How Machine Learning Can Help Grants Management

Machine Learning and Artificial Intelligence are rapidly enhancing how we work, but what does this mean for the granting world? Below are 3 different examples of how it applies.

Machine Learning might sound like a concept you'd read in a sci-fi novel taking place in the distant future, but it's something very present in our daily lives. And you've definitely interacted with some form of it, probably even today. From your Recommended movie section on Netflix, to self-driving cars, Machine Learning is quickly becoming a tool that enables projects and businesses make more effective and impactful decisions in their processes and strategies in effort to connect with their audiences in the most appropriate way. When it comes to the granting world, Machine Learning can create a major shift in the landscape as we know it. Machine Learning can enable any grants foundation to look at their past giving to assist in achieving better giving outcomes in the future.

What is Machine Learning?

Machine Learning is a form of technology that autonomously learns from collected data through algorithms, discovers patterns, and makes predicted decisions for the future. Stanford University succinctly defines Machine Learning as "the science of getting computers to act without being explicitly programmed.” For the grant management world, Machine Learning technology can match the scale of a growing organization’s complex needs and helps make informed, data-based decisions to allow you to focus on all the important elements of grants management. When approaching the use of Machine Learning, it’s helpful to map out a set of objectives that you want to learn from your data; having this information will provide greater insight and impact of using Machine Learning as a resource.

How Machine Learning Works (A Brief Overview)

Without getting bogged down in granular details, the overall concept of all Machine Learning algorithms are broken down into three components that all run concurrently:
  • Representation: This component is the "language" and form in how the Machine Learning algorithm will speak and display your data-based information and predictions. Examples include decision outcomes (ie. yes/no), sets of rules, instances (i.e. sequence of events), and graphical models (i.e. charts).
  • Evaluation: This component is how the Machine Learning algorithm assesses, and reads the data collected in your system. Examples of evaluation include consistency of accuracy, likelihood of a prediction happening in the future.
  • Optimization: This component is an extension of how Machine Learning algorithm predicts the ‘best’ option from the evaluated data. Examples include choosing the most likely and best outcome to predict in the future based on findings in your processed data.
From these three elements, Machine Learning technology enables a more efficient workflow while cutting down on errors in data analyzation.
Dr. Pedro Domingos, winner of the SIGKDD Innovation AwardFulbright Scholarship recipient, and Professor of Computer Science & Engineering at the University of Washington states that, “Machine learning can’t get something from nothing…what it does is get more from less.” meaning that Machine Learning is a tool to make your data become more of a resource in your processes, allowing for more time spent on dynamic human relationships within the grant world.

Machine Learning and Data Privacy

Data privacy is critically important, especially as technology processes countless forms of personal data every day. This is why Machine Learning understandably may cause anxiety in how it inherently processes data. This is where transparency and accountability practices come in. To ease the stress of using Machine Learning technology, organizations (including SmartSimple) can analyze data that is anonymous, where the data can no longer be attributed to a specific subject without the use of additional information, which is kept separately.
Other accountability measures and practices with Machine Learning technology include legislations like Europe’s General Data Protection Regulation (GDPR) which advocates for data subjects and the right to access their data and objection to the processing of their data. GDPR lays out specific mandates including “the right to explanation.” It’s stated in Article 22 that “the data subject shall have the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her.”
GDPR is just one set of principles that apply to the use of data, including Canada’s Anti-Spam Legislation (CASL) as another example.

Now that we've established the Machine Learning process, here are three real-world scenarios of how it can help with Grants Management...

Grant Funding Impact

Challenge: One of the biggest priorities for organizations is not only ensuring their grant dollars are making the most impact, but also engaging with donors in an authentic way.
Solution: By pulling information from grant reports and analyzing their funding impact, Machine Learning algorithms can pinpoint specific community needs and predict where funding dollars will have the most impact, making for robust, high value reporting for donor engagement news.

Data Breach Security

Challenge: At every point in the granting process, making sure the personal data of all applicants is kept secure and safe from data hacking and privacy breaches.

Solution: Machine Learning algorithms can detect anomalies in user behavior, which uses statistics  building a historical baseline within your data. The system alerts the admin on deviations from established baselines that can predict a potential attack.

Review Process

Challenge: The review process evaluating applications using a combination of quantitative and qualitative criteria can be an exhaustive one. Reviewers spend too many hours assessing the relative experience of the applicant or caliber of the organization.
Solution: Implementing Machine Learning algorithms can be used to analyze the content of any grant application and establish a baseline view of the quantitative criteria such as the applicant’s years of experience or specific characteristics of an organization. The algorithms can help predict the impact per dollar invested the funded project will generate.
Machine Learning is a valuable resource that can be used to glean a deeper understanding of not only the impact of our work, but how we work in general. At SmartSimple, we continue to work on making technology more comprehensive and accessible for organizations to thrive in their work’s mission, and Machine Learning is a powerful tool to help along the way.


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