Exploring Chatbots: Deception and Trust in AI

- Authors
- Published on
- Published on
In this riveting episode by IBM Technology, the team delves into the intriguing world of chatbots and their potential to deceive. They explore the spectrum of falsehood, ranging from innocent errors to intentional lies, shedding light on the nuances of misinformation and disinformation. Through a captivating example involving a chatbot wrongly portraying a cybersecurity expert, the team unveils the concept of "hallucinations" in generative AI, where inaccuracies surface despite an overall semblance of truth.
Furthermore, the channel presents a compelling dialogue with another chatbot, unearthing contradictions in the bot's claims about its identity. This sparks a thought-provoking discussion on the reliability of AI-generated responses and the necessity of verification in crucial decision-making processes. The video underscores the essential principles for trustworthy AI, advocating for explainability, fairness, robustness, transparency, and privacy as foundational pillars in AI development.
By emphasizing the significance of selecting appropriate models and techniques to enhance chatbot accuracy, IBM Technology paints a roadmap towards minimizing errors and ensuring reliability in AI interactions. The episode concludes with a bold assertion that chatbots indeed possess the capability to lie, as evidenced by prompt injection experiments. Despite this revelation, the team encourages viewers to adopt a "trust, but verify" approach when relying on AI-generated information, highlighting the importance of critical evaluation in the age of artificial intelligence.

Image copyright Youtube

Image copyright Youtube

Image copyright Youtube

Image copyright Youtube
Watch Can AI Chatbots Lie? AI Trustworthiness & How Chatbots Handle Truth on Youtube
Viewer Reactions for Can AI Chatbots Lie? AI Trustworthiness & How Chatbots Handle Truth
AI trustworthiness and the importance of transparency
Using AI for medical questions and the need for verification
Potential bias in AI trained on Reddit and blog posts
The idea of AI lying to maintain its principles
Elon Musk's experience with retraining AI
The Chinese Room issue
Challenges with AI output accuracy and data sources
Doubts about AI intelligence
Concerns about AI lying and sources of information
Criticism of AI technology and its reliability
Related Articles

Decoding Generative and Agentic AI: Exploring the Future
IBM Technology explores generative AI and agentic AI differences. Generative AI reacts to prompts, while agentic AI is proactive. Both rely on large language models for tasks like content creation and organizing events. Future AI will blend generative and agentic approaches for optimal decision-making.

Exploring Advanced AI Models: o3, o4, o4-mini, GPT-4o, and GPT-4.5
Explore the latest AI models o3, o4, o4-mini, GPT-4o, and GPT-4.5 in a dynamic discussion featuring industry experts from IBM Technology. Gain insights into advancements, including improved personality, speed, and visual reasoning capabilities, shaping the future of artificial intelligence.

IBM X-Force Threat Intelligence Report: Cybersecurity Trends Unveiled
IBM Technology uncovers cybersecurity trends in the X-Force Threat Intelligence Index Report. From ransomware decreases to AI threats, learn how to protect against evolving cyber dangers.

Mastering MCP Server Building: Streamlined Process and Compatibility
Learn how to build an MCP server using the Model Context Protocol from Anthropic. Discover the streamlined process, compatibility with LLMs, and observability features for tracking tool usage. Dive into server creation, testing, and integration into AI agents effortlessly.