Computational Assessment Model for Blind Medical Image Watermarking with Deep Learning

Chacko, Anusha; Chacko, Shanty

doi:10.1007/978-3-031-21438-7_69

Cited by 1 publication

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies have consistently shown that benchmarks can reveal sophisticated insights into how biases manifest in different contexts, offering a more granular understanding of model behavior [19], [59]- [63]. There is a growing consensus that benchmarks must evolve to capture the multidimensional nature of fairness, incorporating diverse perspectives and scenarios [64]- [67]. Research has also highlighted the importance of transparency and interpretability in benchmark results, emphasizing that stakeholders should understand the basis of evaluations to trust and act on them [68]- [71].…”

Section: Evaluating Fairness and Benchmarks In Ai Modelsmentioning

confidence: 99%

“…The ethical dimension of AI development has gained increased attention, with calls for more transparent and accountable modeling practices that prioritize ethical considerations [73], [74]. Moreover, there is a growing acknowledgment of the role of diverse stakeholder engagement in guiding the ethical development of AI, emphasizing the need for multidisciplinary collaboration in addressing the challenges posed by LLM bias [3], [67], [75], [76].…”

Section: Ethical Considerations and Societal Impacts Of Llm Biasmentioning

confidence: 99%

See 1 more Smart Citation

A Comparative Analysis to Evaluate Bias and Fairness Across Large Language Models with Benchmarks

陳文意,

黃兆明

2024

Preprint

View full text Add to dashboard Cite

This study performs a comprehensive evaluation of bias and fairness within Large Language Models (LLMs), including ChatGPT-4, Google Gemini, and Llama 2, utilizing the Google BIG-Bench benchmark. Our analysis reveals varied levels of biases across models, with disparities particularly notable in dimensions such as gender, race, and ethnicity. The Google BIG-Bench benchmark proved instrumental in identifying these biases, though its effectiveness is tempered by challenges in capturing the sophisticated manifestations of bias that emerge in real-world contexts. Comparative performance analysis indicates that while each model exhibits strengths in certain areas, no single model uniformly excels across all fairness and bias metrics. The study underscores the intricate balance between model performance, fairness, and efficiency, highlighting the necessity for ongoing research and development in AI ethics to mitigate bias effectively. Insights from this research advocate for a multifaceted approach to AI development, integrating ethical considerations at every stage to ensure the equitable advancement of technology. The findings prompt a call for continued innovation in model training and benchmarking methodologies, aiming to enhance the fairness and inclusivity of future LLMs.

show abstract

Section: Evaluating Fairness and Benchmarks In Ai Modelsmentioning

confidence: 99%