[HTML][HTML] Gender bias in transformers: A comprehensive review of detection and mitigation strategies
P Nemani, YD Joel, P Vijay, FF Liza - Natural Language Processing …, 2024 - Elsevier
Gender bias in artificial intelligence (AI) has emerged as a pressing concern with profound
implications for individuals' lives. This paper presents a comprehensive survey that explores …
implications for individuals' lives. This paper presents a comprehensive survey that explores …
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 …
Exposing implicit biases and stereotypes in human and artificial intelligence: state of the art and challenges with a focus on gender
Biases in cognition are ubiquitous. Social psychologists suggested biases and stereotypes
serve a multifarious set of cognitive goals, while at the same time stressing their potential …
serve a multifarious set of cognitive goals, while at the same time stressing their potential …
Towards a science of human-ai decision making: a survey of empirical studies
As AI systems demonstrate increasingly strong predictive performance, their adoption has
grown in numerous domains. However, in high-stakes domains such as criminal justice and …
grown in numerous domains. However, in high-stakes domains such as criminal justice and …
How child welfare workers reduce racial disparities in algorithmic decisions
Machine learning tools have been deployed in various contexts to support human decision-
making, in the hope that human-algorithm collaboration can improve decision quality …
making, in the hope that human-algorithm collaboration can improve decision quality …
Disentangling fairness perceptions in algorithmic decision-making: the effects of explanations, human oversight, and contestability
Recent research claims that information cues and system attributes of algorithmic decision-
making processes affect decision subjects' fairness perceptions. However, little is still known …
making processes affect decision subjects' fairness perceptions. However, little is still known …
Ground (less) truth: A causal framework for proxy labels in human-algorithm decision-making
A growing literature on human-AI decision-making investigates strategies for combining
human judgment with statistical models to improve decision-making. Research in this area …
human judgment with statistical models to improve decision-making. Research in this area …
A checklist to combat cognitive biases in crowdsourcing
Recent research has demonstrated that cognitive biases such as the confirmation bias or the
anchoring effect can negatively affect the quality of crowdsourced data. In practice, however …
anchoring effect can negatively affect the quality of crowdsourced data. In practice, however …
Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and …
The increasing use of data-driven decision support systems in industry and governments is
accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of …
accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of …
Investigations of performance and bias in human-AI teamwork in hiring
In AI-assisted decision-making, effective hybrid (human-AI) teamwork is not solely
dependent on AI performance alone, but also on its impact on human decision-making …
dependent on AI performance alone, but also on its impact on human decision-making …