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Bridging code and conscience: UMD’s quest for ethical and inclusive AI

AI News

As artificial intelligence systems increasingly permeate critical decision-making processes in our everyday lives, the integration of ethical frameworks into AI development is becoming a research priority. Kameswaran suggests developing audit tools for advocacy groups to assess AI hiring platforms for potential discrimination.

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Journey to AI blog

. “Foundation models make deploying AI significantly more scalable, affordable and efficient.” It’s essential for an enterprise to work with responsible, transparent and explainable AI, which can be challenging to come by in these early days of the technology. ” Are foundation models trustworthy?

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Seven Trends to Expect in AI in 2025

Unite.AI

In fact, as many as 63% of global business leaders admit their investment in AI was down to FOMO (fear of missing out), according to a recent study. AI developers willlikely provideinterfaces that allow stakeholders to interpret and challenge AI decisions, especially in critical sectors like finance, insurance, healthcare, and law.

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How the EU AI Act and Privacy Laws Impact Your AI Strategies (and Why You Should Be Concerned)

Unite.AI

Navigating this new, complex landscape is a legal obligation and a strategic necessity, and businesses using AI will have to reconcile their innovation ambitions with rigorous compliance requirements. GDPR's stringent data protection standards present several challenges for businesses using personal data in AI.

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

Unite.AI

Perhaps, then, the response from banks should be to arm themselves with even better tools, harnessing AI across financial crime prevention. Financial institutions are in fact starting to deploy AI in anti-financial crime (AFC) efforts – to monitor transactions, generate suspicious activity reports, automate fraud detection and more.

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AI Auditing: Ensuring Performance and Accuracy in Generative Models

Unite.AI

Automated tools can streamline this process, allowing real-time audits and timely interventions. Transparency and Explainability Enhancing transparency and explainability is essential. Tools like IBM's AI Fairness 360 provide comprehensive metrics and algorithms to detect and mitigate bias.

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Generative AI in the Healthcare Industry Needs a Dose of Explainability

Unite.AI

AI developers for highly regulated industries should therefore exercise control over data sources to limit potential mistakes. Generative AI-powered chatbots could help alleviate much of the workload and preserve overextended patient access teams.