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Epaminondas Kapetanios
You will be able to use the Aequitas Tool as a tool to measure and detect bias in the outcome of a machine learning prediction model. As a use case, we will be working with the dataset about recidivism, i.e., the likelihood for a former imprisoned person to commit another offence within the first two years, since release from prison. The guided project will be making use of the COMPAS dataset, which already includes predicted as well as actual outcomes. Given also that this technique is largely based on statistical descriptors for measuring bias and fairness, it is very independent from specific Machine Learning (ML) prediction models. In this sense, the project will boost your career not only as a Data Scientists or ML developer, but also as a policy and decision maker.
Register for this course and see more details by visiting:
OpenCourser.com/course/aouuut/interpretable
What's inside
Syllabus
Project Overview
By the end of this project, you will be able to use the Aequitas Tool as a tool to measure and detect bias in the outcome of a machine learning prediction model. As a use case, we will be working with the dataset about recidivism, i.e., the likelihood for a former imprisoned person to commit another offence within the first two years, since release from prison. The guided project will be making use of the COMPAS dataset, which already includes predicted as well as actual outcomes. Given also that this technique is largely based on statistical descriptors for measuring bias and fairness, it is very independent from specific Machine Learning (ML) prediction models. In this sense, the project will boost your career not only as a Data Scientists or ML developer, but also as a policy and decision maker.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines measuring and detecting bias in outcomes of machine learning prediction models
Uses real-world datasets (COMPAS dataset) for a practical understanding of bias mitigation
Provides a comprehensive overview of the Aequitas toolkit for bias assessment
Applicable to various roles, including data scientists, ML developers, policy, and decision-makers
No explicit prerequisites or prior knowledge requirements specified
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Reviews summary
Interpretable machine learning course
According to students, instructional difficulties bring down an otherwise good course on interpretable machine learning. Students say that the instructor's window is too narrow and that the rationale behind steps performed is not clear. However, students also say that there is good content available.
This interpretable machine learning course has good content.
"Good "
"Good content, but hard to follow the instructor and do as he does"
There are several instructional difficulties in this course.
"Good content, but hard to follow the instructor and do as he does"
"It was very difficult to follow the project because the instructor's window did not allow the complete script to be displayed"
"the rationale behind the steps performed was not provided"
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 Interpretable machine learning applications: Part 5 with these
activities:
Review previous coursework and study materials
Show steps
Reviewing previous knowledge in statistical descriptors for measuring bias and fairness will help understand the course content more effectively.
Show steps
-
Gather notes, assignments, quizzes, and exams from previous courses related to bias measurement, fairness in machine learning, or machine learning models.
-
Review the materials, focusing on key concepts, definitions, and examples.
-
Take practice questions or complete exercises to test your understanding.
Review Machine Learning Concepts
Show steps
Refresh your understanding of machine learning concepts, which are essential for understanding bias.
Browse courses on
Machine Learning
Show steps
-
Review fundamental machine learning algorithms such as linear regression and decision trees.
-
Explore advanced topics such as overfitting and underfitting.
-
Practice implementing machine learning models.
Volunteer with organizations working on bias mitigation in machine learning
Show steps
Volunteering will provide practical experience in applying bias mitigation techniques and contribute to real-world impact.
Browse courses on
Volunteering
Show steps
-
Identify organizations working on bias mitigation in machine learning or related fields.
-
Contact the organizations and inquire about volunteer opportunities.
-
Participate in projects or initiatives that focus on bias mitigation.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Review Statistics
Show steps
Refresh your foundational knowledge of statistics, which is essential for understanding data bias.
Browse courses on
Statistics
Show steps
-
Review basic statistical concepts such as mean, median, and standard deviation.
-
Practice applying statistical techniques to real-world datasets.
Practice using the Aequitas Tool
Show steps
Hands-on practice with the Aequitas Tool will help master the techniques for measuring and detecting bias in machine learning prediction models.
Show steps
-
Install and set up the Aequitas Tool.
-
Load and explore the COMPAS dataset.
