Responsible AI: What it Means and Why it Matters
Thanks to the rise in AI-assisted technology, and specifically AI-based language technologies like ChatGPT and others, data science teams are doubling down on their efforts to research, experiment, refine, and operationalize various inference models related to human language.
Finch AI is somewhat different. We began offering our first commercial text-analytics and data enrichment service in 2016. We developed significant intellectual property in natural language processing and in-memory computing technology and have leveraged both to support customers in the defense and national security space since that time.
We’ve also continually enhanced our capabilities so that, together with our customers, we can now confront emerging challenges in language analysis. As one example, when large language models (LLMs) began showing signs of technological singularity in 2022, Finch was uniquely positioned to build out our analytics platform to pair generative AI with entity-aware knowledge discovery techniques.
A throughline across all of our work is responsibility. And that’s important because there are many misconceptions and fundamental misunderstandings about what AI is, what it does, and whether or not it should be entrusted to assist humans in making critical decisions that impact our daily lives. Responsible AI is about meeting opacity and those misunderstandings with transparency, comprehensibility and collaboration that, together, build trust.
This is why we make it standard practice to, when necessary, educate customers about the breadth of use-cases that can be solved using our AI alongside an assessment of potential risk and what can be done to mitigate them.
We begin projects with an eye on the traceability and trustworthiness of our source data. We actively research and include features for explainable AI, model disgorgement, and watermarking of AI-generated content within our platform.
As a matter of practice, we train, evaluate, refine, and deploy our AI models – and then we keep training, keep evaluating and keep refining them so that we can ensure the utmost accuracy and performance. This attention to precision helps us measure and improve the veracity and trustworthiness of our outputs.
Additionally, because our teams develop on a two-week sprint cycle, we prioritize focus and performance. We narrowly scope use cases in order to prevent unexpected or unwanted outcomes. We mitigate the risk of model hallucinations by ensuring high quality standards in our data curation, and transparently showing evidence to back a model’s claims or assertions.
All of these efforts are complemented by executing rigorous quality control at all stages of the data and product lifecycles, including the curation of our training data; enforcing governance measures such as guardrails and model monitoring; providing model explainability where possible; and embedding our policies in responsible AI both upstream and downstream in the product development chain. We employ use case-specific testing that ensures that our outputs align with specific customer domain expectations.
Customers tell us responsible AI is evident in our models and our software products, and they appreciate our team’s careful attention to it. National security missions, in particular, require responsibility from start to finish and from every team member that contributes, including Finch AI.
With the rise of generative AI models, the range of potential model behaviors has grown exponentially. So has the subjectivity of erroneous or problematic outputs, such as false information or toxic language, and even adversarial attacks. Our commitment to trust and transparency go hand-in-hand with our ability to proactively imagine, experimentally test, and engineer guardrails against unwanted or harmful behaviors.
At Finch, we are steadfast in our belief that responsible AI makes for better AI, and as every industry, every agency and every sector of the economy continues to benefit from AI’s enormous and transformative potential, developing responsibly will become more important than ever.