Advance Trustworthy AI and ML, and Identify Best Practices for Scaling AI

2021-10-28 · Source: AI Trends · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Public Policy & Governance · Depth: Fundamental Awareness, quick

Summary

The US Department of Energy (DOE) prioritizes advancing trustworthy AI and machine learning to mitigate agency risk, while the US General Services Administration (GGSA) focuses on identifying best practices for implementing AI at scale. These priorities were highlighted at a recent AI event, indicating a governmental push towards responsible and scalable AI adoption. The DOE's emphasis on trustworthiness suggests a need for robust validation and ethical frameworks in AI development and deployment within critical national infrastructure. Concurrently, the GSA's focus on best practices for large-scale implementation points to challenges in operationalizing AI across diverse governmental functions, requiring standardized approaches and efficient resource allocation.

Key takeaway

For CTOs and VPs of Engineering evaluating AI strategies for large organizations, recognize that governmental priorities reflect broader industry needs for trustworthy and scalable AI. Your teams should integrate robust validation and ethical considerations into AI development from the outset, while also focusing on establishing clear operational best practices to ensure successful, large-scale deployment.

Key insights

US government agencies prioritize trustworthy AI for risk mitigation and best practices for scalable AI implementation.

Principles

• Trustworthy AI mitigates agency risk.
• Best practices enable AI at scale.

Topics

• Trustworthy AI
• AI at Scale
• AI Best Practices
• Government AI Policy
• Risk Mitigation

Best for: CTO, VP of Engineering/Data, Executive, Policy Maker, Director of AI/ML, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Trends.