Data Science Ethics: Building Responsible AI Models

Course Description

"Data Science Ethics: Building Responsible AI Models" is an intermediate-level course offered by Statistics.comX that delves deep into the critical aspects of ethical data science and responsible AI development. This comprehensive course addresses the growing concerns surrounding machine learning algorithms and big data AI models, particularly focusing on issues of bias and fairness. As the second installment in the data science ethics program, it equips both practitioners and managers with practical tools and guidance to develop better models, conduct more ethical data analysis, and avoid potentially harmful impacts of their work.

What Students Will Learn

• Evaluation techniques for predictor impact in black box models using interpretability methods
• Explanation of average feature contributions to predictions and individual feature value impacts on specific predictions
• Assessment of model performance using metrics to measure bias and unfairness
• Identification and addressing of potential ethical issues in image and text data
• Conducting ethical audits of data science projects to identify possible harms and areas for bias mitigation or harm reduction

Prerequisites

• Completion of the "Principles of Data Science Ethics" course
• Familiarity with Python programming language
• A Gmail account for the lab in Module 3 (hosted on Google Colaboratory)

Course Content

• Tools for model interpretability
• Global versus local model interpretability methods
• Metrics for model fairness
• Auditing models for bias and fairness
• Remedies for biased models
• Real-world problems and datasets
• Framework for developing ethical data science projects
• Audit process for reviewing projects
• Case studies with ethical considerations
• Python code examples

Who This Course Is For

This course is ideal for data scientists, AI developers, machine learning engineers, and managers involved in data-driven decision-making processes. It's particularly beneficial for professionals who want to ensure their data science projects and AI solutions are designed and implemented responsibly, with a focus on addressing issues of bias and unfairness across protected groups.

Real-World Applications

The skills acquired in this course are invaluable in today's data-driven world. Learners will be able to:

• Develop more ethical and fair AI models and algorithms
• Identify and mitigate potential biases in existing systems
• Improve decision-making processes in various industries (e.g., finance, healthcare, hiring)
• Conduct thorough audits of data science projects to ensure ethical compliance
• Explain complex AI models to stakeholders and end-users
• Address and prevent discriminatory outcomes in automated systems
• Contribute to the development of responsible AI practices in their organizations