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Study the Data, But Eat the Cake—Put the Human Factor Forward

.orgSource

Researchers have identified over 180 types of cognitive bias. You’ll recognize these examples. Machine Learning, predictive modeling, and natural language processing are a few of the ways AI makes data more meaningful. These are examples of AI analytic tools that are available at multiple price points and levels of complexity.

Data 221
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Should you continue to host hybrid or virtual events in a post-pandemic world?

Association Analytics

For example, you may have wanted to ask questions like: “What if we hosted our event for free?”. For example, because the virtual events were free you probably had a higher attendee count than at your in-person events. Using predictive models – predictive modeling typically uses 3 -5 years of historical data.

Hosting 321
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Build a Mission-Worthy Team

.orgSource

Bell is researching what mix of personalities is most likely to work effectively together. In our models, we assume that astronauts are intelligent, that they’re experts in their technical areas, and that they have at least some teamwork skills. That’s a simplified example, but it’s the kind of idea that woke me up.”

Team 221
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Philanthropic collaboratives are finding ways to more effectively measure impact

Candid

In the United States, philanthropic collaboratives—entities that either pool or channel resources from multiple donors to nonprofits— collectively directed between $2 billion and $3 billion to a variety of grantees in 2021, and our research indicates that figure has grown since then. Tracking common outcome metrics across grantees.

Measure 52
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AI in Action: Deploying Responsible, Effective, and Trustworthy AI

sgEngage

Then, you provide an algorithm with examples in the form of sample data, and you allow the algorithm to identify the best way to get to that outcome. For example, let’s say you’re building a CRM to track relationships with your donors. Decisions When Building the Models When building a predictive AI model, you’ll have many questions.

Action 81
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How to Determine Your Needs When Choosing a New Fundraising CRM

sgEngage

If you are unclear about your needs, abilities, and opportunities, you will quickly find it difficult to vet vendors and evaluate CRMs, not to mention bringing internal stakeholders onboard or getting sign-off at key stages. For example, your new CRM could help you get started with using AI as part of your fundraising. #4

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If Racial Equity is Our North Star, How Can We Navigate Racial Bias in AI/LLMs?

John Kenyon

For nonprofits and grantmaking organizations committed to racial equity, using artificial intelligence (AI) and large language models (LLMs) raises important concerns. For example, there are fewer quality online materials representing the viewpoints of certain racial groups. However, they also offer intriguing potential benefits.