// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
ACM Ethics
Ethical principles and codes of conduct from the Association for Computing Machinery that guide computer professionals, including those working with AI, to act responsibly.
TECHNICAL DEFINITION
ACM Ethics encompasses the ethical guidelines and the Code of Ethics and Professional Conduct established by the Association for Computing Machinery, providing principles for computing professionals, including AI practitioners, to ensure responsible and beneficial use of technology, addressing societal impact, privacy, and fairness.
BACKGROUND
Generative artificial intelligence (GenAI) is a subfield of artificial intelligence (AI) that uses generative models to generate text, images, videos, audio, software code or other forms of data. These models learn the underlying patterns and structures of their training data, and use them to generate new data in response to input, which often takes the form of natural language prompts.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- ACM Code of Ethics
- ACM AI Ethics
- Association for Computing Machinery Ethics
USAGE NOTE
The ACM Ethics code serves as a foundational guide for ethical decision-making in AI research and development.
DEVELOPERS
Organizations developing technology related to ACM Ethics.
The ACM is the professional organization that created and maintains the ACM Code of Ethics and Professional Conduct. Its various working groups, conferences (like FAccT), and publications directly shape the standards and principles for ethical technology development in AI engineering.
Google develops and promotes tools and frameworks to help engineers implement its AI Principles. This includes the Model Card Toolkit for transparent model reporting, the What-If Tool for model inspection, and Fairness Indicators for evaluating fairness metrics.
Microsoft has established a comprehensive Responsible AI framework and develops open-source tools to support it. Key technologies include Fairlearn for assessing and mitigating fairness issues, and InterpretML for improving model transparency and explainability.
IBM focuses on building ethical and trustworthy AI systems. It has released several open-source toolkits, including AI Fairness 360 (AIF360) to detect and mitigate bias, and AI Explainability 360 (AIX360) to provide insights into machine learning model predictions.
An AI safety and research company focused on building reliable and steerable AI systems. They developed 'Constitutional AI,' a technological approach to train AI models (like their assistant, Claude) to follow a set of ethical principles derived from sources like the UN Declaration of Human Rights, reducing the need for direct human feedback on harmful outputs.
A company that provides an AI governance platform. Their technology is designed to help organizations operationalize responsible AI by assessing, managing, and monitoring their AI systems for risks related to fairness, performance, transparency, and compliance with regulations.
A non-profit coalition of academic, civil society, industry, and media organizations. PAI develops best practices, frameworks, and resources to guide the responsible development of AI. Their work includes projects on fairness, transparency, and the societal impact of AI technologies.
As a leading AI research and deployment company, OpenAI invests heavily in safety and alignment research. They develop techniques like Reinforcement Learning from Human Feedback (RLHF) to align model behavior with human values and have extensive safety policies and moderation APIs to prevent misuse of their models.