// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Responsible AI
An umbrella term for developing and using AI systems in a way that is fair, transparent, accountable, and beneficial to society.
TECHNICAL DEFINITION
Responsible AI encompasses the comprehensive framework and practices for designing, developing, and deploying artificial intelligence systems that adhere to ethical principles, legal requirements, and societal values, emphasizing fairness, transparency, accountability, and safety.
BACKGROUND
Prompt injection is a cybersecurity exploit and an attack vector in which innocuous-looking inputs are designed to cause unintended behavior in machine learning models, particularly large language models (LLMs). The attack takes advantage of the model's inability to distinguish between developer-defined prompts and user inputs to bypass safeguards and influence model behaviour. While LLMs are designed to follow trusted instructions, they can be manipulated into carrying out unintended responses through carefully crafted inputs.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Ethical AI
- trustworthy AI
- human-centered AI
- beneficial AI
USAGE NOTE
Implementing Responsible AI frameworks helps organizations mitigate risks and build public trust.
DEVELOPERS
Organizations developing technology related to Responsible AI.
Develops tools, frameworks, and research for building and deploying AI systems responsibly, focusing on fairness, interpretability, privacy, and safety, impacting AI engineering and prompt design practices.
Offers a comprehensive Responsible AI toolkit, operational guidelines, and integrates fairness, transparency, and accountability features into its Azure AI platform, crucial for ethical AI engineering and prompt development.
Provides open-source toolkits like AI Fairness 360 and AI Explainability 360, and platforms for managing the lifecycle of trustworthy AI, directly supporting responsible AI engineering and model governance.
Has an Office of Ethical & Humane Use of AI and develops internal tools and frameworks to ensure their AI products and features, including those impacting prompt design, adhere to ethical principles.
Conducts research and develops tools focusing on fairness, interpretability, and privacy within AI systems, contributing to the responsible development and deployment of AI models and data handling.
Fosters an ecosystem for responsible AI development through its platform, providing tools for model evaluation, bias detection, and transparency, which are critical for ethical AI engineering and prompt design.
Actively researches and implements safety and alignment strategies for large language models, focusing on responsible deployment, mitigating misuse, and addressing biases inherent in prompt-driven AI systems.