unitQ AI Statement of Principles andGovernanceNovember 30, 2023

The rapid spread of artificial intelligence (AI) throughout various industries makes it imperativefor companies to establish a clear set of principles guiding the development and application ofAI technologies. This document outlines unitQ's approach to ensure our generative AI use istransparent and trustworthy.

unitQ AI Statement of Principles
unitQ's commitment to using AI ethically and responsibly is grounded in the following principles:● Positive impact: Our AI solutions are designed to benefit users and society by helping
companies improve the overall quality of their user experience.
● Transparency: We believe in being open about how our AI models work, the data theyuse, and their decision-making processes.
● Fairness: We strive to ensure our AI models are equitable and do not perpetuate oramplify biases.
● Privacy and security: Protecting user data and ensuring the security of our systems isparamount. We adhere to the highest standards of data protection and cybersecurity.
● Accountability: We take responsibility for our AI's actions and have mechanisms toaddress issues or concerns.
● Continuous learning: We are committed to ongoing research and development to ensure
our AI remains at the forefront of ethical considerations and best practices.

unitQ AI Governance
unitQ's AI governance framework is built on transparency, accountability, and ethicalconsiderations. The framework comprises:● Regular audits: We conduct periodic internal and external audits to ensure our AI
practices comply with industry standards and regulations.
● User deidentification: unitQ redacts all personally identifiable information (PII) from user
feedback before storing data, ensuring that PII is never used in model development.
● Feedback mechanism: We actively seek feedback from both users and experts tocontinuously refine our governance practices.

Use of Generative AIunitQ uses generative AI in the following capacities with its unitQ GPT feature:● Summarization: unitQ summarizes user feedback along with relevant subtopics.
● Natural language Q&A: unitQ GPT enables users to ask questions using natural language
and receive relevant insights related to user pain points, feature requests, and general
feedback as consolidated in unitQ. unitQ GPT uses generative AI to:○ Extract relevant metadata from user questions to filter on a subset of the dataset.
For example, the question "What issues are trending for my Spanish languageusers?" automatically filters on Language = Spanish.

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○○Understand the meaning behind user questions. For example, unitQ GPT
understands that a user may reference terms like "issues," "bugs," or "problems"interchangeably.
Synthesize a response based on unitQ's existing categories and features or byscanning a subset of anonymized feedback data.

unitQ GPT uses self-hosted large language models (LLMs) and pre-trained models provided by
the OpenAI Enterprise API platform. OpenAI does not train on unitQ customer data with theOpenAI Enterprise API platform. To learn more about OpenAI and privacy, read
https://openai.com/enterprise-privacy.

Addressing Bias
unitQ recognizes the importance of addressing bias in AI systems. unitQ is taking the following
steps to reduce bias in unitQ GPT:
● Data collection: We limit user Q&A to questions about user feedback. By limiting
questions to user feedback, we capture a broad demographic range and allow users tounderstand diverse perspectives.
● Audits: We regularly audit models to improve accuracy and rectify potential biases. This
includes both automated testing and human evaluations.
● Feedback loop: We established a feedback mechanism where users can report biased
outputs, which we then use to improve the model.
● Transparency: We document our methodologies and share them with stakeholders uponrequest to ensure clarity in our approach to bias mitigation.
● Continuous training: Our models are continuously updated and refined to handle
potential biases better as they emerge.
● Source evaluations: We regularly review research on the bias of any underlying models
used by unitQ.

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