AI Literacy Workout: Your Path to Mastering AI
Are you ready to pursue your passions while navigating the world of AI? Just like managing your finances, building your AI literacy can empower you to take on life's opportunities while managing its challenges. We call it your AI Literacy Workout.
What is AI Literacy Workout?
AI Literacy Workout is the ability to confidently understand and engage with AI systems, enabling you to deploy them effectively and responsibly while staying aware of their risks and potential harm. Whether you're looking to use AI in your workplace, explore its creative potential, or ensure you're protected against its unintended consequences, developing these skills early will set you up for success.
Dive into our three-part training plan and see where it can take you.
Bamboozled by AI? Check out our glossary at the end to demystify the terms. And don’t just take our word for it—hear what industry leaders have to say about the importance of training your AI literacy muscles.
Step One: Building Blocks – Foundations of AI Literacy
Every great journey starts with the basics. Building your AI literacy foundation is no different.
The good news is, you've already warmed up—you interact with AI daily, knowingly or not. Our goal is to give you the confidence to understand how AI works and impacts your life.
From understanding what AI is and how it functions, to recognizing its potential risks and benefits, this week is all about mastering the basics. Let’s get started with the building blocks of AI.
Step Two: Critical Thinking with AI – Opportunities and Risks
With your foundation set, it’s time to level up. This step, we focus on using critical thinking to evaluate the opportunities and risks associated with AI.
How can AI create value for you or your organization? What ethical considerations and societal implications should you keep in mind? And how can you safeguard yourself and others from potential harm?
The earlier you build these skills, the more confidently you’ll be able to harness AI's potential while mitigating its risks.
Step Three: Staying Covered – Responsible AI Deployment
We get it. By step three, you've tackled the essentials of AI literacy and explored its risks and opportunities. Now comes the crucial step: responsible deployment.
Just as insurance gives you peace of mind in uncertain times, being prepared for AI's impacts—both intended and unintended—can make all the difference. Whether you're designing, deploying, or interacting with AI systems, understanding compliance requirements, human oversight, and accountability will ensure you're covered.
This week, we’ll show you how to ensure your AI usage aligns with ethical guidelines and legal standards, no more, no less.
AI Literacy Phrasebook
Stumped by terms like "algorithm bias" or "human-centric AI"? Head to our glossary for quick and simple explanations.
Stay informed, stay prepared, and unlock the power of AI with confidence. Your AI Literacy Workout starts today.
Algorithm
An algorithm is like a recipe—a step-by-step set of rules or instructions that an AI system follows to perform a task, such as identifying patterns in data or making predictions.
Bias in AI
AI bias occurs when an AI system produces results that are unfair or discriminatory. This often stems from biased training data or flawed system design, making it crucial to recognize and address bias for fair AI deployment.
Human-Centric AI
Human-centric AI ensures that AI systems are designed and deployed with a focus on benefiting people. This includes respecting human rights, enhancing well-being, and maintaining accountability for any potential harm.
AI Literacy A-Z Glossary
A - Algorithm: A step-by-step procedure used by AI systems to solve problems or make decisions based on data.
B - Bias: Systematic errors in AI systems that lead to unfair or discriminatory outcomes, often stemming from biased data or design.
C - Compliance: Adherence to legal, ethical, and regulatory requirements when deploying AI systems.
D - Dataset: A collection of data used to train, validate, or test AI systems.
E - Ethics: Principles guiding the responsible and fair use of AI to avoid harm and respect human rights.
F - Fairness: Ensuring AI systems produce equitable outcomes and do not discriminate against individuals or groups.
G - Governance: Frameworks and processes for overseeing the responsible design, development, and deployment of AI systems.
H - Human Oversight: The involvement of humans in monitoring and intervening in AI system decisions to ensure accountability.
I - Interpretability: The degree to which humans can understand how an AI system makes decisions or predictions.
J - Jurisdiction: The legal authority or region within which AI systems must comply with specific laws and regulations.
K - Knowledge Base: A structured repository of information that AI systems use to make informed decisions.
L - Liability: Legal responsibility for the harm or risks caused by AI systems.
M - Model: The mathematical framework or structure used by AI systems to process data and generate predictions.
N - Neural Network: A type of AI model inspired by the human brain, used for complex tasks like image or speech recognition.
O - Optimization: The process of improving an AI system’s performance to achieve specific objectives efficiently.
P - Privacy: Protecting individuals’ personal data when using AI systems, in compliance with regulations like GDPR.
Q - Quality Assurance: Processes to ensure AI systems perform as intended and meet safety and compliance standards.
R - Regulation: Legal frameworks, such as the EU AI Act, that govern the safe and ethical use of AI systems.
S - Safety: Measures taken to ensure AI systems do not cause harm to people or property.
T - Transparency: Openness about how AI systems work, including their design, data, and decision-making processes.
U - Usability: The ease with which people can use and interact with AI systems effectively.
V - Validation: Testing AI systems to ensure they perform correctly and reliably under various conditions.
W - Workflow: The sequence of steps or processes involved in deploying and managing AI systems.
X - Explainability: The ability to articulate the reasoning behind an AI system’s decisions in a way humans can understand.
Y - Yield: The outcomes or results produced by an AI system, which should align with intended goals.
Z - Zero Harm: The principle of designing AI systems to avoid causing harm to individuals or society.