Autonomous Vehicles, Moral Responsibility Gap, and the Ethics of Knowing Responsibly

“I could not have known” is a characteristic defense against moral blame when a driverless vehicle, such as an Apollo Go robotaxi, causes harm and no human made the decision at issue. This article argues that the defense holds only where the ignorance survived the inquiry that the actor’s role and situation required. Existing responses address a narrower problem: meaningful human control secures the control condition of moral responsibility, and explainable AI makes a particular output intelligible after the fact. Neither supplies a standard for how actors should investigate, weigh, and communicate evidence before deployment and during operation. Drawing on Lorraine Code’s responsibilist epistemology, the article develops such a standard. Responsible inquiry requires actors to test their evidence against what would count against it, and to organize what they learn so that it reaches the decisions it bears on. How much it demands varies with stakes, capacity, role, and subject matter. Ignorance produced or sustained by a breach of this standard constitutes epistemic negligence. Such negligence supports moral blame when it informs a decision that exposes others to unjustified risk or compromises justified reliance. Individuals answer for inquiry and communication, organizations for integrating distributed knowledge, institutions for the evidential and corrective structures within which both operate. The Apollo Go incidents supply the article’s principal test case.

Authors

Institutions

Publication Details

Journal
Science and Engineering Ethics
Published
2026-11-09
DOI
https://doi.org/10.1007/s11948-026-00639-3
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

Autonomous Vehicles, Moral Responsibility Gap, and the Ethics of Knowing Responsibly

Weimin Zeng
Science and Engineering Ethics
Ethics and Social Impacts of AI
article

Autonomous Vehicles, Moral Responsibility Gap, and the Ethics of Knowing Responsibly

Weimin Zeng
article en

Abstract

“I could not have known” is a characteristic defense against moral blame when a driverless vehicle, such as an Apollo Go robotaxi, causes harm and no human made the decision at issue. This article argues that the defense holds only where the ignorance survived the inquiry that the actor’s role and situation required. Existing responses address a narrower problem: meaningful human control secures the control condition of moral responsibility, and explainable AI makes a particular output intelligible after the fact. Neither supplies a standard for how actors should investigate, weigh, and communicate evidence before deployment and during operation. Drawing on Lorraine Code’s responsibilist epistemology, the article develops such a standard. Responsible inquiry requires actors to test their evidence against what would count against it, and to organize what they learn so that it reaches the decisions it bears on. How much it demands varies with stakes, capacity, role, and subject matter. Ignorance produced or sustained by a breach of this standard constitutes epistemic negligence. Such negligence supports moral blame when it informs a decision that exposes others to unjustified risk or compromises justified reliance. Individuals answer for inquiry and communication, organizations for integrating distributed knowledge, institutions for the evidential and corrective structures within which both operate. The Apollo Go incidents supply the article’s principal test case.

Science and Engineering Ethics
Peking University (CN)
Ethics and Social Impacts of AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon