Responsible AI Policy

Effective date: May 27, 2025

1. Our Commitment

At VaryOn Works, we build measurement infrastructure for the frontiers of artificial intelligence. We believe that the ability to measure AI's impact on society is itself a responsibility - one that demands rigor, honesty, and care. This policy outlines the principles that guide how we design our frameworks, conduct our research, and operate as an organization.

2. Core Principles

Fairness and Equity

Our measurement frameworks are designed to produce results that are fair, unbiased, and representative. We actively work to identify and mitigate sources of bias in our scoring methodologies, datasets, and evaluation criteria. We recognize that measurement itself can reinforce or challenge existing inequities, and we take that influence seriously.

Transparency

We are committed to making our methodologies understandable and our processes visible. When we publish scores, assessments, or research findings, we provide clear explanations of how results were derived, what data was used, and what limitations exist. We do not use opaque or unexplainable methods where interpretable alternatives are available.

Accountability

We take ownership of the impact our frameworks and research have on the organizations and communities that use them. We maintain clear lines of responsibility for the design, deployment, and outcomes of our work. When our tools produce unexpected or harmful results, we investigate, disclose, and correct.

Scientific Rigor

Our frameworks - including VaryOn Amplitude and VaryOn Meridian - are grounded in research, not speculation. We subject our methodologies to peer scrutiny, document our assumptions, and distinguish clearly between established findings and emerging hypotheses. We do not overstate the capabilities or accuracy of our tools.

Privacy and Data Protection

We collect and process only the data necessary for our research and services. We design our frameworks to minimize the need for sensitive or personally identifiable information. When data is required, we handle it in accordance with our Privacy Policy and applicable data protection laws.

3. Framework Design Standards

Every measurement framework we develop adheres to the following standards:

• Validity - Our frameworks measure what they claim to measure. We validate our scoring models against real-world outcomes and revise them when evidence warrants.
• Reliability - Our measurements produce consistent results under consistent conditions. We test for reproducibility across different contexts and populations.
• Inclusivity - Our frameworks are designed to be applicable across diverse organizations, industries, and geographies. We actively seek input from underrepresented perspectives during development.
• Proportionality - The scope and intrusiveness of our measurement approaches are proportionate to the value and insight they provide. We do not collect more data than necessary.
• Interpretability - Our scores and assessments come with clear documentation explaining what they mean, how they should be used, and what they do not capture.

4. Research Ethics

Our research practices are guided by the following commitments:

• We conduct research with integrity, reporting results honestly regardless of whether they support our hypotheses or commercial interests.
• We acknowledge the limitations of our work and do not make claims that extend beyond what our evidence supports.
• We respect intellectual property rights and properly attribute the work of others that informs our research.
• We consider the potential for misuse of our research and take reasonable steps to prevent it.
• We engage with the broader AI research community through publications, discussions, and collaborations.

5. Human Oversight

We believe that AI measurement tools should augment human judgment, not replace it. Our frameworks are designed to inform decision-making, not to automate it. We encourage all users of our tools to apply their own expertise and context when interpreting results, and we design our outputs to support - not shortcut - critical thinking.

6. Environmental Responsibility

We are mindful of the environmental costs of AI research and computation. We strive to design efficient methodologies that minimize unnecessary computational overhead, and we consider the environmental impact of our infrastructure choices.

7. Continuous Improvement

Responsible AI is not a static achievement - it is an ongoing practice. We commit to:

• Regularly reviewing and updating our frameworks to reflect new research, standards, and societal expectations.
• Seeking external feedback on our methodologies and policies.
• Monitoring the real-world impact of our tools and adjusting our approach when necessary.
• Staying informed about evolving regulations, guidelines, and best practices in responsible AI.

8. Reporting Concerns

We welcome feedback, questions, and concerns about our AI practices. If you believe any of our frameworks, research, or processes raise ethical concerns, please contact us. We take all reports seriously and will investigate and respond in a timely manner.

VaryOn Capital LLC
Email: ethics@varyon.ai