Responsible AI
Responsible AI is the development, deployment, and operation of AI systems with attention to principles such as safety, fairness, accountability, transparency, privacy, and societal impact.
Generative AI in Action
Responsible AI is the practice of developing and operating artificial intelligence in ways that address safety, fairness, accountability, transparency, and related societal risks.
- How it is used
- Guides enterprise GenAI governance, deployment, and output handling around potential harms.
- How deep it goes
- Details four lifecycle stages and operational safeguards, while acknowledging that harm assessment and evaluation tools remain imperfect.
- Examples
- Uses an enterprise chatbot, response filtering, and named fairness toolkits as concrete application points.
- What you can do after
- Practice identifying, measuring, mitigating, and operating around harms in an enterprise GenAI deployment.
Introduction to Generative AI, Second Edition
Responsible AI adoption is the uptake and use of artificial-intelligence systems with attention to safety, ethics, accountability, and societal impacts.
- How it is used
- Guides the development, deployment, governance, and everyday use of AI systems amid safety, ethical, legal, and societal risks.
- How deep it goes
- Explains responsible AI through governance, literacy, oversight, and layered safeguards, but stops short of implementation-level technical detail.
- Examples
- Uses data inventories, model cards, privacy assessments, red teaming, external audits, acceptable-use policies, citations, and revision histories.
- What you can do after
- Apply layered safeguards, question AI outputs, recognize limitations, and involve human judgment before consequential use.
Read how Introduction to Generative AI, Second Edition explains responsible AI →
First-Party Data Activation
Practices for developing and operating artificial intelligence in ways that address fairness, accountability, transparency, safety, and societal impact.
- How it is used
- Guides marketing organizations in deploying AI consistently with privacy principles, regulatory obligations, and societal values.
- How deep it goes
- Explains core risks and mitigation categories but stops short of detailed implementation procedures or model-level unlearning methods.
- Examples
- Uses biased training data, discriminatory outcomes, opaque model outputs, and privacy-preserving identity technologies as concrete examples.
- What you can do after
- Evaluate AI marketing initiatives through bias testing, explainability, governance, and privacy impact assessments.
Read how First-Party Data Activation explains responsible AI →
Building Reliable AI Systems
Practices that address the safety, fairness, accountability, transparency, and societal impact of AI systems.
- How it is used
- Treats fairness, privacy, and trustworthiness as dimensions of production AI reliability.
- How deep it goes
- Names bias mitigation, privacy protection, decision transparency, and governance without detailing their procedures.
- What you can do after
- Apply bias-detection, privacy-protection, transparency, and governance practices to support trustworthy AI systems.
Read how Building Reliable AI Systems explains responsible AI →
Related concepts
- content filteringused by · Content filtering is the inspection of content against defined criteria to determine whether it should be blocked, modified, flagged, or removed.
- privacyhas part · Privacy is the protection and controlled handling of information or activities to prevent inappropriate access, collection, use, or disclosure.
- bias detectionhas part · Bias detection is the systematic assessment of data, decisions, or system behavior to identify distortions or unjustified disparities across groups or conditions.
- compliancecontrasts with · Compliance is conformity with applicable legal, regulatory, contractual, policy, or other governing requirements.
- Azureused by · Azure is Microsoft's public cloud computing platform, providing on-demand infrastructure, application hosting, and managed services.
- Azure OpenAIconfigured by · Azure OpenAI Service is Microsoft’s managed Azure cloud service for accessing OpenAI models through Azure-hosted deployments, APIs, infrastructure, and controls.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.