About

Alex John London is the K&L Gates Professor of Ethics and Computational Technologies at Carnegie Mellon University, where he also co-leads the K&L Gates Initiative in Ethics and Computational Technologies. His work focuses on ethical and policy issues related to the development and deployment of novel technologies in medicine, biotechnology, and artificial intelligence, as well as methodological issues in theoretical and practical ethics, and cross-national issues of justice and fairness. He has authored the book 'For the Common Good: Philosophical Foundations of Research Ethics,' which explores the ethical foundations of research involving human participants, emphasizing the importance of connecting research to social justice and the common good. London has contributed extensively to the field of research ethics, policy, and oversight, shaping key guidelines and participating in international and national advisory groups, including the World Health Organization and the U.S. National Academy of Medicine. His research extends to the ethical challenges of AI, including issues of bias, accountability, and governance, and he critiques current standards such as explainability in favor of institutional mechanisms that ensure social trust and non-domination. Additionally, he has engaged with biosecurity policy, serving on the U.S. National Science Advisory Board for Biosecurity and collaborating with organizations like the Nuclear Threat Initiative. His foundational work in ethics also addresses issues in clinical research design, justice in international research, and the ethical oversight of biomedical research, emphasizing non-paternalistic approaches and the importance of incentives aligned with social goals.

Research topics

• Political Science
• Sociology
• Engineering ethics
• Medicine
• Psychology
• Law and economics
• Law
• Computer Science
• Artificial Intelligence
• Public relations
• Process management
• Management science
• Data science
• Social psychology
• Business
• Knowledge management
• Economics
• Risk analysis (engineering)
• Engineering

Selected publications

• Solving the problem of inception: a cross-species perspective on strategies for a mechanistic refinement of intracortical microstimulation

  Journal of Neural Engineering · 2026-03-19

  articleOpen access

  Abstract Objective. Microstimulation delivers electrical pulses directly into the brain, with one of its promises being to restore lost senses to millions of people. Yet a fundamental challenge remains: how do intracortical microstimulation (ICMS) patterns engage neural circuits to achieve the inception of specific experiences, such as vivid sensory percepts of touch and vision? Here, we define ‘inception’ as the initiation of percepts evoked by microstimulation through the mapping of stimulation to circuit-level activity that results in sensory experiences. Approach. This perspective proposes an integrated research framework that combines Reverse Translation, Forward Translation, and computational neuroscience to bridge insights between clinical observations and high-resolution animal studies. Framework. Our framework envisions the development and evaluation of ICMS strategies within a cross-species system that narrows the range of plausible underlying neural mechanisms and the set of evoked perceptual outcomes. Reverse Translation uses human perceptual reports about phosphenes, tones, and touch to guide investigations in rodents and non-human primates, mapping the cell types and circuits underlying each percept. Forward Translation leverages these biological insights to design refined ICMS protocols for selective circuit engagement. Bidirectional Translation weaves these approaches together through computational neuroscience, ensuring that experimental observations iteratively and continuously refine one another across species and experimental modalities. Significance. This integrated strategy aims to transform microstimulation research into a dynamic dialogue between fundamental science and human experience. Harnessing the Bidirectional Translation Framework can accelerate therapies that enhance quality of life for people with sensory or motor impairments, and contribute more broadly to systems neuroscience by uncovering the mechanisms by which causal manipulation changes activity in neurons and networks.

  PublisherDOI

• A scoping review of silent trials for medical artificial intelligence

  OSF Preprints (OSF Preprints) · 2026-02-05

  preprintOpen access

  ‘Silent’ evaluation refers to the prospective, non-interventional testing of artificial intelligence (AI) model performance in the intended clinical setting without affecting patient care or institutional operations. The silent evaluation phase has received less attention than in silico algorithm development or formal clinical evaluations, despite increasing recognition of this type of evaluation as a critical phase in an effective translation process for healthcare AI tools. There are currently no formal guidelines for conducting silent AI evaluations in health settings. We undertook a scoping review to identify silent AI evaluations described in the literature, aiming to summarize current practices for the conduct of silent evaluations. We screened PubMed, Web of Science, and Scopus databases for articles fitting our criteria for silent AI evaluations, or ‘silent trials’, published from 2015 to 2025. A total of 891 articles were identified, and 75 met the criteria for inclusion into the final review. We found wide variance in terminology, description, and rationale for silent evaluations; this led to substantial heterogeneity in what was reported. Overwhelmingly, papers reported measurement of AUC, precision/recall, positive and negative predictive values and similar technical performance metrics. Far fewer studies reported the verification of outputs against an in-situ clinical ground truth, and, when reported, the comprehensiveness of such verification was highly variable. We noted relatively less discussion of sociotechnical components such as stakeholder engagement and human-computer interaction elements. We conclude that there is an opportunity to bring together diverse evaluative practices (e.g., from data science, human factors, and other fields) if the silent evaluation phase is to be maximally effective as a translational mechanism these gaps mirror challenges in effective translation of AI tools from “computer to bedside” and identify opportunities to improve silent evaluation protocols that address key translational needs. This is important as healthcare organizations and regulatory bodies worldwide seek guidance for gathering meaningful evidence of the impact of AI tools on clinical practice.

