Chatbots Are Scheming 5x More

2026-03-27 · Source: There's An AI For That · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Novice, extended

Summary

The provided content outlines a comprehensive, free AI literacy course designed for absolute beginners, developed by Henry Lee and the learnaianywhere.org team. This course, structured into four lessons, covers fundamental AI concepts such as machine learning, data bias, privacy, and responsible AI. Participants learn by designing their own AI classifier using the NearPocket app, which allows them to add images, train a model, and test its recognition capabilities. The curriculum emphasizes that AI is a product of human choices, highlighting the importance of diverse data to mitigate algorithmic bias and the necessity of obtaining permission for private data use. The course aims to equip learners with the knowledge and judgment to become responsible creators and critical users of AI tools, with lessons on algorithms, feature identification, and the environmental costs of AI.

Key takeaway

For CTOs and VPs of Engineering/Data considering AI adoption or internal training, this course framework offers a robust model for foundational AI literacy. Your teams should prioritize understanding AI's human-centric nature, focusing on data diversity and privacy from the outset. Implementing similar hands-on, project-based learning can foster a culture of responsible AI development and critical evaluation, mitigating risks associated with algorithmic bias and data misuse.

Key insights

AI literacy is crucial for responsible creation and critical use of AI tools, emphasizing human choice and data integrity.

Principles

• AI is a product of human choices.
• Diverse data reduces algorithmic bias.
• Privacy requires explicit permission.

Method

The course uses the NearPocket app for hands-on AI classifier design, guiding users through data collection, model training, and testing, while integrating lessons on algorithms and responsible AI practices.

In practice

• Design an AI classifier using NearPocket.
• Collect diverse data to minimize bias.
• Always seek permission for private data.

Topics

• AI Chatbot Behavior
• Large Language Models
• AI Agents
• Machine Learning Fundamentals
• Responsible AI

Code references

• czlonkowski/n8n-mcp
• Liquid4All/cookbook
• kepano/obsidian-skills

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Student, General Interest, Entrepreneur

Related on AIssential

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• Human Capital — Data workers, primarily in the Global South, are exploited and invisibilized despite powering AI development.
• AI chatbots can prioritize flattery over facts – and that carries serious risks — AI sycophancy, driven by training data and human bias, poses significant epistemic, psychological, and political risks.
• Any Headphones = Live Translation — AI adoption faces regulatory and ethical considerations while simultaneously advancing in practical applications and autonomous agents.
• Guiding a Safe Future for AI – Part 2 — AI's rapid advancement necessitates a focus on safety, ethical deployment, and robust infrastructure to serve humanity.

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Editorial summary, takeaway, and curation by AIssential. Original article published by There's An AI For That.