PJ Hagerty
AI & Ethics
#1about 6 minutes
Defining key AI concepts from algorithms to LLMs
Key terms like algorithm, machine learning, deep learning, foundation models, and large language models (LLMs) are defined to establish a common understanding.
#2about 3 minutes
Understanding and addressing inherent bias in AI models
AI models inherit biases from their training data, which can lead to unethical outcomes like offensive chatbots if not carefully managed.
#3about 5 minutes
The danger of AI hype and misapplication in business
Many businesses claim to use AI when they are only using simple algorithms, leading to misapplication and wasted resources on overhyped solutions.
#4about 2 minutes
The ethical risks of outdated and insecure AI models
Large language models quickly become outdated and can be exploited without proper security guardrails, posing significant ethical risks like malicious image generation.
#5about 3 minutes
AI's current state is more toddler than terminator
Current AI is comparable to a toddler that repeats what it hears without true reasoning, meaning it is not yet capable of replacing complex developer roles.
#6about 3 minutes
Learning from past failures in AI development
Historical examples like Microsoft's Tay chatbot and biased facial recognition systems demonstrate the critical need for guardrails and diverse testing data.
#7about 2 minutes
Accountability, auditability, and end-user rights in AI
Developers have a responsibility to build accountable and publicly auditable AI systems while ensuring end-users are informed and have the right to opt out.
#8about 4 minutes
Practical governance and technical solutions for ethical AI
Adhering to regulations like GDPR and using technical solutions for prompt filtering, data anonymization, and hallucination mitigation are key to building ethical AI.
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