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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. They may associate words like “fantastic” and “great” more with white-sounding names versus Black-sounding names. Define clear “red line” policies.

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Preparing Your Association for AI

Association Analytics

The most popular model today is called a Large Language Model (LLM) , which is trained on massive text datasets. LLMs are meant to produce conversational human language responses. San Diego, California Orlando, Florida Seattle, Washington Let’s say these all sound appealing, but you want to get a bit more granular. The results?

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Top Ten Data Challenges (And Solutions) for Associations

Association Analytics

Sound familiar? Solution : Solve this challenge by creating a data capture policy. It can be a challenge to get everyone on board with one language when things have been done differently in the past. Solution : Restart your data governance efforts by ensuring you include the 5 Ps: people, process, priorities, policy, and plan.

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Forum One Wins 5 dotCOMM Awards!

Forum One

Within the organization, the AARP Public Policy Institute maintains the AARP Livability Index , a key tool in their mission to promote the development of sound, creative policies to address common needs for economic security, health care, and quality of life.

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Words to Avoid—2022 Edition

Candid

One final bit of context: we recognize that language is subject to regional differences as well, meaning that even though we are sticking to English here, there are still undoubtedly many English words that we might have missed or not addressed. Many organizations that use this language exist because there is something that can be done.

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Nonprofit Guide to US Digital Privacy Laws: CCPA, SHIELD

Whole Whale

There are two new major online privacy policy changes coming in 2020, the CCPA in California and SHIELD act in New York. These acts mimic the standard set by the European Union with GDPR and many of the policies will sound familiar. What is weird is that these acts begin to treat U.S.

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Need a copy editor? Look in the mirror

M+R

Eliminate, or clarify, any jargon or Official Language that may be opaque to readers. Most of the time, you don’t need a policy briefing in order to make sense of the call to action. Q: How does it sound? Some other things to listen for or look for: Repetitive language. Wonky language and jargon. How does it look?