  Publisher

• A scoping review of silent trials for medical artificial intelligence

  Nature Health · 2026-02-16 · 3 citations

  articleOpen access

  Abstract A ‘silent trial’ refers to the prospective, noninterventional testing of artificial intelligence (AI) models in the intended clinical setting without affecting patient care or institutional operations. The silent evaluation phase has received less attention than in silico algorithm development or formal clinical evaluations, despite its increasing recognition as a critical phase. There are no formal guidelines for performing silent AI evaluations in healthcare settings. We conducted a scoping review to identify silent AI evaluations described in the literature and to summarize current practices for performing silent testing. We screened the PubMed, Web of Science and Scopus databases for articles fitting our criteria for silent AI evaluations, or silent trials, published from 2015 to 2025. A total of 891 articles were identified, of which 75 met the criteria for inclusion in the final review. We found wide variance in terminology, description and rationale for silent evaluations, leading to substantial heterogeneity in the reported information. Overwhelmingly, the papers reported measurements of area under the curve and similar metrics of technical performance. Far fewer studies reported verification of outputs against an in situ clinical ground truth; when reported, the approaches varied in comprehensiveness. We noted less discussion of sociotechnical components, such as stakeholder engagement and human–computer interaction elements. We conclude that there is an opportunity to bring together diverse evaluative practices (for example, from data science, human factors and other fields) if the silent evaluation phase is to be maximally effective. These gaps mirror challenges in the effective translation of AI tools from computer to bedside and identify opportunities to improve silent evaluation protocols that address key needs.

  PublisherOA PDFDOI

• A scoping review of silent trials for medical artificial intelligence

  2026-02-05

  articleOpen access

  ‘Silent’ evaluation refers to the prospective, non-interventional testing of artificial intelligence (AI) model performance in the intended clinical setting without affecting patient care or institutional operations. The silent evaluation phase has received less attention than in silico algorithm development or formal clinical evaluations, despite increasing recognition of this type of evaluation as a critical phase in an effective translation process for healthcare AI tools. There are currently no formal guidelines for conducting silent AI evaluations in health settings. We undertook a scoping review to identify silent AI evaluations described in the literature, aiming to summarize current practices for the conduct of silent evaluations. We screened PubMed, Web of Science, and Scopus databases for articles fitting our criteria for silent AI evaluations, or ‘silent trials’, published from 2015 to 2025. A total of 891 articles were identified, and 75 met the criteria for inclusion into the final review. We found wide variance in terminology, description, and rationale for silent evaluations; this led to substantial heterogeneity in what was reported. Overwhelmingly, papers reported measurement of AUC, precision/recall, positive and negative predictive values and similar technical performance metrics. Far fewer studies reported the verification of outputs against an in-situ clinical ground truth, and, when reported, the comprehensiveness of such verification was highly variable. We noted relatively less discussion of sociotechnical components such as stakeholder engagement and human-computer interaction elements. We conclude that there is an opportunity to bring together diverse evaluative practices (e.g., from data science, human factors, and other fields) if the silent evaluation phase is to be maximally effective as a translational mechanism these gaps mirror challenges in effective translation of AI tools from “computer to bedside” and identify opportunities to improve silent evaluation protocols that address key translational needs. This is important as healthcare organizations and regulatory bodies worldwide seek guidance for gathering meaningful evidence of the impact of AI tools on clinical practice.

