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  • Writer's pictureMichelle Plyem Kocin

2024 TRENDS - AI for Nonprofits 101

What exactly is artificial intelligence (AI) for Nonprofits and how can your organization use it safely?

We've been reviewing the most important nonprofit fundraising trends for 2024 and in this article we'll explore Artificial Intelligence (AI) for nonprofits. If you haven't already, check out the trends that we expect to dominate 2024 and the next article in this series: AI for Nonprofits 102 - A Lesson in Vocabulary.

Artificial Intelligence 101: What exactly is AI and how are nonprofits using it?

Artificial Intelligence (AI) is a rather broad field that is rapidly expanding and taking many different forms. For now we want to drill down to the make basic question: what does AI for nonprofits mean?

AI broadly refers to any process where machines attempt to mimic human intelligence to perform tasks such as problem-solving, speech recognition, learning, and decision-making. As with most new technology, it's both exciting and scary. Learning basic terminology and concepts help to make it more manageable. Here are a few key concepts:

  1. Artificial Intelligence (AI): A broad field of computer science that focuses on creating machines capable of performing tasks that typically require human intelligence, such as learning, reasoning, problem-solving, perception, and language understanding.

  2. Machine Learning (ML): Machine learning involves systems that can learn and improve from experience without explicit programming. This is particularly valuable for data analysis and pattern recognition.

  3. AI's relationship with data: AI couldn't exist without data. Different AI tools and platforms use massive data sets, primarily from scraping public website, to train their chatbots and allow users to input "natural language" (words and phrases rather than formulas or code) to prompt responses and return results that sound like they're more human than computer.

  4. Algorithms: These are step-by-step procedures or formulas used by AI systems to perform specific tasks.

How have nonprofits started using AI in the last few years?

The for-profit and nonprofit industries are using AI in more ways than we could cover in a reasonably sized blog post, so let's focus on key AI tools that help to optimize fundraising campaigns, study donor data, and automate time-consuming tasks.

  1. Predictive analytics for fundraising: Predictive analytics use data recognize trends, identify potential donors, and predict donor behavior. AI algorithms analyze historical data to identify patterns and make predictions about which individuals or groups were more likely to contribute.

  2. Donor segmentation: AI tools can analyze donor data to create more targeted and personalized fundraising campaigns using donor segmentation. By understanding the preferences and behaviors of different donor segments, organizations can tailor their messaging and appeals to individual segments and optimize fundraising campaigns.

  3. Personalized email campaigns: One specific way custom messaging can be used is in personalized email campaigns. This allows for email content to be based on donor preferences and past interactions. This can help to improve open rates, click-through rates, and increase overall email engagement.

  4. Automation for operations: AI-driven automation can help to simplify and streamline various operational tasks. This includes automating administrative processes, managing donor databases, and handling routine communication with supporters, allowing staff to focus on more on their most important task: building relationships with their constituents.

  5. Fraud detection and security: AI can be used to enhance security measures, especially in online transactions. Nonprofits can set up systems that leverage AI to and prevent fraudulent activities, ensuring the integrity of their fundraising efforts.

As we've covered some of the exciting ways AI can be used, let's take a look at some of the key safety and ethical concepts nonprofits should consider.

Key considerations for nonprofits using AI

  1. AI ethics: The study of ethical principles and guidelines to ensure responsible and humane development and use of AI technologies.

  2. Algorithm bias and fairness: As leading AI systems scrape the web for data to train their models in natural language, there are valid concerns over the potential for biases in AI systems. Some critics have pointed to the use of Reddit and X (formerly Twitter) for data sets. As these sites host groups and threads where users can post biased and inappropriate there is valid concern that this could lead to unfair or discriminatory outcomes, particularly in areas like hiring, lending, and law enforcement.

  3. Ethical AI: The consideration of moral and societal implications in the development and deployment of AI systems, including issues related to privacy, transparency, and accountability. Most models scrap public content like images and text posted online but there are no regulations on how to properly credit copywritten and trademarked content. There also isn't consensus on whether AI models can use intellectual property without getting the owner's permission.

  4. Explainability and interpretability: The ability to understand and interpret the decisions and actions of AI systems, particularly important in applications where trust and accountability are critical. If you don't understand how the model works, you can't explain it to your staff, board of directors, or donors.

Since artificial intelligence is relatively new, it will take some time to work out these ethical issues. There will likely be government regulation and lawsuits over fair use of intellectual property. The overall AI landscape is sort of like the Wild West. Until there are structures and rules in place, nonprofits should be critical of any AI systems or tools they use and ensure they understand the terms of use and how any of your donors data will be used.

How can nonprofits safely start using AI technology?

  1. Understand the purpose: Clearly define the purpose of integrating AI into your nonprofit's operations. Identify specific challenges you aim to address and the desired outcomes.

  2. Prioritize ethical practices: Ensure you understand how AI tools are trained and partner with software providers you trust to employ ethical data use and practices.

  3. Invest in data security: As many nonprofits typically have less to invest on data security, this is another area where partnering with a company you trust can help to ensure your donor's data is secure. Using tools backed by large companies like Microsoft mean you benefit from the massive investments these companies have made in security.

  4. Educate your team: Provide comprehensive training to your team on AI concepts, ethical considerations, and data security protocols. This empowers staff to make informed decisions and minimizes the risk of unintended consequences.

  5. Create an organization-wide policy for AI usage: One of the biggest dangers is the improper use of donor data. Ensure your team understands which tools are safe to use and sanctioned by your organization. Tools that are free and open to the entire web should never be used to analyze donor data. Donor trust is vital to your success, so you want to make sure you're protecting both your organization and your supporters.

  6. Collaborate with experts: Again, partnering with organizations that specialize in AI solutions is key. Collaborative efforts can help navigate complex technological landscapes and ensure the responsible use of AI.

We've barely scratched the surface of the many terms, ideas, and considerations nonprofits need to understand before using artificial intelligence tools. In upcoming posts we'll delve more deeply into data security, ethical and safe use of AI, and tips on how to choose the right software to leverage the benefits of AI tools.

If you haven't already, make sure you check out our other posts in this series: Top 7 Charitable Giving Trends to Watch in 2024, AI for Nonprofits 102 - A Lesson in Vocabulary, and Cloud-Based and SaaS Software.

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