Thank you for Subscribing to CIO Applications Weekly Brief
Thank you for Subscribing to CIO Applications Weekly Brief
In an exclusive interview with CIO Applications, Dr. Vlad Eidelman, Chief Scientist and the Head of AI research at FiscalNote, shared his insight on how FiscalNote has been able to enable a cultural change in the policymaking world with its AI and Machine Learning capabilities. He also sheds light on FiscalNote’s AI-enabled data-driven solutions, which have aided thousands of organizations, from small nonprofits and government agencies to multinational corporations, in managing federal and regulatory policy issues and risks.
FiscalNote is recognized as the premier hub for domestic and global government information today. Can you please elaborate on the AI-driven transformation that FiscalNote has brought into the legislative and regulatory landscape?
Every organization is significantly affected by the legislative and regulatory policies formulated by local, state, federal, and international governments. Traditionally, organizations have depended on policy consulting or analysis firms for in-depth, long-form research on policies and regulations. While they certainly offered a lot of value in the past, the question now is whether that is sufficient in today’s complex, data-driven era?
Notably, in the past decade, finance, sales, and marketing workflows have gone digital, but similar adoption has been much slower in the departments surrounding them, namely legal and regulatory. And that’s where FiscalNote comes in with its data-enabled and AI-augmented capabilities, facilitating a data-driven culture to complement the existing work in regulatory and legal departments of organizations.
Data related to policies and regulations are available from numerous sources today – disparate policymaking sources of local, state, and federal governments, including regulatory, legislative, and other departments; courts; social media, news, and the list goes on. Our AI-enabled tools give a company’s regulatory and legal department access to information from these thousands of sources. They can analyze these insights and swiftly arrive at informed decisions by identifying trends, patterns, opportunities, and risks. For instance, they can spot if any legislation or regulation is being reintroduced or recognize emerging trends across different localities and policymaking issues. Similarly, they can understand the relationship between the different policymakers and determine how aligned legislators are on various issues.
What we are doing here is to put policy analysis one step ahead of where it traditionally had been while enabling people to work together collaboratively and securely
How exactly can organizations translate massive amounts of information to their advantage? Can you please shed some light on the process for our readers?
We leverage AI at multiple stages to transform the stream of unstructured data and present it to augment our clients’ capabilities. The data extracted from diverse sources are generally unstructured text documents with dense information and very little metadata. Leaning on our Natural Language Processing algorithms, we perform various automated analyses to connect disparate sources of data together, including categorizing topics, identifying similar language, extracting keyphrases, people and organization entities, and determining stance from those documents to build a policy knowledge graph composed primarily of ingested policy documents, stakeholders, and organizations. The relationships between them in the graph are computed automatically based on the analyses.
Once we have ingested the external data and analyzed it, our clients can collaborate on top of the data using the workflow elements in our tools while defining their perspectives and viewpoints. We are thus able to create a knowledge center that skillfully combines the external data with the client’s internal knowledge and operations. This knowledge center brings everything related to our clients’ needs to discover, track, plan, and measure success on regulatory initiatives into one place, thereby providing transparency to the whole process within different departments and stakeholders of an organization. Besides significantly reducing arduous manual data collection and analysis, the center also provides a wide range of features, such as real-time reporting and customized analysis, which enable our clients to take decisive actions in the real world.
FiscalNote boasts of an exceptionally diverse clientele. How does each of them stand to benefit?
Our clients leverage us in diverse ways. For example, many large organizations use us as a single source of truth. For example, trade associations have the ability to collate their internal data with the vast external data that is available on the platform and push this updated information to their members with ease. Meanwhile, there are also multinational corporations that use our software to ensure that their teams located in various parts of the world are on the same page regarding policy messaging. We remove their communication clutter.
It is imperative to mention at this point that what FiscalNote provides to its clients is augmented intelligence. We are not trying to automate decision-making, but rather offering actionable information and the right tools that enhance our client’s strategies. What we are doing here is to put policy analysis one step ahead of where it traditionally had been while enabling people to work together collaboratively and securely. In that regard, our software is SOC 2 Type 2 compliant, and we have an excellent customer service team to support client needs.
As an expert in the domain, where do you see the AI space heading? And how FiscalNote will transcend the benchmark that it has already set in terms of its AI capabilities?
There are a lot of horizontal and vertical applications of AI coming onto the market. At the same time, solutions to some of the challenges with previous implementations of AI systems are gaining prominence—such as human-in-the-loop (HITL) ML. Besides, there is a lot more emphasis today on how to make AI user-friendly. For instance, there is currently a surge in no-code AI platforms and a desire for model explainability and bias detection in the coming years.
From FiscalNote’s future standpoint, we are looking forward to enhancing the enterprise workflows that would enable our clients to collaborate even more. We plan to extend the AI capabilities as well so that FiscalNote can serve a lot more suggestions, recommendations, and integration points to connect new datasets and content. And in addition to that, we’re enhancing the API offerings as many of our clients increasingly want to integrate the external data and the derived information from the AI models into their internal systems.