-
Use the Aequitas Tool to measure and detect bias in the dataset.
-
Experiment with different parameters and settings to understand how they affect the results.
Solve Aequitas Tool Practice Problems
Show steps
Develop your proficiency in using the Aequitas Tool by completing practice problems.
Show steps
-
Access the online Aequitas Tool practice problems.
-
Solve practice problems of varying difficulty levels.
-
Review your answers and identify areas for improvement.
Contribute to open-source projects related to bias mitigation
Show steps
Contributing to open-source projects will provide hands-on experience in developing and refining bias mitigation techniques.
Browse courses on
Open Source
Show steps
-
Identify open-source projects related to bias mitigation or fair machine learning.
-
Review the project's documentation and codebase.
-
Identify areas where you can contribute.
-
Make code contributions, bug fixes, or documentation improvements.
Attend Industry Conferences
Show steps
Connect with professionals in the field and stay up-to-date on the latest trends in bias mitigation.
Show steps
-
Research and identify relevant industry conferences.
-
Attend conference sessions and workshops on bias detection and mitigation.
-
Network with attendees and exchange knowledge.
Peer Review of Bias Mitigation Projects
Show steps
Provide feedback and learn from peers by reviewing each other's bias mitigation projects.
Show steps
-
Form peer review groups and exchange projects.
-
Review projects and provide constructive feedback on the methodology and findings.
-
Incorporate feedback into your own project to improve its quality.
Develop a presentation on bias mitigation strategies
Show steps
Creating a comprehensive presentation on bias mitigation strategies will reinforce understanding and encourage critical thinking about the ethical implications of machine learning.
Browse courses on
Bias Mitigation
Show steps
-
Research and identify different bias mitigation strategies.
-
Evaluate the pros and cons of each strategy.
-
Develop a presentation that clearly explains the strategies and their implications.
-
Present the findings to peers or mentors for feedback.
Bias Analysis Project
Show steps
Apply your understanding of bias to a real-world dataset and create a report on your findings.
Show steps
-
Identify a dataset of interest and define the target variable.
-
Use the Aequitas Tool to analyze bias in the dataset.
-
Write a report summarizing your findings and recommendations for mitigating bias.
Annotated Bibliography on Bias Mitigation
Show steps
Build your understanding of the literature on bias mitigation and become familiar with the state-of-the-art.
Show steps
-
Identify and gather relevant research papers and articles on bias mitigation.
-
Read and summarize the key findings and contributions of each paper.
-
Organize and present your findings in an annotated bibliography.
Contribute to the Aequitas Tool GitHub Repository
Show steps
Gain practical experience by contributing to the development of the open-source Aequitas Tool.
Show steps
-
Familiarize yourself with the Aequitas Tool GitHub repository.
-
Identify a bug or propose a feature enhancement.
-
Create a pull request and collaborate with the project maintainers.
Participate in a machine learning competition focused on bias mitigation
Show steps
Engaging in a competitive environment will provide practical experience in applying bias mitigation techniques and foster a deeper understanding of real-world challenges.
Show steps
-
Identify and register for a machine learning competition that focuses on bias mitigation.
-
Form a team or work independently on the competition.
-
Develop and implement a machine learning model that mitigates bias.
-
Submit the model and results to the competition.
Review previous coursework and study materials
Show steps
Reviewing previous knowledge in statistical descriptors for measuring bias and fairness will help understand the course content more effectively.
Show steps
Show steps
Show steps
- Gather notes, assignments, quizzes, and exams from previous courses related to bias measurement, fairness in machine learning, or machine learning models.
- Review the materials, focusing on key concepts, definitions, and examples.
- Take practice questions or complete exercises to test your understanding.
Review Machine Learning Concepts
Show steps
Refresh your understanding of machine learning concepts, which are essential for understanding bias.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review fundamental machine learning algorithms such as linear regression and decision trees.
- Explore advanced topics such as overfitting and underfitting.
- Practice implementing machine learning models.