  PublisherDOI

• Avoiding a bridge to nowhere: managing the transfer of agency when an older adult can no longer use assistive AI

  AI and Ethics · 2026-02-01

  articleOpen access1st authorCorresponding

  We define an agency transfer point as the point at which a person is no longer capable of performing an important task or function such that responsibility for that task or function must be transferred to another person. Because many older adults worry that if they experience cognitive decline they might lose their ability to function in ways they value, there’s growing interest in the possibility that AI systems might help older adults maintain their independence. This paper identifies several strategies through which AI systems might help older adults avert agency transfer points caused by cognitive decline. Although safe and effective assistive AI systems for older adults do not yet exist, it is important to proactively identify and address problems likely to result from their development and deployment. Importantly, although AI support might avert agency transfer points, there has been little recognition of the prospect that if individuals experience further declines in cognitive capacity, agency transfer points will reemerge. We therefore examine the ethical implications of alternative governance strategies and social policies for coping with the reemergence of agency transfer points for older adults who rely on AI systems to remain independent. This includes consideration of a requirement for contingency planning on the part of older adults as a condition of using such systems. We also consider the role that conversational AI systems might play in the process of contingency planning and after responsibility for some aspects of an older adult’s care has been transferred to another person.

  PublisherOA PDFDOI

• CANAIRI: the Collaboration for Translational Artificial Intelligence Trials in healthcare

  Nature Medicine · 2025-01-01 · 20 citations

  letter
  PublisherDOI

• Disclosure as Absolution in Medicine: Disentangling Autonomy from Beneficence and Justice in Artificial Intelligence

  The American Journal of Bioethics · 2025-02-24 · 4 citations

  letterOpen accessSenior author
  PublisherOA PDFDOI

• Opening the ‘black box’ of the silent phase evaluation for artificial intelligence: a scoping review and critical analysis

  2025-10-28

  articleOpen access

  ‘Silent’ evaluation refers to the prospective, non-interventional testing of artificial intelligence (AI) model performance in the intended clinical setting without affecting patient care or institutional operations. The silent evaluation phase has received less attention than in silico algorithm development or formal clinical evaluations, despite increasing recognition of this type of evaluation as a critical phase in an effective translation process for healthcare AI tools. There are currently no formal guidelines for conducting silent AI evaluations in health settings. We undertook a scoping review to identify silent AI evaluations described in the literature, aiming to summarize current practices for the conduct of silent evaluations. We screened PubMed, Web of Science, and Scopus databases for articles fitting our criteria for silent AI evaluations, or ‘silent trials’, published from 2015 to 2025. A total of 891 articles were identified, and 75 met the criteria for inclusion into the final review. We found wide variance in terminology, description, and rationale for silent evaluations; this led to substantial heterogeneity in what was reported. Overwhelmingly, papers reported measurement of AUC, precision/recall, positive and negative predictive values and similar technical performance metrics. Far fewer studies reported the verification of outputs against an in-situ clinical ground truth, and, when reported, the comprehensiveness of such verification was highly variable. We noted relatively less discussion of sociotechnical components such as stakeholder engagement and human-computer interaction elements. We conclude that there is an opportunity to bring together diverse evaluative practices (e.g., from data science, human factors, and other fields) if the silent evaluation phase is to be maximally effective as a translational mechanism these gaps mirror challenges in effective translation of AI tools from “computer to bedside” and identify opportunities to improve silent evaluation protocols that address key translational needs. This is important as healthcare organizations and regulatory bodies worldwide seek guidance for gathering meaningful evidence of the impact of AI tools on clinical practice.

  PublisherDOI

• Adaptive versus Fixed Designs in Confirmatory Clinical Trials: Centering the Choice on Ethics

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Multi-objective reinforcement learning framework for beneficent artificial intelligence

  Neural Computing and Applications · 2025-06-08

  article
  PublisherDOI

Frequent coauthors

• Jonathan Kimmelman

  McGill University

  65 shared

• Spencer Phillips Hey

  Manchester University NHS Foundation Trust

  57 shared

• Benjamin Gregory Carlisle

  Berlin Institute of Health at Charité - Universitätsmedizin Berlin

  50 shared

• Tim Ramsay

  Ottawa Hospital Research Institute

  50 shared

• Nadine Demko

  McGill University

  49 shared

• Georgina Freeman

  University of Calgary

  49 shared

• Amanda Hakala

  McGill University

  49 shared

• Nathalie MacKinnon

  St. Paul's Hospital

  49 shared

Education

• Ph.D., Philosophy

  University of Virginia

  1999

Awards & honors

• Elliott Dunlap Smith Award for Distinguished Teaching and Ed…
• Distinguished Service Award from the American Society of Bio…
• New Directions Fellowship from the Andrew W. Mellon Foundati…