Trustworthy AI: responses to commentators.

Asian J Philos

COGITO Epistemology Research Centre, University of Glasgow, 67-69 Oakfield Ave, Glasgow, G12 8LP UK.

Published: December 2024

In 'Trustworthy Artificial Intelligence', we develop a novel account of how it is that AI can be trustworthy and what it takes for an AI to be trustworthy. In this paper, we respond to a suite of recent comments on this account, due to J. Adam Carter, Dong-yong Choi, Rune Nyrup, and Fei Song. We would like to thank all four for their thoughtful engagement with our work, as well as the Asian Journal of Philosophy for publishing the symposium on our paper. The game plan for the paper is as follows. We will first briefly rehearse the account and then respond to comments in turn.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11663812PMC
http://dx.doi.org/10.1007/s44204-024-00229-9DOI Listing

Publication Analysis

Top Keywords

trustworthy responses
4
responses commentators
4
commentators 'trustworthy
4
'trustworthy artificial
4
artificial intelligence'
4
intelligence' develop
4
develop novel
4
novel account
4
account trustworthy
4
trustworthy takes
4

Similar Publications

Background: This study aims to evaluate the capabilities and limitations of large language models (LLMs) for providing patient education for men undergoing radiotherapy for localized prostate cancer, incorporating assessments from both clinicians and patients.

Methods: Six questions about definitive radiotherapy for prostate cancer were designed based on common patient inquiries. These questions were presented to different LLMs [ChatGPT‑4, ChatGPT-4o (both OpenAI Inc.

View Article and Find Full Text PDF

Introduction: The orphan and vulnerable children crisis has raised the need for alternative solutions to their problems. These new alternatives gave prominence to the growth of community-based organisations and their interventions. Community-based interventions are a crucial component of the response to ensure that the demands of orphans and vulnerable children are mitigated as they offer initial support and act as well-being nets.

View Article and Find Full Text PDF

Background: One of the main challenges in the maintenance of registries is to keep a high follow-up rate and a reliable strategy to limit dropout is currently lacking. Aim of this study was to utilize machine learning (ML) models to highlight the characteristics of patients who are most likely to drop out, and to evaluate the potential cost effectiveness of the implementation of a follow-up system based on the obtained data.

Methods: All patients recruited in the local spine surgery registry were included and demographic, peri- and postoperative data were collected.

View Article and Find Full Text PDF

Trustworthiness of a machine learning early warning model in medical and surgical inpatients.

JAMIA Open

February 2025

Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN 55905, United States.

Objectives: In the general hospital wards, machine learning (ML)-based early warning systems (EWSs) can identify patients at risk of deterioration to facilitate rescue interventions. We assess subpopulation performance of a ML-based EWS on medical and surgical adult patients admitted to general hospital wards.

Materials And Methods: We assessed the scores of an EWS integrated into the electronic health record and calculated every 15 minutes to predict a composite adverse event (AE): all-cause mortality, transfer to intensive care, cardiac arrest, or rapid response team evaluation.

View Article and Find Full Text PDF

Background: Prior research has shown that African American men and women are more likely to receive lower quality healthcare compared to their white counterparts, which is exacerbated in jail and prison healthcare systems.

Objective: The purpose of this study is to explore barriers and facilitators to quality healthcare among African American men and women released from Illinois State Prisons or Cook County Jail by examining their opinions and experiences with overall healthcare and cancer screening during and after incarceration.

Design: Four focus groups (n = 25 "co-researchers") were conducted to understand how formerly incarcerated African American men and women perceive and describe their experience of accessing, understanding, and utilizing healthcare during and after incarceration.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!