Volunteer with organizations working on bias mitigation in machine learning
Show steps
Volunteering will provide practical experience in applying bias mitigation techniques and contribute to real-world impact.
Browse courses on
Volunteering
Show steps
Show steps
Show steps
- Identify organizations working on bias mitigation in machine learning or related fields.
- Contact the organizations and inquire about volunteer opportunities.
- Participate in projects or initiatives that focus on bias mitigation.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Review Statistics
Show steps
Refresh your foundational knowledge of statistics, which is essential for understanding data bias.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review basic statistical concepts such as mean, median, and standard deviation.
- Practice applying statistical techniques to real-world datasets.
Practice using the Aequitas Tool
Show steps
Hands-on practice with the Aequitas Tool will help master the techniques for measuring and detecting bias in machine learning prediction models.
Show steps
Show steps
Show steps
- Install and set up the Aequitas Tool.
- Load and explore the COMPAS dataset.
- Use the Aequitas Tool to measure and detect bias in the dataset.
- Experiment with different parameters and settings to understand how they affect the results.
Solve Aequitas Tool Practice Problems
Show steps
Develop your proficiency in using the Aequitas Tool by completing practice problems.
Show steps
Show steps
Show steps
- Access the online Aequitas Tool practice problems.
- Solve practice problems of varying difficulty levels.
- Review your answers and identify areas for improvement.
Contribute to open-source projects related to bias mitigation
Show steps
Contributing to open-source projects will provide hands-on experience in developing and refining bias mitigation techniques.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open-source projects related to bias mitigation or fair machine learning.
- Review the project's documentation and codebase.
- Identify areas where you can contribute.
- Make code contributions, bug fixes, or documentation improvements.
Attend Industry Conferences
Show steps
Connect with professionals in the field and stay up-to-date on the latest trends in bias mitigation.
Show steps
Show steps
Show steps
- Research and identify relevant industry conferences.
- Attend conference sessions and workshops on bias detection and mitigation.
- Network with attendees and exchange knowledge.
Peer Review of Bias Mitigation Projects
Show steps
Provide feedback and learn from peers by reviewing each other's bias mitigation projects.
Show steps
Show steps
Show steps
- Form peer review groups and exchange projects.
- Review projects and provide constructive feedback on the methodology and findings.
- Incorporate feedback into your own project to improve its quality.
Develop a presentation on bias mitigation strategies
Show steps
Creating a comprehensive presentation on bias mitigation strategies will reinforce understanding and encourage critical thinking about the ethical implications of machine learning.
Browse courses on
Bias Mitigation
Show steps
Show steps
Show steps
- Research and identify different bias mitigation strategies.
- Evaluate the pros and cons of each strategy.
- Develop a presentation that clearly explains the strategies and their implications.
- Present the findings to peers or mentors for feedback.
Bias Analysis Project
Show steps
Apply your understanding of bias to a real-world dataset and create a report on your findings.
Show steps
Show steps
Show steps
- Identify a dataset of interest and define the target variable.
- Use the Aequitas Tool to analyze bias in the dataset.
- Write a report summarizing your findings and recommendations for mitigating bias.
Annotated Bibliography on Bias Mitigation
Show steps
Build your understanding of the literature on bias mitigation and become familiar with the state-of-the-art.
Show steps
Show steps
Show steps
- Identify and gather relevant research papers and articles on bias mitigation.
- Read and summarize the key findings and contributions of each paper.
- Organize and present your findings in an annotated bibliography.
Contribute to the Aequitas Tool GitHub Repository
Show steps
Gain practical experience by contributing to the development of the open-source Aequitas Tool.
Show steps
Show steps
Show steps
- Familiarize yourself with the Aequitas Tool GitHub repository.
- Identify a bug or propose a feature enhancement.
- Create a pull request and collaborate with the project maintainers.
Participate in a machine learning competition focused on bias mitigation
Show steps
Engaging in a competitive environment will provide practical experience in applying bias mitigation techniques and foster a deeper understanding of real-world challenges.
Show steps
Show steps
Show steps
- Identify and register for a machine learning competition that focuses on bias mitigation.
