareness in machine learning
Taught by an industry-recognized expert
Multi-modal with hands-on exercises
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