Analyzing Data with an Equity Lens

In this course, experts Mareisha Reese and Mary-Frances Winters explore the importance of equity in data practices to ensure fair, unbiased, and inclusive outcomes. It highlights how inequities in data collection, analysis, and reporting can perpetuate harm and skew results, particularly in areas like healthcare, HR, and AI applications. Learners will uncover sources of bias, such as sampling and algorithmic bias, and discover practical strategies to address these challenges. Through discussions on equity-focused methodologies and the role of AI and data ethicists, participants will gain tools and insights to incorporate equity into their data projects. By the end, learners will be equipped to create more just and inclusive data practices in their organizations.

Learning objectives
Define equity in data practices: Articulate the principles of equity in data collection, analysis, and reporting and explain their importance in reducing bias and improving fairness.
Identify and address bias: Recognize and mitigate different types of data bias, including sampling, selection, algorithmic, and attribution biases, to ensure accurate and ethical analysis.
Implement equity-based data collection and reporting: Develop equitable data collection methods, aggregation practices, and reporting strategies to ensure fair representation and meaningful insights.
Evaluate AI's role in data equity: Analyze the benefits, risks, and tools for mitigating bias in AI-driven data analysis, and understand the critical role of data ethicists.
Apply equity-centered decision-making: Utilize equitable practices in analyzing data from HR, healthcare, and other fields, ensuring outcomes are just and actionable for diverse populations.