How can you do good with data? The ethical and legal principles surrounding data and its use—from information to analytics and insight and beyond, into data science and artificial intelligence (AI)—are global in nature, even if the laws are not. And, regardless of whether your company is local or has offices around the world, if you’re using data (and you probably are), it’s important to know how to properly handle it, what to consider, and how to achieve good with it. In short, data professionals today need both the frameworks and the methods in their job to achieve optimal results while being good stewards of their critical role in society today. Corporations, governments, and individuals have powerful tools in Analytics and AI to create real-world outcomes.
Ready to dive in? Join Microsoft Senior Content Developer Ben Olsen, along with Seattle University Robert B. O’Brien Endowed Chair in Business Geneva Anne Lasprogata and Digital Dignity Consulting Co-Founder Nathan Colaner for a fascinating look at “Analytics and AI: Ethics and Law.” Spend few hours per week for six weeks, and learn the fundamental considerations—even if you’re not an AI practitioner. Take hands-on labs and work with real-life data (anonymized and masked, of course), as you learn to apply ethical and legal frameworks to initiatives in the data profession. Plus, get resources for next steps and to help answer questions you may have on this topic in the future.
Start with the foundations of ethics and law, and then explore how data relates to “me” as an individual and “me” as person in society—that is, privacy, identity, anonymity, and security. Then, examine bias, data collection, and IoT. From there, get a look at how these fundamentals apply to businesses, including issues for Human Resources departments, and learn about the European Union’s General Data Protection Regulation (GDPR), handling customer data, and more.
Don’t miss this opportunity to investigate applied data methods for ethical and legal work in analytics and AI and to explore practical approaches to data and analytics problems posed by work in big data, data science, and AI.