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AI and Financial Crime Prevention: Why Banks Need a Balanced Approach

Unite.AI

AI is a two-sided coin for banks: while its unlocking many possibilities for more efficient operations, it can also pose external and internal risks. In the US alone, generative AI is expected to accelerate fraud losses to an annual growth rate of 32%, reaching US$40 billion by 2027, according to a recent report by Deloitte.

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Data Monocultures in AI: Threats to Diversity and Innovation

Unite.AI

AI is reshaping the world, from transforming healthcare to reforming education. Data is at the centre of this revolutionthe fuel that powers every AI model. In AI, relying on uniform datasets creates rigid, biased, and often unreliable models. Facial recognition is a well-documented example of data monoculture in AI.

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How Quality Data Fuels Superior Model Performance

Unite.AI

Heres the thing no one talks about: the most sophisticated AI model in the world is useless without the right fuel. That fuel is dataand not just any data, but high-quality, purpose-built, and meticulously curated datasets. Data-centric AI flips the traditional script. Why is this the case?

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Beyond the Hype: Unveiling the Real Impact of Generative AI in Drug Discovery

Unite.AI

Since Insilico Medicine developed a drug for idiopathic pulmonary fibrosis (IPF) using generative AI, there's been a growing excitement about how this technology could change drug discovery. Traditional methods are slow and expensive , so the idea that AI could speed things up has caught the attention of the pharmaceutical industry.

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Navigating Explainable AI in In Vitro Diagnostics: Compliance and Transparency Under European Regulations

Marktechpost

The Role of Explainable AI in In Vitro Diagnostics Under European Regulations: AI is increasingly critical in healthcare, especially in vitro diagnostics (IVD). The European IVDR recognizes software, including AI and ML algorithms, as part of IVDs.

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How data stores and governance impact your AI initiatives

IBM Journey to AI blog

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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Considerations for addressing the core dimensions of responsible AI for Amazon Bedrock applications

AWS Machine Learning Blog

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.