Tag

responsible AI

Terms of (Algorithmic) Endearment: Engineering Trust into Your AI Stack

Aaron Wise

Engineering trust into your AI stack demands ethics embedded in product design through bias audits, explainability tools, and cross-functional oversight.

The Trust Stack: Building AI You Can Rely On from Data to Deployment

Aaron Wise

Trust in AI requires a layered stack from data transparency to human oversight, ensuring reliability, fairness, and accountability at every stage.

Scaling Wisdom: Designing Human-in-the-Loop Architectures for Intelligent Agents

Aaron Wise

Human-in-the-loop agentic AI combines autonomous decision-making with strategic human oversight to balance speed, control, and accountability.

Automate the Boring, Govern the Risky: The Dual Mandate of Modern Data Teams

Aaron Wise

Data teams must automate routine workflows while actively governing high-risk AI and data practices to ensure both speed and trust.

Data Engineering vs. ML Engineering: Bridging the Data-to-Insight Divide

Aaron Wise

Data engineers build reliable data pipelines while ML engineers deploy models, but both must collaborate closely to bridge the data-to-insight divide.