Stanford
University
  • Stanford Home
  • Maps & Directions
  • Search Stanford
  • Emergency Info
  • Terms of Use
  • Privacy
  • Copyright
  • Trademarks
  • Non-Discrimination
  • Accessibility
© Stanford University.  Stanford, California 94305.
HAI Weekly Seminar with Ron Chrisley - Against Ethical Robots | Stanford HAI
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.
eventSeminar

HAI Weekly Seminar with Ron Chrisley - Against Ethical Robots

Status
Past
Date
Friday, May 08, 2020 11:00 AM - 12:00 PM PST/PDT
Topics
Ethics, Equity, Inclusion
Robotics
Human Reasoning
Share
Link copied to clipboard!

Related Events

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!

Empirical Methods in the Age of AI Conference
ConferenceOct 02, 2026
October
02
2026

Save the Date. Artificial intelligence is transforming how researchers collect, analyze, and learn from data. As AI systems become increasingly integrated into scientific discovery, business decision-making, and policy analysis, they are reshaping both the questions researchers can ask and the methods they use to answer them.

Event

Empirical Methods in the Age of AI Conference

Oct 02, 2026

Save the Date. Artificial intelligence is transforming how researchers collect, analyze, and learn from data. As AI systems become increasingly integrated into scientific discovery, business decision-making, and policy analysis, they are reshaping both the questions researchers can ask and the methods they use to answer them.

"In this talk I hope to illustrate how AI ethics can avoid the undesirable extremes of two dimensions:

First dimension: Complacency vs Inflation

On the one hand, I will argue that we should eschew the complacent view that AI presents no novel challenges for ethics, the view that AI is just a technology like any other, so extant ethical principles for non-AI technologies are all we need.  On the other hand, I will also argue that we should also avoid the inflationary view that the need for new ethical principles for AI derives from the fact that AI systems are themselves moral agents and/or patients.

Second dimension: Reactive systems vs Robots with Obligations

On the one hand, I will argue that the ethical construction of autonomous AI systems (including, but not limited to, autonomous robots such as driverless cars) will require that such systems do more than merely transform an input signal to an output signal (as is prevalent in much machine learning technology); at least part of that transformation, to have the right counterfactual richness that ethics requires, will have to have deliberative structure. On the other hand, AI systems that reason about their ethical obligations and what is morally permissible for them are not a solution since AI systems will not, for the foreseeable future, be the kinds of things that could have ethical obligations or moral permissions.

For each of these dimensions, I give a specific example (a policy, and a design, respectively) that avoids the horns of the dilemma, and the moral hazards they entail." - Ron Chrisley

Presentation Slides

Watch Event Recording

Speaker
Ron Chrisley
Professor of Cognitive Science and Artificial Intelligence (Informatics) at University of Sussex