Applied Data Science Ethics from edX

Concern about the harmful effects of machine learning algorithms and big data AI models (bias and more) has resulted in greater attention to the fundamentals of data ethics. News stories appear regularly about credit algorithms that discriminate against women, medical algorithms that discriminate against African Americans, hiring algorithms that base decisions on gender, and more. In most cases, the data scientists who developed and deployed these decision making algorithms and data processes had no such intentions, and were unaware of the harmful impact of their work.

This data science ethics course, the second in the data science ethics program for both practitioners and managers, provides guidance and practical tools to build better models, do better data analysis and avoid these problems. You’ll learn about ****

Tools for model interpretability
Global versus local model interpretability methods
Metrics for model fairness
Auditing your model for bias and fairness
Remedies for biased models

The course offers real world problems and datasets, a framework data scientists can use to develop their projects, and an audit process to follow in reviewing them. Case studies with ethical considerations, along with Python code, are provided.

What you'll learn

How to evaluate predictor impact in black box models using interpretability methods
How to explain the average contribution of features to predictions and the contribution of individual feature values to individual predictions
How to Assess the performance of models with metrics to measure bias and unfairness
How to describe potential ethical issues that can arise with image and text data, and how to address them
How to donduct an audit of a data science project from an ethical standpoint to identify possible harms and potential areas for bias mitigation or harm reduction

In this course we will mostly be addressing things the data scientist can do to ensure that their projects and solutions are designed and implemented responsibly. We will primarily focus on issues of bias and unfairness across protected groups.

What's inside

Learning objective

How to evaluate predictor impact in black box models using interpretability methods

Syllabus

This course is arranged in 4 modules. We estimate that you will need to spend at least 5 hours per week. The course is self-paced, so you have the flexibility to complete modules in your own time. ****

Week 1 – Audit and Remediation

Videos:

Introduction

Audit and Remediation

Confusion Matrix

Beyond Classic Bias

Regression

Knowledge Checks

Lab 1 (for verified users only)

Discussion Prompt (for verified users only)

Week 2 – Interpretability in Practice

Interpretability

Global Interpretability

Fidelity, Robustness, Caveats

Local Interpretability Methods

Reading

Lab 2 (for verified users only)

Week 3 – Image and Text Data

Image and Text Data

Neural Net Interpretability

Readings

Lab 3 (for verified users only) - will need gmail account for this lab

Week 4 – Tools and Documentation

Tools and Documentation

Quiz (for verified users only)

Please note:

There are 4 modules in total.

Labs are for verified users only. They are 'open book' and there is no set time limit. You will need a gmail account for the lab on Colab (Colabatory on Google) for Week 3.

The exercises involve hands-on work with Python (we will provide useful hints)

You will only have one attempt to answer each exercise.

You can complete the exercises at any time while the course is open, however, we do recommend that you complete them sequentially, after you complete the relevant module.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches skills, knowledge, and tools that are highly relevant to industry

Addresses ethical issues in data science and artificial intelligence

Taught by experienced instructors from the industry

Provides practical tools and real-world problems to reinforce learning

Examines bias and fairness in machine learning algorithms and big data AI models

Requires a gmail account for one of the labs

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 Applied Data Science Ethics with these activities:

Review how machine learning bias is generated

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Review how various techniques and modeling choices affect predictions to better identify potential bias in models.

Browse courses on Machine Learning Bias

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Review sources of bias in ML algorithms
Identify specific examples of unfair and biased model outcomes
Consider real-world consequences and implications of biased AI models

Review Python Basics

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Refresh your knowledge of Python syntax and basic programming concepts to strengthen your foundation for this course.

Browse courses on Python

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Review Python data types, variables, and operators.
Practice writing simple Python functions.

Complete a Tutorial on Model Interpretability

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Go through a guided tutorial to understand different methods for interpreting machine learning models and their predictions.

Browse courses on Model Interpretability

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Choose a tutorial on model interpretability.
Follow the tutorial and implement the code.

Six other activities

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Join a Study Group for Course Review

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Participate in a study group to engage with peers, discuss course content, and reinforce your understanding of the material.

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Find or create a study group with fellow classmates.
Meet regularly to review course material and discuss concepts.

Utilize resources on using interpretability methods

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Examine and work through examples of applying interpretability methods to models, assessing their strengths and limitations.

Browse courses on Explainable AI

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Follow tutorials and examples on using interpretability methods like SHAP and LIME
Experiment with different interpretability techniques on sample datasets
Evaluate the effectiveness of various interpretability methods

Complete Practice Exercises on Bias and Fairness Metrics

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Engage in practice exercises to develop proficiency in evaluating and mitigating bias and fairness issues in machine learning models.

Browse courses on Machine Learning Bias

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Find a set of practice exercises on bias and fairness metrics.
Solve the exercises and analyze the results.

Develop a Short Presentation on Ethical Considerations in Data Analysis

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Create a presentation to demonstrate your understanding of ethical considerations and best practices in data analysis, including potential biases and their impact.

Browse courses on Data Ethics

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Research ethical guidelines and best practices in data analysis.
Develop a presentation outline.
Create slides and prepare your presentation.

Volunteer for a Data Ethics Project

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Gain hands-on experience by volunteering for a project that promotes ethical practices in data analysis and artificial intelligence.

Browse courses on Data Ethics

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Research organizations or initiatives focused on data ethics.
Identify a project that aligns with your interests.
Reach out to the organization and inquire about volunteering opportunities.

Contribute to an Open-Source Project on Data Ethics

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Engage in a collaborative project by contributing to an open-source initiative that addresses data ethics and responsible AI.

Browse courses on Data Ethics

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Identify open-source projects focused on data ethics or responsible AI.
Review the project documentation and identify areas where you can contribute.
Reach out to the project maintainers to discuss your ideas.

