智能向善：利用人工智能加速社会影响进程

It seems as if AI mania is all around us. Mainstream media is discussing its many uses. Congress is debating its merits. Companies are announcing significant investments and touting its benefits, while others are more closely evaluating its ethics.

We’re at an inflection point in AI’s history, but AI isn’t actually new. It’s been around for decades in forms like machine learning and natural language processing. What’s changing the game today is powerful advances in generative AI trained on massive large language models.

While some hesitation and a healthy dose of skepticism around AI are probably smart, there is an application for AI far from the headlines, that has the potential to drive seismic shifts in not just how we work, but how we positively impact the community we live in and communities around the world. Already AI is being used to find solutions to complex challenges—whether that be using large volumes of weather data to help farmers in Kenya understand weather patterns and better manage their livestock, or using satellite images to stop deforestation before it begins. AI is also being used to improve health care and patient outcomes through advances in precision medicine, genetics-based solutions, and drug discovery and development.

We believe AI, when built and deployed responsibly, also has the potential to transform how purpose-driven companies think about social impact, helping them amplify and accelerate the good they can accomplish.

So, how can we maximize its capacity for impact while being a leading voice for its ethical use through minimizing bias, ensuring safe and factual responses, protecting privacy, and driving transparency?

That’s the big question we’ve been asking ourselves lately, and while we don’t have all the answers, we think we’re on to something important with our Intelligence for Good strategy.

Intelligence for Good

What is Intelligence for Good? Put simply, it’s our strategy for AI for purpose-driven companies and the social impact sector that’s accessible, powerful, and responsible. One that’s centered on the concept of trust and grounded in safety, fairness, inclusiveness, and reliability. According to our internal research, when it comes to AI, social impact professionals are highly concerned with bias, discrimination, and ethical decision-making. So are we.

At its heart, Intelligence for Good empowers purpose-driven companies and organizations to supercharge their social impact efforts with a set of complete, ready-made solutions that address their specific challenges by making both daily operations and longer-term strategic planning more efficient and effective. It will also help level the playing field, making ethical AI accessible to more companies and organizations of all sizes.

To get there, we’re creating powerful systems of intelligence and building them into tools that make it easy for social impact teams to simply their workflows, integrate data that currently resides in multiple locations, and be far more efficient with day-to-day tasks, even with limited resources. Then, we want to go even further, enabling companies to gather game-changing descriptive, predictive, and prescriptive insights across their entire social impact effort.

AI beyond the hype: Real-world solutions

While it’s clear that AI may be compelling for assisting students as they learn and improving the quality of photos, what about supporting social impact? What would a useful AI solution look like and what would it do, exactly?

In speaking with social impact professionals, our research showed that more than 70% said it was challenging to collect, create, and provide data across the range of social-impact-related activities they work with. Most also said they rely on a jumbled mix of spreadsheets, email, text documents, and reports to manage their impact reporting, making the process highly inefficient.

When we thought about an Intelligence for Good solution that could address these issues, our priority was translating intelligence into real action. It’s why we created Blackbaud Impact Edge, a new AI-powered social-impact-reporting and storytelling solution for teams of all sizes. Impact Edge helps create a compelling picture of a company or organization’s entire social impact, seamlessly merging, organizing, and comparing social impact data from across the organization and trusted external sources. With responsible generative AI, machine learning, and analytics, it then identifies insights—and can create draft content—to tell a powerful story of how a company supports its communities.

The larger idea is that AI can help resource-constrained teams feel like they have “superpowers,” or at least a lot more help, as they field daily data requests from stakeholders across their organization, freeing up time to work on longer-term strategy. With more intelligent tools, social impact professionals can tell powerful impact stories more easily and effectively while automating day-to-day tasks and responsibilities. This is already happening today, with organizations and companies using AI to help manage fundraising, recommend more personalized methods of donor engagement, connect with donors and drive them to action more easily, and even write better grant submissions.

Advancing social good, responsibly

As AI becomes more integrated in our daily lives, it’s essential that purpose-driven companies maximize their capacity to help do good in the world, while minimizing their risks. We’re working to make that happen, and we’re excited for the potential of this transformative technology, both today and in the years ahead.

While we don’t have all the answers when it comes to using AI for good, we’re going to continue exploring, iterating, and learning. Because at the heart of it, we believe that purpose-driven companies and the entire social impact sector can do more and help more people by harnessing the rapidly expanding potential of artificial intelligence—together, responsibly.

 Tom Davidson is the founder and CEO of EVERFI from Blackbaud, a founding partner of Fortune’s Impact Initiative.