Tag: Responsible AI
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Responsible AI, Model Risk and Governance That Survives an Audit (Data Science Series, Part 29)
Model governance is not a policy document. It is an inventory, an independent challenge function, and a monitoring trail that lets you answer who approved this model and on what evidence. Here is how I build one that holds up when an auditor asks.
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EU AI Act Readiness and Responsible AI on watsonx (IBM Gen AI Series, Part 21)
The 2026 Digital Omnibus pushed high-risk deadlines to December 2027, but GPAI and transparency duties still bite this year. Here is how I classify a watsonx system and build the evidence pack before an auditor asks.
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Granite Guardian and Hallucination Detection on watsonx (IBM Gen AI Series, Part 10)
Granite Guardian is a judge model that scores watsonx answers for hallucination. Here is where it sits in a RAG pipeline, how groundedness detection works, and where to put your block threshold.
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Vertex AI Responsible AI, Governance, and Audit Logging (Google Cloud Gen AI Series, Part 27)
Data Access audit logs, SynthID provenance, Model Registry, and Access Transparency, wired into a governance baseline for a Gemini workload on Vertex AI. What is on by default, what you have to switch on, and what it costs.
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Azure Responsible AI, the RAI Dashboard and Scorecard (Azure Gen AI Series, Part 27)
Responsible AI on Azure is two toolchains: the Responsible AI dashboard for tabular models and Foundry safety evaluators for generative apps. Here is which one your workload needs, how to run each, and how to keep a scorecard for the EU AI Act.
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Azure AI Content Safety, Prompt Shields to Groundedness (Azure Gen AI Series, Part 14)
Azure AI Content Safety scans prompts and responses for harm, jailbreaks, hallucinations, and copied material. Which API catches what, how the 0 to 7 severity scale drives a block decision, and where each check belongs in a request.
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Responsible AI and Watermark Detection on Amazon Bedrock (AWS Gen AI Series, Part 27)
A practical walk through responsible AI on AWS: the eight dimensions AWS documents, invisible watermarking on Titan and Nova, the DetectGeneratedContent API, and bias checks with SageMaker Clarify.
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Amazon Bedrock Guardrails, Content Filters, and Grounding Checks (AWS Gen AI Series, Part 14)
Amazon Bedrock Guardrails inspects text into and out of a model across six policies. Where each fits, how to call it inline and standalone, what it costs, and where it trips you in production.
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Guardrails and Responsible AI: What They Catch, and What They Miss (GenAI Series, Part 21)
Guardrails screen what goes into and out of an AI model. What they catch, harmful content, jailbreaks, prompt injection, data leaks, and why safety must be layered, not a single filter.
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Guardrails and Responsible AI on VMware Private AI: What NeMo Guardrails Actually Stops (Private AI Series, Part 28)
Private does not mean safe. Here is how NeMo Guardrails wraps your models on VMware Private AI, the five rail types, and an honest line on what guardrails catch and what they do not.
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About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
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