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AI trustworthiness 101 for product builders
Rating: 4.0 out of 5(1 rating)
75 students

AI trustworthiness 101 for product builders

Learn the essentials of AI trustworthiness
Created byMary Beth Snodgrass
Last updated 2/2026
English
English [Auto],

What you'll learn

  • Know why AI trustworthiness matters for anyone building, buying or adopting AI
  • Understand how new products and innovation gain widespread acceptance
  • Consider key AI risks related to B2B and B2C product design, development, deployment and GTM
  • Identify key roles and approaches for embedding trust in your product strategy

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Course content

1 section • 7 lectures • 31m total length

  • Introduction2:47

    Explore why trust matters in product, define trust and how it grows, and integrate trustworthy and responsible AI into product strategy to close adoption barriers.

  • Why Trust Matters5:04

    Prioritize trust as the one challenge for product leaders; operationalize trustworthy ai through governance, responsible ai initiatives, and up-to-date procurement to boost adoption in regulated industries like healthcare and finance.

  • Definitions and Mechanisms of Trust3:09

    Explore how institutional protections and social acceptance shape AI trust, from virality and regulation to sustainable use, and define technical, organizational, and social trust for product builders.

  • Contextualizing Trust4:44

    Contextualizing trust for product builders, this lecture explains how trust is dynamic, modular, and context-dependent, and how to assess AI trustworthiness through risk, governance, and five main trustworthiness characteristics.

  • Product and Trustworthy AI10:33

    Product and trustworthy AI build user trust to accelerate adoption and governance, while AI evals guide end-user needs, privacy, and transparency in responsible AI.

  • Healthcare & AI Questions Answered3:04

    Context matters in healthcare AI, linking outcomes like lower hemoglobin A1c and reduced cardiovascular risk to ambient AI adoption, while data, engagement, and regulations shape clinician and product team roles.

  • Testimonials: why this content matters for some participants2:31

    Explore how trust and AI intersect in healthcare, highlighting data disparities, trust gaps, adoption barriers, and the need for transparency to empower safe, predictable product design.

Requirements

  • No pre-requisites

Description

The intro course provides high-level, foundational information on trustworthy AI for tech professionals, AI builders, vibe coders and entrepreneurs.

Take this brief course to gain clarity on what trustworthy AI is and why it's important to bridge the gap between high-level concepts of trust and the realities of operationalizing and productizing trust.

There are four main discussions:

  • Trust in AI:

    • Why trust matters

    • Trust on a macro and micro level

  • Mechanics and Definitions of Trust:

    • Societal acceptance of innovation

    • Definitions of trust

    • "Shadow strategy" behind AI's social acceptance

  • Contextualizing AI Trustworthiness:

    • Trust can be assessed modularly

    • Role of risk in decoding trustworthiness characteristics

    • 5 key characteristics of trustworthiness and 40+ sub-characteristics

  • The Role of Product in Trustworthy AI:   

    • The roles product managers play in productizing trustworthy AI.

    • An overview of how trustworthiness can be integrated into product strategy.

This short, foundational primer is the short, high-level result after thousands of hours researching and working at the intersection of of responsible AI, social impact, enterprise governance, and early stage tech products. 

Complete this free course to receive an invitation to take a more in-depth, interactive course on Applied Trustworthy AI, which will also offer feedback on your own product(s) AI trustworthiness.

Who this course is for:

  • (1) Product builders - PMs, engineers and product designers - interested in contributing to or leading safe, responsible.
  • (2) Early stage (pre-seed to Series A) and bootstrapped tech entrepreneurs building and bringing solutions to market.
  • (3) Side-hustlers, vibe-coders and aspiring founders building 'mini-apps,' AI tools, workflows and full products, either for fun or work.

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