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Art in branding provided by The Arc Westchester’s Gallery 265, where artists with developmental disabilities demonstrate their talents in the community. Artist is Robert Vittorini.

Thursday Keynote Session: The Smart Nonprofit: How to Use Artificial Intelligence to Make Your Organization Smart and Your Staff Smarter with Allison Fine & Beth Kanter

Thursday AI Poll

Ask the Tech Impact Experts Panel

Facilitated by NYCON and supported by MVP Health Care, this special panel features national nonprofit Tech Impact’s experts answering specific nonprofit questions related to technology issues like cyber security, digital fundraising and donor management systems.

Moderated by Andrew Marietta, New York Council of Nonprofits

Panelists from Tech Impact

Chris Baranec, Infrastructure and Security Consultant

Kimberly Sanberg, Digital fundraising instructor

Jahzeer Terrell, IT Security Associate

Learning Opportunities Related to Nonprofits and Artificial Intelligence

Read

Applying Artificial Intelligence for Social Good

by Michael Chui, Martin Harrysson, James Manyika, Roger Roberts, Rita Chung, Peter Nel, and Ashley van Heteren.

Points to Highlight

       “Artificial intelligence (AI) has the potential to help tackle some of the world’s most challenging social problems. To analyze potential applications for social good, we compiled a library of about 160 AI social-impact use cases. They suggest that existing capabilities could contribute to tackling cases across all 17 of the UN’s sustainable-development goals, potentially helping hundreds of millions of people in both advanced and emerging countries.”

       “Addressing challenges to equality, inclusion, and self-determination (such as reducing or eliminating bias based on race, sexual orientation, religion, citizenship, and disabilities) are issues in this domain. One use case, based on work by Affectiva, which was spun out of the MIT Media Lab, and Autism Glass, a Stanford research project, involves using AI to automate the recognition of emotions and to provide social cues to help individuals along the autism spectrum interact in social environments”.

       “Some of these use cases consist of tasks a human being could potentially accomplish on an individual level, but the required number of instances is so large that it exceeds human capacity (for example, finding flooded or unusable roads across a large area after a hurricane). In other cases, an AI system can be more accurate than humans, often by processing more information (for example, the early identification of plant diseases to prevent infection of the entire crop).”

       “Data accessibility remains a significant challenge. Resolving it will require a willingness, by both private- and public-sector organizations, to make data available. Much of the data essential or useful for social-good applications are in private hands or in public institutions that might not be willing to share their data. These data owners include telecommunications and satellite companies; social-media platforms; financial institutions (for details such as credit histories); hospitals, doctors, and other health providers (medical information); and governments (including tax information for private individuals).”

●“As with any technology deployment for social good, the scaling up and successful application of AI will depend on the willingness of a large group of stakeholders—including collectors and generators of data, as well as governments and NGOs—to engage. These are still the early days of AI’s deployment for social good, and considerable progress will be needed before the vast potential becomes a reality. Public- and private-sector players all have a role to play.”

Read

What Nonprofits Stand to Gain from Artificial Intelligence
by Dr.Lobna Karoui

 

Points to Highlight

       Artificial intelligence is not a new technology. In 1956, it was defined by John McCarthy, a Stanford professor for more than three decades, as the “science and engineering of making intelligent machines.” AI is when machines learn from human intelligence in order to automate repetitive tasks, augment cognitive capacities and facilitate the decision-making process.

       “Organizations have a huge number of repetitive tasks that are time-consuming and not necessarily involving high-cognitive capacities. For these tasks, two solutions can be deployed to automate the repetitive tasks, reduce costs and risks of human inputting errors. Machine learning extracts patterns to define repetitive tasks and automate them. Once tasks are known, robotic process automation (RPA) tools automate the process.”

       “Understanding your supporters’ interests helps you proposing the right program and ask for the appropriate donation amount. Supporters are communicating via social platforms and their interests can change. This year of pandemic presents many examples of flexibility even for supporters. Learning from internal and external data by developing Machine learning and deep learning algorithms extract valuable insights about existing supporters but also potential new supporters.”

      “When it comes to user data, it’s very important to care about the AI ethics principles to avoid bias, discrimination and other forms of injustice. AI algorithms predict from the existing data and extract patterns that reflect defined processes.”

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