In this article, learn about the importance of aligning AI initiatives with organizational goals and regulatory requirements to ensure responsible and effective AI deployment.
Learn how the Discovery Navigator team at Verisk pursued best practices for delivering responsible and safe AI products to customers.
High expectations for AI might not be realized without balancing value and risks via well-thought-out AI governance. Get started with great governance today by reviewing this playbook from Gartner (subscription required).
In the race to be at the forefront of artificial intelligence, companies try to stand out for their different offerings and direct business value. Governance might be the key to success.
From the EU AI Act to state-by-state regulations regarding AI usage in the US, rules are appearing almost as rapidly as AI itself. In this article from Fast Company, learn why it’s better to build transparency into AI projects now rather than wait.
Learn how Nayya prioritizes governing with responsible AI practices throughout their modeling systems by continuing to invest in making them safe, resilient, and trustworthy.
The National Association of Insurance Commissioners (NAIC) Membership voted to adopt the Model Bulletin on the Use of Artificial Intelligence Systems by Insurers during the 2023 Fall National Meeting.
The NAIC has enacted their model bulletin on the use of artificial intelligence systems by insurers. Public comment periods started in August 2023 and was adopted by both the H-committee and Executive committee in December 2023.
Europe on Friday reached a provisional deal on landmark European Union rules governing the use of artificial intelligence, otherwise known as the foundation of the EU AI Act.
Insurance should be the industry that leads all industries on demonstration of responsible and ethical AI governance,” he writes, noting that the measures insurers take internally aren’t just good business practices. They also position carriers to better evaluate—and backstop—the use of AI by other companies.
The need for Responsible AI is undisputed, but implementing it is challenging. Techopedia brought together the following leaders to discuss how and why organizations are adopting Responsible AI as a governance framework.
In the height of conversations about ChatGPT and generative AI, our colleagues at WilmerHale provided this comprehensive list of what businesses need to consider for their own business, for their customers, and within their partnerships.
It is becoming more urgent that automated decisions are fair and ethical. But how we address this task of assuring fair and ethical algorithmic decision-making remains a gray area. What does an ethical decision really mean?
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Watch this replay about the intersection of AI innovation and insurance regulation. Chris Aufenthie, who leads regulatory filings at AAIS, will lead the webinar. Anthony Habayeb, CEO of Monitaur and co-founder, and Mary Block, who is in charge of insurance regulation at the Vermont Department of Financial Regulation, will also be on the panel.
AI governance significantly shapes the future trajectory of businesses. In fact, the most forward-thinking companies in AI share a secret: they recognize that governance and risk management serve as catalysts for deploying more AI and supporting regulatory compliance needs for both current and future use cases.
AI bias is one of the most complex business challenges of our time. This presentation is built for data science leaders who want to strengthen their foundational understanding of model bias. It's also geared toward anyone who is passionate about AI ethics and AI governance. Sign-up to receive a replay of this online event!
Machine Learning model deployment can be a complicated undertaking, but it doesn’t have to be overly complex. This presentation is built for data scientists who want to strengthen their foundational understanding of model deployment as well as data science leaders who want to create paradigms for their teams to follow. It's also geared toward anyone passionate about Data Science, Python, and Responsible AI.
What's driving the need for Machine Learning Assurance as a framework for building trust and confidence in ML decisions.
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