相关论文: Ethical Machine Learning in Health Care

The utilization of large language models (LLMs) in the Healthcare domain has generated both excitement and concern due to their ability to effectively respond to freetext queries with certain professional knowledge. This survey outlines the…

In this manuscript, we present an argument that machine learning, a subfield of artificial intelligence, can drive improvement in value-based health care through reducing error in clinical decision making. Much of what has been previously…

The past decade has observed a significant advancement in AI with deep learning-based models being deployed in diverse scenarios, including safety-critical applications. As these AI systems become deeply embedded in our societal…

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and…

Social media platforms provide a continuous stream of real-time news regarding crisis events on a global scale. Several machine learning methods utilize the crowd-sourced data for the automated detection of crises and the characterization…

Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance…

Given the success of ChatGPT, LaMDA and other large language models (LLMs), there has been an increase in development and usage of LLMs within the technology sector and other sectors. While the level in which LLMs has not reached a level…

Rapid advances in Machine Learning (ML) have triggered new trends in Autonomous Vehicles (AVs). ML algorithms play a crucial role in interpreting sensor data, predicting potential hazards, and optimizing navigation strategies. However,…

Following the recent surge in adoption of machine learning (ML), the negative impact that improper use of ML can have on users and society is now also widely recognised. To address this issue, policy makers and other stakeholders, such as…

Large language model (LLM)-based AI agents are increasingly capable of complex clinical reasoning and may soon participate in medical decision-making with limited or no real-time human oversight. This shift raises fundamental questions…

Machine learning (ML) approaches have demonstrated promising results in a wide range of healthcare applications. Data plays a crucial role in developing ML-based healthcare systems that directly affect people's lives. Many of the ethical…

Given the growing use of Artificial Intelligence (AI) and machine learning (ML) methods across all aspects of environmental sciences, it is imperative that we initiate a discussion about the ethical and responsible use of AI. In fact, much…

Ethical frameworks for the use of natural language processing (NLP) are urgently needed to shape how large language models (LLMs) and similar tools are used for healthcare applications. Healthcare faces existing challenges including the…

The aim of this extended abstract is to introduce five examples of ethical issues in HRI that could have potential ethical implications, particularly on HRI participants. We consider these examples important to discuss in order to reach a…

[Context] Generative AI technologies, particularly Large Language Models (LLMs), have transformed numerous domains by enhancing convenience and efficiency in information retrieval, content generation, and decision-making processes. However,…

Interactive machine learning (IML) is a field of research that explores how to leverage both human and computational abilities in decision making systems. IML represents a collaboration between multiple complementary human and machine…

The integration of Large Language Models (LLMs) in social robotics presents a unique set of ethical challenges and social impacts. This research is set out to identify ethical considerations that arise in the design and development of these…

The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to…

Concerns over the risks associated with advances in Artificial Intelligence have prompted calls for greater efforts toward robust and beneficial AI, including machine ethics. Recently, roboticists have responded by initiating the…

An ambitious goal for machine learning is to create agents that behave ethically: The capacity to abide by human moral norms would greatly expand the context in which autonomous agents could be practically and safely deployed, e.g. fully…