Unite.AI

MARCH 14, 2024

This includes AI systems used for indiscriminate surveillance, social scoring, and manipulative or exploitative purposes. In the realm of high-risk AI, the legislation imposes obligations for risk assessment, data quality control, and human oversight.

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Pickl AI

JUNE 23, 2023

Monitoring and Evaluation Data-centric AI systems require continuous monitoring and evaluation to assess their performance and identify potential issues. This involves analyzing metrics, feedback from users, and validating the accuracy and reliability of the AI models. Governance Emphasizes data governance, privacy, and ethics.

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Pickl AI

APRIL 27, 2023

Hence, introducing the concept of responsible AI has become significant. Responsible AI focuses on harnessing the power of Artificial Intelligence while complying with designing, developing, and deploying AI with good intentions. By adopting responsible AI, companies can positively impact the customer.

Data Science 52

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John Snow Labs

OCTOBER 19, 2023

One reason for this bias is the data used to train these models, which often reflects historical gender inequalities present in the text corpus. To address gender bias in AI, it’s crucial to improve the data quality by including diverse perspectives and avoiding the perpetuation of stereotypes.

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Viso.ai

DECEMBER 18, 2023

Organizations can easily source data to promote the development, deployment, and scaling of their computer vision applications. This allows for: Developing Robust and Generalizable AI Models. Training AI models on synthetic data exposes them to a wider range of variations and edge cases.

Computer Vision 52

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AWS Machine Learning Blog

JANUARY 26, 2024

After your generative AI workload environment has been secured, you can layer in AI/ML-specific features, such as Amazon SageMaker Data Wrangler to identify potential bias during data preparation and Amazon SageMaker Clarify to detect bias in ML data and models.

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Topbots

DECEMBER 19, 2023

Presenters from various spheres of AI research shared their latest achievements, offering a window into cutting-edge AI developments. In this article, we delve into these talks, extracting and discussing the key takeaways and learnings, which are essential for understanding the current and future landscapes of AI innovation.

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