Responsible AI

At Finch, we believe responsible AI is better AI. Developing responsibly is more important than ever.

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. 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.

Responsible AI is about meeting opacity and misunderstanding with transparency, comprehensibility and collaboration that, together, build trust.

Finch AI’s Framework for Responsible AI

Responsible AI and how to avoid misconceptions

Avoiding Misconceptions

A throughline across all of our work is responsibility. 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.

This is why we make it standard practice to educate customers on the breadth of use cases that can be solved using our AI alongside an assessment of potential risks and what can be done to mitigate them.

Trustworthy Source Data

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.

Responsible AI and the role of trustworthy source data
Responsible AI requires constant testing

Constant Testing

As a matter of practice, we train, evaluate, refine, and deploy our AI models – we then 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. 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. Our customers tell us responsible AI is evident in our models and our software products, and they appreciate our team’s careful attention to it. Which is the best possible outcome – because responsible AI is inherently better AI.

Mitigating Bias

Finch AI is strongly committed to mitigating bias and upholding responsible AI practices through a multi-faceted approach to bias detection and analysis. The company employs a range of sophisticated AI and machine learning techniques to identify and quantify potential biases or undue emphasis within data sources.

These techniques include:

  • Objective vs. Subjective Analysis: Finch AI’s algorithms can distinguish between objective statements of fact and subjective opinions or interpretations within text. This helps in isolating potentially biased viewpoints from more neutral information.
  • Entity-Level Sentiment Analysis: The system assigns sentiment scores to individual mentions of entities (e.g., people, organizations, or concepts). This granular approach allows for a nuanced understanding of how different subjects are portrayed.
  • Topic-Based Sentiment Tracking: By categorizing content into topic areas and analyzing the associated sentiment, Finch AI can identify potential biases in how various subjects are covered or discussed.
  • Temporal Bias Monitoring: The platform tracks these bias indicators over time, enabling the detection of shifts in emphasis or sentiment. This longitudinal analysis can reveal emerging biases or changes in narrative framing.

By implementing these advanced analytical methods, Finch AI not only identifies potential biases but also provides a framework for ongoing monitoring and assessment. This approach aligns with the company’s broader commitment to transparency and trust in AI systems, ensuring that users can make informed decisions based on a clear understanding of potential biases in their data sources.

Responsible AI mitigating bias

Protecting Data from Theft, Breach, or Misuse

Responsible AI protecting data from theft
AI systems are susceptible to cyberattacks, posing significant risks, especially for our clients who manage highly sensitive information. Malicious individuals with access to a network can exploit this access to compromise data and systems, potentially causing severe damage. FinchAI prioritizes data privacy and security through a comprehensive approach. The company employs continuous model training, evaluation, and refinement to ensure accuracy and trustworthiness of outputs. Rigorous quality control measures are implemented throughout the data and product lifecycles, including careful curation of training data, governance measures like guardrails and model monitoring, and model explainability where possible. FinchAI embeds responsible AI policies throughout the product development chain and conducts use case-specific testing to align outputs with customer expectations. Their solutions are deployed securely with proper governance, allowing organizations to leverage AI’s potential without compromising network, system, or data security, integrity, or confidentiality.

Protecting Your Cyber Ecosystem

FinchAI improves and upholds a strong cybersecurity infrastructure for clients by harnessing the power of AI while addressing potential vulnerabilities. The company employs a continuous cycle of training, evaluation, and refinement of AI models to ensure optimal accuracy and performance. FinchAI’s development process follows a two-week sprint cycle, prioritizing focus and performance while narrowly scoping use cases to prevent unexpected outcomes. The company builds world-class software using Agile SAFe and DevSecOps methodologies, embracing Continuous Integration/Continuous Delivery (CI/CD) to rapidly deliver business value. FinchAI’s system offers predictive insights, automated security measures, and adaptive solutions, creating a comprehensive defense against digital threats. By combining AI with cybersecurity expertise and ethical design principles, FinchAI provides clients with an ever-evolving, powerful arsenal to protect their digital assets, allowing them to navigate the digital landscape with confidence and peace of mind.
Responsible Ai protecting the data ecosystem

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