Ethics in AI and Data Science

Artificial Intelligence (AI) is often touted as a key technology spurring the Fourth Industrial Revolution in which the physical, digital and biological worlds are being fused together in a way that will have a tremendous impact on our global culture and economy. The unprecedented amount of data we create every day fuels this new paradigm of AI. This new world of opportunities also brings with it concerns about security, user privacy, data misuse, surveillance, data ownership, and more. People distrust the use of artificial intelligence and institutions that rely on it without building accountability and transparency. It is the responsibility of business and technology leaders and data scientists to change that: add transparency, develop standards and share best practices to drive AI adoption with trust.

Business leaders and data professionals today need AI frameworks and methods to achieve optimal results while also being good technology and business stewards. Though companies and institutions are adopting AI principles and the language of ethics, trust and responsibility has entered emerging technologies, AI and data science, there is still confusion about when and why it’s needed. This course introduces some of the principles and frameworks that puts ethics and responsibility into practice in the data analytics profession, and offers practical approaches to technical, business and leadership dilemmas and challenges posed by work in AI and Data Science.

• Welcome!
• Chapter 1. The State of Ethics, Trust & Responsibility with AI and Data Science
• Chapter 2. What Do We Mean by Artificial Intelligence (AI) and Data Science and Why It Matters
• Chapter 3. Strategies (& Challenges) of Putting Ethics & Responsibility into Practice
• Final Exam (Verified track only)