What is AI Literacy?

AI literacy involves more than just preparing learners for future careers involving AI, such as teaching algorithms and their usage. It is equally crucial to equip them with an understanding of the potential risks associated with AI, such as algorithmic bias and the potential for malicious use.

Different paradigms of AI literacy include:

Know and Understand AI: Acquiring fundamental concepts, skills, and knowledge.
Use and Apply AI: Utilizing data analytics with AI suggestions or using tools like ChatGPT.
Evaluate AI: Critically evaluating AI technologies and the outcomes they generate.
Ethical AI: Addressing fairness, accountability, transparency, safety, etc.

In our ML workshops, we focus extensively on understanding AI and using it. However, when discussing Generative AI (such as LLMs), the aspects of evaluation and ethics become even more critical.

Evaluating AI-Generated Content

Do not blindly rely on the content generated by AI. For some image generation models, the results might be far from reality. Similarly, with some LLMs, the generated text might be inaccurate.

What should you do?
Fact-Check. Fact-Check. Fact-Check. Verify the generated results using trusted sources. While LLMs can effectively synthesize information and present it coherently, always ask for citations and be cautious, as some LLMs might generate fake citations.

Ethical AI

The rapid rise in AI raises profound ethical concerns:

Privacy: Every time you use Generative AI products (like ChatGPT), you are sending a copy of your data to the model servers. This data may be used to improve the model or potentially sold to third-party companies for further development and research. When engaging with AI tools, exercise caution about sharing sensitive personal, confidential, or proprietary information.
Bias: Generative AI can amplify pre-existing biases present in the training data. Additionally, the biases of those who provide input prompts may be reflected in the AI’s outputs.
Lack of Explainability and Transparency: LLMs are built on complex neural networks that perform extensive processing. Neural networks are often considered “black boxes,” making it difficult to understand how deep neural networks make their decisions.

Final thought:

When using LLMs as writing or research assistants, it is very important to consider how they can affect one’s skills and ability to produce original work. It is essential to understand that the more we rely on LLMs to do complete or assist with our writing or researching tasks, it can diminish our skills and increase our reliance on such tools. Therefore, it is best to use LLMs only when necessary or when working with deadlines and tight schedules. To maintain and improve on existing skills such as writing, coding, or researching, one must deliberately practice constantly and attempt to produce original work.

What is AI Literacy?

Evaluating AI-Generated Content

Ethical AI

Final thought:

Read More At