A Comprehensive Review of Bias in Deep Learning Models: Methods, Impacts, and Future Directions

Shah, Milind; Sureja, Nitesh

doi:10.1007/s11831-024-10134-2

Cited by 3 publications

References 41 publications

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Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications

Mienye,

Swart,

Obaido

2024

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Recurrent neural networks (RNNs) have significantly advanced the field of machine learning (ML) by enabling the effective processing of sequential data. This paper provides a comprehensive review of RNNs and their applications, highlighting advancements in architectures, such as long short-term memory (LSTM) networks, gated recurrent units (GRUs), bidirectional LSTM (BiLSTM), echo state networks (ESNs), peephole LSTM, and stacked LSTM. The study examines the application of RNNs to different domains, including natural language processing (NLP), speech recognition, time series forecasting, autonomous vehicles, and anomaly detection. Additionally, the study discusses recent innovations, such as the integration of attention mechanisms and the development of hybrid models that combine RNNs with convolutional neural networks (CNNs) and transformer architectures. This review aims to provide ML researchers and practitioners with a comprehensive overview of the current state and future directions of RNN research.

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Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications

Mienye,

Swart,

Obaido

2024

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Empirical Study and Mitigation Methods of Bias in LLM-Based Robots

Zhou

2024

AJST

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Our study provides a comprehensive analysis of biased behaviors exhibited by robots utilizing large language models (LLMs) in real-world applications, focusing on five experimental scenarios: customer service, education, healthcare, recruitment, and social interaction. The analysis reveals significant differences in user experiences based on race, health status, work experience, and social status. For instance, the average satisfaction score for white customers is 4.2, compared to 3.5 for black customers, and the response accuracy for white students is 92%, versus 85% for black students. To address these biases, we propose several mitigation methods, including data resampling, model regularization, post-processing techniques, diversity assessment, and user feedback mechanisms. These methods aim to enhance the fairness and inclusivity of robotic systems, promoting healthy human-robot interactions. By combining our quantitative data analysis with existing research, we affirm the importance of bias detection and mitigation, and propose various improvement strategies. Future research should further explore data balancing strategies, fairness-constrained models, real-time monitoring and adjustment mechanisms, and cross-domain studies to comprehensively evaluate and improve the performance of LLM-based robotic systems across various tasks.

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Perspective Chapter: Future Impact of Artificial Intelligence on Medical Subspecialties – Dermatology and Neurology

Abidi,

Hanrahan

et al. 2024

Artificial Intelligence in Medicine and Surgery - An Exploration of Current Trends, Potential Opportunities, and Evolving Threa

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Without a doubt, academic medicine and research fields have been greatly impacted by the recent introduction of artificial intelligence (AI) machines and software programs. For subspecialties, such as dermatology and neurology, AI systems have been integrated to assist in the management of workflow in the office and clinical settings. This chapter highlights a review of the most up-to-date AI tools for clinical applications in dermatology, and its impact on telemedicine and medical education. Our authors also comment on challenges with AI in dermatology, particularly with consumer trust. Within the field of neurology, the authors examined the impact of AI technologies in imaging interpretation, electroencephalography (EEG) interpretation, in the neuro-intensive care unit (ICU) setting, for stroke events, epilepsy, and neurodegenerative conditions. We conclude our chapter with a brief overview of job security and the implications for medical professionals to work more with AI in the future.

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A Comprehensive Review of Bias in Deep Learning Models: Methods, Impacts, and Future Directions

Cited by 3 publications

References 41 publications

Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications

Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications

Empirical Study and Mitigation Methods of Bias in LLM-Based Robots

Perspective Chapter: Future Impact of Artificial Intelligence on Medical Subspecialties – Dermatology and Neurology

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