Responsible Data Science
This course, for both data science practitioners and managers, provides guidance and tools to build better models that avoid bias and unfairness.
Overview
Public and corporate concern about bias and other unintended harmful effects resulting from data science models has resulted in greater attention to the ethical practice of data science. This course, for both data science practitioners and managers, provides guidance and practical tools to build better models and avoid these problems. The course offers a framework to follow in implementing data science projects, and an audit process to follow in reviewing them. Case studies along with R and Python code are provided.
- Intermediate
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
This course covers processes and tools to minimize bias and unfairness in predictive models, particularly those using black-box algorithms. You will learn how to:
- Identify the types of unintended harm that can arise from AI models
- Explain why interpretability is key to avoiding harm
- Distinguish between intrinsically interpretable models and black box models
- Evaluate tradeoffs between model performance and interpretability
- Establish and implement a Responsible Data Science framework for your projects
- Evaluate predictor impact in black box models using interpretability methods
- Assess the performance of models with metrics to measure bias and unfairness
- Conduct an audit of a data science project from an ethical standpoint
Who Should Take This Course
Data Science architects and programmers, managers of data science projects and teams.
Our Instructors
Mr. Peter Bruce
Course Syllabus
Week 1
- Review of predictive modeling
- Why Responsible Data Science?
- Types of harms
- The black box problem
- Legal considerations, legal
Week 2
- Why interpretability is an ethical issue
- Interpretability versus performance tradeoff
- Establishing baseline
- Intrinsically-interpretable algorithms
- Interpretability for black-box algorithms
- Global interpretability
- Local interpretability
Week 3
- The RDS Framework
- Enhancing standard “best practices” from an ethical standpoint
- The 10 RDS questions to answer
Week 4
The Audit
- Metrics for assessing bias
- Assessment of results of applying local and global interpretability methods
- Presentation
- Technical
- Nontechnical
- Auditing for neural nets (briefly)
Class Dates
2023
Instructors:
2024
Instructors:
Instructors:
2025
Instructors:
Instructors:
Prerequisites
You should be familiar with predictive modeling and able to work in R or Python. Either of the following courses is good preparation:
Predictive Analytics 1 with Python – Machine Learning Tools
- Skill: Intermediate
- Credit Options: CEU
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Additional Information
Time Requirements
About 15 hours per week, at times of your choosing.
Homework
Homework consists primarily of practical exercises with R or Python.
Course Text
The required text for this course is Responsible Data Science, Wiley, by Grant Fleming, Peter Bruce. Please order a copy of your course textbook prior to course start date.”
Software
Python or R
Course Fee & Information
Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.
Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.
Group Rates
Contact us to get information on group rates.
Discounts
Academic affiliation? In most courses you are eligible for a discount at checkout. Use promo code ACADEMIC where prompted during registration.
Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.
Supplemental Information
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