‍AI for Social Good.

Artificial intelligence (AI) has emerged or at least been pursued as a powerful force for positive change in the rapidly evolving social impact landscape. New research reports that it has the potential to improve some of the sustainable goals such as Good Health and Well-Being (SDG 3), Quality Education (SDG 4), Climate Action (SDG 13), Affordable and Clean Energy (SDG 7), and Sustainable Cities and Communities (SDG 11). It also has risks of inaccurate outputs, embedded biases, and the potential for large-scale misinformation. Many nonprofits working hard to bring societal change have more questions than answers. AI can improve productivity for some tasks, but what can it bring to the continuous outcome learning?

The article will assess how AI can reshape the world of measuring and managing social impact by leveling the playing field of technology and data.

Where can AI Help?

• Shortening the Outcome learning cycle: Many organizations begin their impact measurement journey by creating detailed ToC or logic models. While this approach seems logical, it can often delay the transition to data-driven decision-making. The Theory of Change is not always STEP #1. Our customers frequently come to us after spending months or even years building these frameworks with expensive consultants, only to find they need to be more practical and actionable. AI can help frame nimble ToC and make getting actionable insights faster and more straightforward. This agile process helps organizations understand where to focus their efforts and shorten the cycle from months to minutes

• Accelerating the inclusion of digitization in the social sector to bridge the gap in tech skills. One of AI's key benefits in the social sector is its potential to promote digital inclusion. By making advanced analytics and insights accessible to organizations of all sizes

• Identifying patterns and trends that humans might miss: You can analyze vast amounts of data quickly and accurately, uncovering insights that might go unnoticed.

• Scaling social good with efficiency: AI enables organizations to automate processes and optimize resources, allowing them to scale their impact.

Where can AI Fall Short?

AI needs human partnership

While AI offers powerful capabilities, it's crucial to understand that it doesn't operate in isolation. Lived experience and domain expertise remain the cornerstone of effective impact measurement. Here's why:

1. Context is the king: Human issues are complex and context-specific. AI needs human guidance to navigate these nuances.
2. Asking the right questions: Your expertise is vital in effectively framing the questions that AI can answer.
3. Interpreting results: AI can crunch numbers, but humans bring critical thinking to interpret and apply the insights.

We believe in putting humans at the center and using AI as a powerful tool to enhance, not replace, human intelligence in impact measurement.

Impact measurement requires a human touch and contextual understanding, not half-baked solutions that don't scale.

AI Needs Mindset

We saw that AI could support an organization by creating a framework for interpreting results. So, what is stopping the social sector from taking advantage of new AI-driven technology?

For a regular for-profit company, continuous learning is synonymous with business intelligence. It should be the same for social organizations if we want to see the scale of change. Despite this, many social organizations do not invest in this kind of business intelligence. Their reasons include:

• Lack of money: Many social organizations operate on tight budgets, challenging allocating funds for new technologies.
• Lack of technically skilled people: Passion-driven organizations often need more expertise to implement and manage the latest technology and AI solutions, which can be a significant barrier.
• There is no time to develop new skills: Social organizations are often stretched thin, and staff need more time to learn new skills.
• It is hard to measure the impact: Impact measurement can be complex and daunting, especially without the right tools.
• I cannot work with data: Data management and analysis can be overwhelming for organizations without a background.

All the problems listed here are real, but they cannot be an excuse for organizations not to attempt to measure and manage their impact. We can solve the issues collaboratively by investing in the organizations to increase their data capacity, positive mindset, and new empowering AI-driven tech solutions.

Read: Beyond ChatGPT for Text Analysis

AI Needs Continuous Impact Learning

One can use free or paid open AI solutions, but they only address some of the nonprofits' pain points. Impact data is not a one-time need but a continuous learning process. You can use as a helping hand in your impact learning process.

Theory of Change development: Use AI to generate and refine impact frameworks quickly. Built in AI Assistant can guide you through creating and iterating your impact frameworks, while maintaining human oversight.

Survey design: Create more effective, targeted surveys. Surveys are a critical tool in impact measurement, and AI can undoubtedly assist in their creation. However, it's vital to approach AI-generated surveys with caution. A broad prompt like "Create a survey for a training program" might yield a generic survey that misses crucial nuances of your specific context. Combines AI assistance with human expertise to ensure your surveys are:

• Tailored to your specific objectives
• Designed with proper methodological principles
• Capable of collecting unbiased, actionable feedback‍

Data collection: One area where AI truly excels is in simulating data collection and testing results before engaging with real stakeholders. This capability is crucial because data collection is often resource-intensive. New age solution can,

• Help you design surveys and create data validation early in the process.
• Track potential insights before full-scale implementation.
• Let you refine your approach based on simulated outcomes.
• Ensure you're collecting the correct data for meaningful insights.

Analysis: Utilize advanced AI text analytics and predictive modeling for deeper insights. While many tools offer AI functionalities for data analysis, they often need to catch up in capturing the nuances of impact measurement.

Text analysis is one of AI's most powerful applications in impact measurement. You can now identify key themes and sentiments in large volumes of text data, detect emerging trends and issues that human analysts might miss, and provide a nuanced understanding of stakeholder perspectives. You can conduct

• Sentiment analysis: Gauge the overall tone and emotional content of stakeholder feedback.
• Topic modeling: Automatically categorize text data into relevant themes and subjects.
• Named entity recognition: Identify and extract critical information like names, organizations, and locations from text data.

Traditional impact measurement often relies heavily on quantitative data. However, some of the most valuable insights come from qualitative sources - stakeholder feedback, open-ended survey responses, and project reports. This is where AI text analysis shines. Impact measurement demands,

• Longitudinal studies
• Deep contextual qualitative analysis
• Complex impact scenarios

Reporting: Transform data into compelling, AI-enhanced impact stories to increase trust with your stakeholders and donors/funders.

AI Needs to be Responsible

While AI's potential for social good is immense, it's crucial to approach its use responsibly. Look for ethical application of AI in impact measurement. Principles of Responsible AI in Impact Measurement.

1. Transparency: We believe in making our AI processes as transparent as possible, allowing users to understand how insights are generated.
2. Fairness: Our AI systems are designed to minimize bias and ensure equitable analysis across diverse populations.
3. Privacy: We prioritize data protection and privacy in all our AI-powered tools.
4. Human-Centered: While AI enhances our capabilities, we always keep humans at the center of the impact measurement process.

Conclusion:

Integrating AI in impact measurement is not just a trend - it's a fundamental shift in understanding and communicating social impact. Our approach to embracing AI for social good is to empower organizations. So they can,

• Gain deeper insights from their data.
• Make more informed, strategic decisions.
• Communicate their impact more effectively to stakeholders and funders.