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Algorithmic fairness in artificial intelligence for medicine and healthcare
In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
Towards a science of human-AI decision making: An overview of design space in empirical human-subject studies
AI systems are adopted in numerous domains due to their increasingly strong predictive
performance. However, in high-stakes domains such as criminal justice and healthcare, full …
performance. However, in high-stakes domains such as criminal justice and healthcare, full …
Open problems and fundamental limitations of reinforcement learning from human feedback
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems
to align with human goals. RLHF has emerged as the central method used to finetune state …
to align with human goals. RLHF has emerged as the central method used to finetune state …
On the opportunities and risks of foundation models
AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
[PDF][PDF] Ai transparency in the age of llms: A human-centered research roadmap
The rise of powerful large language models (LLMs) brings about tremendous opportunities
for innovation but also looming risks for individuals and society at large. We have reached a …
for innovation but also looming risks for individuals and society at large. We have reached a …
Explainable ai is dead, long live explainable ai! hypothesis-driven decision support using evaluative ai
T Miller - Proceedings of the 2023 ACM conference on fairness …, 2023 - dl.acm.org
In this paper, we argue for a paradigm shift from the current model of explainable artificial
intelligence (XAI), which may be counter-productive to better human decision making. In …
intelligence (XAI), which may be counter-productive to better human decision making. In …
Underspecification presents challenges for credibility in modern machine learning
Machine learning (ML) systems often exhibit unexpectedly poor behavior when they are
deployed in real-world domains. We identify underspecification in ML pipelines as a key …
deployed in real-world domains. We identify underspecification in ML pipelines as a key …
A systematic literature review of user trust in AI-enabled systems: An HCI perspective
Abstract User trust in Artificial Intelligence (AI) enabled systems has been increasingly
recognized and proven as a key element to fostering adoption. It has been suggested that AI …
recognized and proven as a key element to fostering adoption. It has been suggested that AI …
Causal inference in natural language processing: Estimation, prediction, interpretation and beyond
A fundamental goal of scientific research is to learn about causal relationships. However,
despite its critical role in the life and social sciences, causality has not had the same …
despite its critical role in the life and social sciences, causality has not had the same …
[HTML][HTML] Trust in artificial intelligence: From a Foundational Trust Framework to emerging research opportunities
With the rise of artificial intelligence (AI), the issue of trust in AI emerges as a paramount
societal concern. Despite increased attention of researchers, the topic remains fragmented …
societal concern. Despite increased attention of researchers, the topic remains fragmented …