- Form a team or work independently on the competition.
- Develop and implement a machine learning model that mitigates bias.
- Submit the model and results to the competition.
Career center
Learners who complete Interpretable machine learning applications: Part 5 will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
The Interpretable Machine Learning Applications: Part 5 course can help build a foundation for a career as a Data Scientist. Aequitas Tool is a valuable tool for assessing the fairness of machine learning models. Data Scientists can use Aequitas to identify and mitigate bias in their models, a critical skill for building trustworthy and ethical AI systems. This course will also provide learners with valuable insights into the challenges and best practices of developing and deploying machine learning models in the real world.
Machine Learning Engineer
Machine Learning Engineer
For a Machine Learning Engineer, the Interpretable Machine Learning Applications: Part 5 course can be a valuable addition to their skillset. By mastering the Aequitas Tool, Machine Learning Engineers can develop models that are not only accurate but also fair and unbiased. This can lead to significant benefits in downstream applications such as decision-making and policy creation.
Data Analyst
Data Analyst
The Interpretable Machine Learning Applications: Part 5 course can provide a solid foundation for a career as a Data Analyst. Aequitas Tool is a powerful tool for analyzing and interpreting data, and it is valuable for Data Analysts who want to build models that are reliable and unbiased. This course will also help learners develop the critical thinking and communication skills necessary for success in this field.
Software Engineer
Software Engineer
The Interpretable Machine Learning Applications: Part 5 course can help build a foundation for a career as a Software Engineer. Aequitas Tool can be used to ensure that machine learning models are accurate and unbiased, which is increasingly important as AI becomes more prevalent in software applications. This course also provides a strong foundation in the principles and techniques of machine learning, which is essential for Software Engineers who want to build robust and reliable software systems.
Statistician
Statistician
The Interpretable Machine Learning Applications: Part 5 course can help build a foundation for a career as a Statistician. Aequitas Tool is a valuable tool for analyzing and interpreting data, and this course provides a strong foundation in the statistical principles that underpin Aequitas. Statisticians can use Aequitas to develop models that are reliable and unbiased, which is essential for making informed decisions based on data.
Risk Manager
Risk Manager
The Interpretable Machine Learning Applications: Part 5 course can be useful for individuals pursuing a career as a Risk Manager. Aequitas Tool can be used to assess the risks associated with machine learning models, and this course provides a strong foundation in the principles and techniques of machine learning. Risk Managers can use Aequitas to identify and mitigate risks associated with machine learning, which is increasingly important as AI becomes more prevalent in risk management.
Policy Analyst
Policy Analyst
The Interpretable Machine Learning Applications: Part 5 course can be useful for individuals pursuing a career as a Policy Analyst. Aequitas Tool can be used to assess the fairness and bias of machine learning models, which is increasingly important as AI becomes more prevalent in policy-making. Policy Analysts can use Aequitas to identify and mitigate bias in machine learning models, which can lead to more equitable and just policies.
Business Analyst
Business Analyst
The Interpretable Machine Learning Applications: Part 5 course can be useful for individuals pursuing a career as a Business Analyst. Aequitas Tool can be used to analyze and interpret data, and this course provides a strong foundation in the principles and techniques of machine learning. Business Analysts can use Aequitas to develop models that are reliable and unbiased, which can lead to better business decisions.
Data Engineer
Data Engineer
The Interpretable Machine Learning Applications: Part 5 course may be useful for individuals pursuing a career as a Data Engineer. Aequitas Tool can be used to assess the quality and accuracy of machine learning models, and this course provides a strong foundation in the principles and techniques of machine learning. Data Engineers can use Aequitas to build and maintain machine learning pipelines that produce reliable and unbiased models.
Auditor
Auditor
The Interpretable Machine Learning Applications: Part 5 course may be useful for individuals pursuing a career as an Auditor. Aequitas Tool can be used to assess the fairness and bias of machine learning models, and this course provides a strong foundation in the principles and techniques of machine learning. Auditors can use Aequitas to identify and mitigate bias in machine learning models, which is increasingly important as AI becomes more prevalent in auditing.
