After February 2, companies are legally required to ensure AI literacy for employees under the EU AI Act.

The need for AI literacy goes beyond compliance. It is critical for cultivating innovation and competitiveness, aligning with industry best practices. AI will impact every industry, and employees need to be ready to use this as an opportunity to build trust while managing the risks.

Providers and deployers of AI systems shall take measures to ensure, to their best extent, a sufficient level of AI literacy of their staff and other persons dealing with the operation and use of AI systems on their behalf, taking into account their technical knowledge, experience, education and training and the context the AI systems are to be used in, and considering the persons or groups of persons on whom the AI systems are to be used.
Article 4, EU AI Act

To help leaders, we’ve compiled a comprehensive list of resources to help individuals and organizations have more fluency with AI. These tools and training are assembled to enhance technical knowledge, governance acumen and ethical awareness.

Skills for responsible AI: Building a strong foundation  

Developing AI literacy revolves around three critical areas: technical knowledge, governance frameworks and ethical principles. These pillars provide the foundation for organizations to foster transparency, ensure compliance, and drive safe innovation.

1. Technical skills: Understanding the building blocks of AI

AI literacy starts with understanding how AI systems are designed and how they work. While not everyone needs to become a data scientist, foundational levels of understanding will help employees confidently and effectively navigate AI.

Here are some accessible resources to enhance technical skills:

  • Computer scientist explains machine learning in 5 levels of difficulty: Watch a beginner-friendly breakdown of machine learning concepts, progressively tailored for various expertise levels in this 26-minute video.
  • Crash course in machine learning: This 4-hour video series combines theoretical learning with practical examples, making it perfect for beginners.
  • Microsoft Azure AI Fundamentals: A hands-on learning experience using Microsoft Azure to explore foundational AI and machine learning concepts. Ideal for beginners who want to see practical implementations of AI concepts in a widely used cloud platform, spanning 4-8 hours.
  • SAS data literacy essentials: This 3-hour course focuses on the importance of data literacy and teaches the basics of data and its role in AI and machine learning. It is accessible to a wide range of individuals who want to engage with data more meaningfully and build a foundational understanding as a stepping stone to AI skills.
  • Coursera AI for everyone: Introduces AI concepts like machine learning, neural networks, and deep learning and covers AI's capabilities, limitations, ethical considerations and strategies for collaboration and innovation. This 6-hour course is ideal for non-technical professionals.
  • Google’s introduction to large language models: An introductory course providing an overview of large language models, their architecture, and their applications in AI. Learn about various use cases and how to use prompt tuning to enhance performance in this 1-hour micro-learning course.

2. Governance skills: Establishing responsible AI systems

Governance frameworks are crucial for ensuring ethical, transparent and compliant AI systems. Robust governance mitigates risks, addresses biases and builds stakeholder trust – all while aligning with evolving regulations.

Recommended resources for governance skills:

3. Ethics skills: Embedding values into AI practices

Ethics skills focus on addressing biases, ensuring fairness, evaluating risks, and prioritizing transparency. Embedding ethics in organizational culture fosters trust, accountability and inclusivity, enabling responsible AI adoption. Prioritizing ethical frameworks helps tackle challenges like bias and societal impact while aligning innovation with organizational values and goals.

Here are a few key resources for ethical skills:

  • MIT Moral Machine: Test your moral instincts with an interactive game that challenges you to make life-and-death decisions in interactive scenarios, revealing the complexities of ethical AI, with the MIT Moral Machine.
  • SAS data ethics blog posts: Read blog posts about data ethics and trustworthy AI from diverse voices on SAS Blogs.
  • UNESCO Ethics of AI: Challenges and governance: This 7-minute UNESCO video discusses challenges in AI ethics and governance, including addressing inequality and bias.
  • Markkula Center for Applied Ethics: Browse tech ethics articles, case studies, and materials across various fields from the Markkula Center for Applied Ethics resource collection.
  • Artificial Intelligence: Ethics and Societal Challenges by Lund University: This 4-week course from Coursera explores the ethical and societal impacts of AI, focusing on key topics such as bias, surveillance, responsibility and the control problem in AI.
  • AI Incident Database: Explore real-world AI failures and their lessons through a comprehensive database that tracks incidents, fostering awareness and accountability in AI development.

Now is the time to embrace AI literacy. It’s not about fear or hesitation – it’s about unlocking new opportunities for growth, innovation and responsible decision-making. Whether you’re a leader preparing your organization for the future or a team member adapting to new tools, staying curious and committed to learning is the key to thriving in an AI-driven world. Start your journey today!

Learn how SAS drives technology innovation ethically and responsibly

Ravit Dotan also contributed to this blog post.
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About Author

Alison Polito

Associate Data Visualization Analyst

Alison Polito is an associate on the AI Governance Team within SAS's Data Ethics Practice (DEP), where she supports advisory efforts across diverse industries. She is passionate about responsible and trustworthy AI practices, and aligning innovation with regulatory compliance.

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