📮 Maildrop 24.12.24: The AI safety imperative: Building safety first LLMs
The things to know about AI and cyber threats | Tools for the next generation of enterprises in the AI era
📮 Maildrop 24.12.24: The AI safety imperative: Building safety first LLMs
Imagine a world where cyberattacks are not just prevented but predicted and neutralized before they occur.
This is the promise of defining how threats evolve using large language models (LLMs) to transform the cybersecurity landscape and improve online safety.
➤ More on this is discussed in the introduction of this new series.
Imagine an AI-powered safety system designed to protect individuals or critical infrastructure. Trained on historical data, this system inadvertently learns and reinforces existing societal biases.
This can lead to:
False positives: Unjustly flagging individuals or situations as threats, leading to unwarranted interventions and potential harm.
False negatives: Failing to identify genuine threats, leaving individuals or systems vulnerable to harm.
Systemic discrimination: Perpetuating and amplifying existing inequalities, leading to systemic safety issues for certain groups.
This scenario underscores the critical need for rigorous AI safety considerations, including data diversity, transparency, and continuous monitoring, to ensure that AI-powered safety systems are fair, reliable, and do not inadvertently cause harm.
What’s important to know?
↓↓ More below ↓↓