Career center

Learners who complete Applied Data Science Ethics will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists play a key role in the development of artificial intelligence and machine learning models. By taking this course, future Data Scientists will be better equipped to create models that are fair and unbiased. The course covers topics such as interpretability methods, fairness metrics, and auditing models for bias. This knowledge will be essential for Data Scientists who want to develop models that are used in high-stakes applications, such as healthcare, finance, and criminal justice.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are responsible for the design, development, and deployment of machine learning models. By taking this course, future Machine Learning Engineers will gain the knowledge and skills needed to create models that are fair and unbiased. The course covers topics such as interpretability methods, fairness metrics, and auditing models for bias. This knowledge will be essential for Machine Learning Engineers who want to develop models that are used in high-stakes applications, such as healthcare, finance, and criminal justice.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts are responsible for collecting, cleaning, and analyzing data. By taking this course, future Data Analysts will gain the knowledge and skills needed to identify and mitigate bias in data. The course covers topics such as data quality assessment, data visualization, and statistical modeling. This knowledge will be essential for Data Analysts who want to work with sensitive data or who want to develop models that are used in high-stakes applications.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts are responsible for understanding the business needs of an organization and translating those needs into technical requirements. By taking this course, future Business Analysts will gain the knowledge and skills needed to identify and mitigate bias in business processes. The course covers topics such as stakeholder analysis, requirements gathering, and process mapping. This knowledge will be essential for Business Analysts who want to work in organizations that are committed to diversity and inclusion.

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Project Manager

Project Managers are responsible for planning, executing, and closing projects. By taking this course, future Project Managers will gain the knowledge and skills needed to identify and mitigate bias in project plans and deliverables. The course covers topics such as project scope management, risk management, and stakeholder management. This knowledge will be essential for Project Managers who want to work on projects that are complex or that have a high potential for bias.

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Software Engineer

Software Engineers are responsible for the design, development, and maintenance of software systems. By taking this course, future Software Engineers will gain the knowledge and skills needed to identify and mitigate bias in software code. The course covers topics such as software architecture, design patterns, and testing. This knowledge will be essential for Software Engineers who want to work on software systems that are used by a diverse population.

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Product Manager

Product Managers are responsible for the development and launch of new products. By taking this course, future Product Managers will gain the knowledge and skills needed to identify and mitigate bias in product design and marketing. The course covers topics such as user research, market analysis, and product launch. This knowledge will be essential for Product Managers who want to develop products that are inclusive and accessible to all.

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Marketing Manager

Marketing Managers are responsible for the development and execution of marketing campaigns. By taking this course, future Marketing Managers will gain the knowledge and skills needed to identify and mitigate bias in marketing materials and campaigns. The course covers topics such as market segmentation, target marketing, and campaign evaluation. This knowledge will be essential for Marketing Managers who want to develop marketing campaigns that are inclusive and effective.

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Sales Manager

Sales Managers are responsible for the development and execution of sales strategies. By taking this course, future Sales Managers will gain the knowledge and skills needed to identify and mitigate bias in sales processes and practices. The course covers topics such as sales forecasting, customer relationship management, and sales training. This knowledge will be essential for Sales Managers who want to develop sales strategies that are fair and inclusive.

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Human Resources Manager

Human Resources Managers are responsible for the development and implementation of human resources policies and practices. By taking this course, future Human Resources Managers will gain the knowledge and skills needed to identify and mitigate bias in human resources processes such as hiring, promotion, and training. The course covers topics such as equal opportunity law, employee relations, and performance management. This knowledge will be essential for Human Resources Managers who want to create workplaces that are diverse and inclusive.

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Diversity and Inclusion Officer

Diversity and Inclusion Officers are responsible for developing and implementing diversity and inclusion initiatives within an organization. By taking this course, future Diversity and Inclusion Officers will gain the knowledge and skills needed to identify and mitigate bias in organizational policies and practices. The course covers topics such as diversity and inclusion assessment, unconscious bias training, and employee resource groups. This knowledge will be essential for Diversity and Inclusion Officers who want to create workplaces that are welcoming and inclusive for all.

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Compliance Officer

Compliance Officers are responsible for ensuring that an organization complies with applicable laws and regulations. By taking this course, future Compliance Officers will gain the knowledge and skills needed to identify and mitigate bias in compliance processes and procedures. The course covers topics such as ethics and compliance, risk assessment, and internal audit. This knowledge will be essential for Compliance Officers who want to create compliance programs that are fair and impartial.

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Risk Manager

Risk Managers are responsible for identifying and mitigating risks to an organization. By taking this course, future Risk Managers will gain the knowledge and skills needed to identify and mitigate bias in risk assessment and management processes. The course covers topics such as risk analysis, risk management, and risk communication. This knowledge will be essential for Risk Managers who want to create risk management programs that are fair and equitable.

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Insurance Underwriter

Insurance Underwriters are responsible for assessing the risk of an insurance policy and determining the appropriate premium. By taking this course, future Insurance Underwriters will gain the knowledge and skills needed to identify and mitigate bias in underwriting processes and procedures. The course covers topics such as insurance law, risk assessment, and underwriting guidelines. This knowledge will be essential for Insurance Underwriters who want to create underwriting practices that are fair and impartial.

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Financial Analyst

Financial Analysts are responsible for analyzing financial data and making recommendations to investors. By taking this course, future Financial Analysts will gain the knowledge and skills needed to identify and mitigate bias in financial analysis and investment recommendations. The course covers topics such as financial statement analysis, valuation, and portfolio management. This knowledge will be essential for Financial Analysts who want to create financial analysis and investment recommendations that are fair and unbiased.

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