AI fairness in data management and analytics: A review on challenges, methodologies and applications
P Chen, L Wu, L Wang - Applied Sciences, 2023 - mdpi.com
This article provides a comprehensive overview of the fairness issues in artificial intelligence
(AI) systems, delving into its background, definition, and development process. The article …
(AI) systems, delving into its background, definition, and development process. The article …
An empirical survey on explainable ai technologies: Recent trends, use-cases, and categories from technical and application perspectives
M Nagahisarchoghaei, N Nur, L Cummins, N Nur… - Electronics, 2023 - mdpi.com
In a wide range of industries and academic fields, artificial intelligence is becoming
increasingly prevalent. AI models are taking on more crucial decision-making tasks as they …
increasingly prevalent. AI models are taking on more crucial decision-making tasks as they …
The fallacy of AI functionality
Deployed AI systems often do not work. They can be constructed haphazardly, deployed
indiscriminately, and promoted deceptively. However, despite this reality, scholars, the …
indiscriminately, and promoted deceptively. However, despite this reality, scholars, the …
“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI
N Sambasivan, S Kapania, H Highfill… - proceedings of the …, 2021 - dl.acm.org
AI models are increasingly applied in high-stakes domains like health and conservation.
Data quality carries an elevated significance in high-stakes AI due to its heightened …
Data quality carries an elevated significance in high-stakes AI due to its heightened …
Sociotechnical harms of algorithmic systems: Scoping a taxonomy for harm reduction
Understanding the landscape of potential harms from algorithmic systems enables
practitioners to better anticipate consequences of the systems they build. It also supports the …
practitioners to better anticipate consequences of the systems they build. It also supports the …
Problems with Shapley-value-based explanations as feature importance measures
IE Kumar, S Venkatasubramanian… - International …, 2020 - proceedings.mlr.press
Game-theoretic formulations of feature importance have become popular as a way to"
explain" machine learning models. These methods define a cooperative game between the …
explain" machine learning models. These methods define a cooperative game between the …
Assessing the fairness of ai systems: Ai practitioners' processes, challenges, and needs for support
Various tools and practices have been developed to support practitioners in identifying,
assessing, and mitigating fairness-related harms caused by AI systems. However, prior …
assessing, and mitigating fairness-related harms caused by AI systems. However, prior …
How to evaluate trust in AI-assisted decision making? A survey of empirical methodologies
The spread of AI-embedded systems involved in human decision making makes studying
human trust in these systems critical. However, empirically investigating trust is challenging …
human trust in these systems critical. However, empirically investigating trust is challenging …
Studying up machine learning data: Why talk about bias when we mean power?
Research in machine learning (ML) has argued that models trained on incomplete or biased
datasets can lead to discriminatory outputs. In this commentary, we propose moving the …
datasets can lead to discriminatory outputs. In this commentary, we propose moving the …
Human-AI collaboration in data science: Exploring data scientists' perceptions of automated AI
The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One
application domain is data science. New techniques in automating the creation of AI, known …
application domain is data science. New techniques in automating the creation of AI, known …