Human-Centered Artificial Intelligence: Trusted, Reliable and Safe | Stanford HAI
Stanford
University
  • Stanford Home
  • Maps & Directions
  • Search Stanford
  • Emergency Info
  • Terms of Use
  • Privacy
  • Copyright
  • Trademarks
  • Non-Discrimination
  • Accessibility
© Stanford University.  Stanford, California 94305.
Skip to content
  • About

    • About
    • People
    • Get Involved with HAI
    • Support HAI
    • Subscribe to Email
  • Research

    • Research
    • Fellowship Programs
    • Grants
    • Student Affinity Groups
    • Centers & Labs
    • Research Publications
    • Research Partners
  • Education

    • Education
    • Executive and Professional Education
    • Government and Policymakers
    • K-12
    • Stanford Students
  • Policy

    • Policy
    • Policy Publications
    • Policymaker Education
    • Student Opportunities
  • AI Index

    • AI Index
    • AI Index Report
    • Global Vibrancy Tool
    • People
  • News
  • Events
  • Industry
  • Centers & Labs
Navigate
  • About
  • Events
  • AI Glossary
  • Careers
  • Search
Participate
  • Get Involved
  • Support HAI
  • Contact Us

Stay Up To Date

Get the latest news, advances in research, policy work, and education program updates from HAI in your inbox weekly.

Sign Up For Latest News

Your browser does not support the video tag.
event

Human-Centered Artificial Intelligence: Trusted, Reliable and Safe

Status
Past
Date
Wednesday, October 30, 2019 12:00 PM - 4:00 PM PST/PDT
Topics
Ethics, Equity, Inclusion

The next generation of user experiences will produce 1000-fold improvements in human capabilities.  This new tools will amplify, augment, enhance, and empower people, just as the Web, email, search, navigation, digital photography, and many other applications have already done. Rather than emphasize autonomous machines and humanoid robots as team partners, these new tools will produce comprehensible, predictable, and controllable applications that promote self-efficacy, human responsibility, and social participation at scale.  The goal is to ensure human control, while increasing the level of automation. 

Share
Link copied to clipboard!
Event Contact
celia.clark@stanford.edu
650-725-4537

Related Events

2026 Conference on Physics and AI (PAI26)
ConferenceJun 10, 2026
June
10
2026

The Center for Decoding the Universe brings together researchers across scientific disciplines to answer the biggest questions about our Universe by leveraging complex data with the most advanced computational methods. 

Event

2026 Conference on Physics and AI (PAI26)

Jun 10, 2026

The Center for Decoding the Universe brings together researchers across scientific disciplines to answer the biggest questions about our Universe by leveraging complex data with the most advanced computational methods. 

NVIDIA & Marlowe: Scaling Data Science Workloads with RAPIDS
WorkshopJul 15, 20262:00 PM - 3:30 PM
July
15
2026

This workshop will cover how NVIDIA RAPIDS offers a seamless experience to enable GPU-acceleration for many existing data science tasks with zero code changes. You will learn how to use GPU-accelerated tools to conduct data science faster, leading to more scalable, reliable, and cost-effective results!

Event

NVIDIA & Marlowe: Scaling Data Science Workloads with RAPIDS

Jul 15, 20262:00 PM - 3:30 PM

This workshop will cover how NVIDIA RAPIDS offers a seamless experience to enable GPU-acceleration for many existing data science tasks with zero code changes. You will learn how to use GPU-accelerated tools to conduct data science faster, leading to more scalable, reliable, and cost-effective results!

Improved designs that produce trusted, reliable, and safe (TRS) systems will build on successful direct manipulation guidelines that provide a visual display of the objects of interest, rapid, incremental, and reversible operations, with informative feedback for every user action. Elevators, thermostats, airbags, text messaging systems, and the 737 MAX provide positive and negative lessons, in charting the landscape of autonomy and control.  Design guidelines and independent oversight mechanisms for prospective design reviews and retrospective analyses of failures will clarify the role of human responsibility, even as automation increases.  


Ben Shneiderman

Emeritus Distinguished University Professor in the Department of Computer Science, Founding Director (1983-2000) of the Human-Computer Interaction Laboratory (http://hcil.umd.edu), and a Member of the UM Institute for Advanced Computer Studies (UMIACS) at the University of Maryland.  He is a Fellow of the AAAS, ACM, IEEE, and NAI, and a Member of the National Academy of Engineering, in recognition of his pioneering contributions to human-computer interaction and information visualization. His widely-used contributions include the clickable highlighted web-links, high-precision touchscreen keyboards for mobile devices, and tagging for photos.  Shneiderman’s information visualization innovations include dynamic query sliders for Spotfire, development of treemaps for viewing hierarchical data, novel network visualizations for NodeXL, and event sequence analysis for electronic health records.

Ben is the co-author with Catherine Plaisant of Designing the User Interface: Strategies for Effective Human-Computer Interaction (6th ed., 2016).  He co-authored Readings in Information Visualization: Using Vision to Think (1999) and Analyzing Social Media Networks with NodeXL (2nd edition, 2019).  His book Leonardo’s Laptop (MIT Press) won the IEEE book award for Distinguished Literary Contribution. The New ABCs of Research: Achieving Breakthrough Collaborations (Oxford, 2016) describes how research can produce higher impacts.