We All Count: Ethics and Equity in Data Science (306305)*Heather Krause, Datassist, Inc.
Keywords: ethics, equity, bias, influence, assessment, fairness, accountability
Bias, sexism, and racism in data science permeate our systems. They’re hidden in every step of the process; funding, data collection, analysis, and visualization are rife with inequality-causing assumptions, misunderstandings, and blind spots. Ethics and equity in data are a critical issue in the present and will continue to grow in importance in the sector. Understanding how to speak to these issues is fundamental for data professionals. Fairness in machine learning and algorithmic accountability are gaining prominence in the literature - but largely from a theoretical point of view. This talk will present practical solutions to conceptual issues of ethics and equity in data science. We will discuss the foundational issues at each stage of a data science lifecycle and supply you with tools to have an influence over the ethics of data science products and processes in your organization and the world. Real world examples of equity assessments, ethical checklists, and tools to correct problems will be provided at each step in the data product lifecycle. This talk with leave you with actionable insights about how to apply the lens of equity and ethics to help in your everyday job.