Consultant
Consultant
The Interpretable Machine Learning Applications: Part 5 course may be useful for individuals pursuing a career as a Consultant. Aequitas Tool can be used to assess the risks and benefits of machine learning models, and this course provides a strong foundation in the principles and techniques of machine learning. Consultants can use Aequitas to provide guidance to clients on how to use machine learning in a responsible and ethical manner.
Project Manager
Project Manager
The Interpretable Machine Learning Applications: Part 5 course may be useful for individuals pursuing a career as a Project Manager. Aequitas Tool can be used to track and monitor the progress of machine learning projects, and this course provides a strong foundation in the principles and techniques of machine learning. Project Managers can use Aequitas to ensure that machine learning projects are delivered on time and within budget.
Teacher
Teacher
The Interpretable Machine Learning Applications: Part 5 course may be useful for individuals pursuing a career as a Teacher. Aequitas Tool can be used to teach students about the principles and techniques of machine learning, and this course provides a strong foundation in the principles and techniques of machine learning. Teachers can use Aequitas to develop lesson plans and assignments that will help students learn about machine learning in a fun and engaging way.
Researcher
Researcher
The Interpretable Machine Learning Applications: Part 5 course may be useful for individuals pursuing a career as a Researcher. Aequitas Tool can be used to conduct research on the fairness and bias of machine learning models, and this course provides a strong foundation in the principles and techniques of machine learning. Researchers can use Aequitas to develop new methods for detecting and mitigating bias in machine learning models.
Entrepreneur
Entrepreneur
The Interpretable Machine Learning Applications: Part 5 course may be useful for individuals pursuing a career as an Entrepreneur. Aequitas Tool can be used to develop new products and services that use machine learning in a responsible and ethical manner, and this course provides a strong foundation in the principles and techniques of machine learning. Entrepreneurs can use Aequitas to create businesses that will have a positive impact on the world.
For more career information including salaries, visit:
OpenCourser.com/course/aouuut/interpretable
Reading list
We've selected ten 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
Interpretable machine learning applications: Part 5.
Provides a comprehensive overview of interpretable machine learning methods, including both model-agnostic and model-specific techniques. It is an excellent resource for anyone who wants to learn more about interpretability in machine learning.
Provides a comprehensive overview of artificial intelligence, including its history, different types, and potential applications. It is an excellent resource for anyone who wants to learn more about AI.
Provides a comprehensive overview of the algorithmic foundations of discrimination, including different types of discrimination, case studies, and solutions. It is an excellent resource for anyone who wants to learn more about algorithmic discrimination.
Provides a practical guide to machine learning, with a focus on using Scikit-Learn, Keras, and TensorFlow. It covers topics such as data preprocessing, model selection, and model evaluation.
Save
Provides a comprehensive overview of deep learning, with a focus on the mathematical and algorithmic foundations. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Provides a practical guide to machine learning and deep learning using Python, scikit-learn, and TensorFlow 2. It covers topics such as data preprocessing, model selection, and model evaluation.
Provides a practical guide to data science for business professionals. It covers topics such as data mining, data analysis, and machine learning.
Provides a comprehensive overview of statistical learning, with a focus on data mining, inference, and prediction. It covers topics such as linear regression, logistic regression, and decision trees.
Provides a comprehensive overview of pattern recognition and machine learning, with a focus on the mathematical and algorithmic foundations. It covers topics such as Bayesian inference, decision theory, and neural networks.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers topics such as Bayesian inference, graphical models, and reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/aouuut/interpretable
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Effort
1.5 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Epaminondas Kapetanios
Language
English
Good to know
Examines measuring and detecting bias in outcomes of machine learning prediction models
Uses real-world datasets (COMPAS dataset) for a practical understanding of bias mitigation
Provides a comprehensive overview of the Aequitas toolkit for bias assessment
Applicable to various roles, including data scientists, ML developers, policy, and decision-makers
No explicit prerequisites or prior knowledge requirements specified
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