Learn how to build and incorporate ethical principles and frameworks in your AI and Data Science technology and business initiatives to add transparency, build trust, drive adoption, and lead with trust and responsibility.
An introductory course by LinuxFoundationX
This course delves into the critical intersection of technology, ethics, and business in the era of the Fourth Industrial Revolution. It equips business leaders and data professionals with essential frameworks and methods to navigate the complex landscape of AI and data science responsibly.
This is an introductory level course with no specific prerequisites. It's accessible to anyone interested in understanding the ethical implications of AI and data science in business and society.
Learners will be equipped to:
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Learn the fundamentals of Artificial Intelligence with a combination of AI fundamentals and an overview of the rich Linux Foundation AI & Data projects ecosystem. It is well-suited for any kind of AI adopter, regardless of the professional background and level of technical knowledge.
In this course, you will explore the very essence of human progress with influential researchers, business leaders, experts, and practitioners from diverse fields. Learn how to integrate Artificial Intelligence into your company’s culture, enhancing organizational capabilities, and cultivating strong relationships with your customers. Gain deep insights into AI’s impact on business, society, and your own future, and get tools and actionable ideas to stay ahead in a rapidly changing world.
AI’s popularity has resulted in numerous well-publicized cases of bias, injustice, and discrimination. Often these harms occur in machine learning projects that have the best of goals, developed by data scientists with good intentions. This course, the second in the data science ethics program for both practitioners and managers, provides guidance and practical tools to build better models and avoid these problems.
"Basics of Data Science" gives a comprehensible overview of many fundamental concepts and tools of data science, including data quality and data preprocessing, supervised and unsupervised learning techniques including their evaluation, frequent itemsets and association rules, sequence mining, process mining, text mining, and responsible data science.
This course explores how teachers in higher education can reconsider their approach to students’ learning in the age of AI. This course provides a learning-centred approach to the inclusion of AI when designing students’ learning opportunities, especially to scaffold learning, facilitate ideation, and provide real-time assistance. Additionally, the values and principles for responsible AI integration into learning practices are also discussed.
This course provides an exploration of the evolving role of higher education in the context of AI advancements. Participants will examine the concept of AI literacies and their significance in navigating the AI landscape in the context of what future graduates will need to thrive. Emphasis is placed on values-based approaches and considerations for incorporating user-facing AI in higher education, along with discussions on human-machine relationships and preparing for an AI-infused future.
