AWS Machine Learning Blog

NOVEMBER 15, 2024

The rapid advancement of generative AI promises transformative innovation, yet it also presents significant challenges. Concerns about legal implications, accuracy of AI-generated outputs, data privacy, and broader societal impacts have underscored the importance of responsible AI development.

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IBM Journey to AI blog

OCTOBER 12, 2023

But the implementation of AI is only one piece of the puzzle. The tasks behind efficient, responsible AI lifecycle management The continuous application of AI and the ability to benefit from its ongoing use require the persistent management of a dynamic and intricate AI lifecycle—and doing so efficiently and responsibly.

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Pickl AI

FEBRUARY 17, 2025

Introduction: The Reality of Machine Learning Consider a healthcare organisation that implemented a Machine Learning model to predict patient outcomes based on historical data. However, once deployed in a real-world setting, its performance plummeted due to data quality issues and unforeseen biases.

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The MLOps Blog

JUNE 27, 2023

Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations. Data monitoring tools help monitor the quality of the data.

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Unite.AI

JULY 5, 2024

Robust data management is another critical element. Establishing strong information governance frameworks ensures data quality, security and regulatory compliance. Accountability and Transparency: Accountability in Gen AI-driven decisions involve multiple stakeholders, including developers, healthcare providers, and end users.

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Marktechpost

DECEMBER 11, 2024

Image Source : LG AI Research Blog ([link] Responsible AI Development: Ethical and Transparent Practices The development of EXAONE 3.5 models adhered to LG AI Research s Responsible AI Development Framework, prioritizing data governance, ethical considerations, and risk management. model scored 70.2.

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Pickl AI

AUGUST 1, 2024

Data Quality and Quantity Deep Learning models require large amounts of high-quality, labelled training data to learn effectively. Insufficient or low-quality data can lead to poor model performance and overfitting.

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