Transparency Statement for "Objectivity AI"
At Fabled Sky Research, transparency is one of our core values. As a for-profit organization, we are committed to developing cutting-edge artificial intelligence solutions, including large language models (LLMs), that are capable of transforming the way knowledge is processed, analyzed, and shared. While we aim to generate revenue by licensing our proprietary LLM technology to businesses and institutions, our mission is deeply rooted in fostering understanding, objectivity, and innovation.
Our Purpose for the Information Hub
The Israel-Palestine Conflict Information Hub is a public-facing initiative provided entirely free of charge. It serves two primary purposes:
A Robust Knowledge Base for the Public:
This hub is designed to provide individuals with a trustworthy, factual, and objective resource on one of the world’s most complex and divisive topics. We believe that access to unbiased information is a cornerstone of informed decision-making and constructive dialogue.A Real-World Test of Our Objective LLM:
The Israel-Palestine conflict was chosen as a test case for our custom-built LLM due to its global significance and the profound challenges it presents in maintaining objectivity. By demonstrating the model’s ability to process vast amounts of conflicting information and deliver neutral, fact-based insights, we aim to highlight the robustness and capabilities of our technology.
No Profit from this Initiative
To be clear, Fabled Sky Research does not profit directly from this information hub. This project is a contribution to the public good and an opportunity to showcase the strength of our model in navigating a divisive and emotionally charged topic. It is a testament to our commitment to accuracy, neutrality, and the power of AI to serve humanity.
Understanding Bias in LLMs
Bias in large language models (LLMs) is one of the most critical challenges in developing artificial intelligence systems that aim to provide factual, neutral, and universally reliable information. At Fabled Sky Research, we acknowledge that achieving true objectivity in AI is inherently difficult, as all models are products of the data they are trained on, and that data often reflects human biases. Below, we explain in greater detail the nature of bias in LLMs, how we address it, and the principles that guide our efforts to create an unbiased system.
Bias in LLMs can arise from several stages in their lifecycle
Training Data Bias
LLMs are trained on large datasets derived from the internet, books, news articles, and other human-generated content. These datasets often reflect the cultural, political, and social biases of their creators.
Example: News sources may have political leanings, historical documents may reflect outdated norms, and social media posts can amplify extreme viewpoints.
Selection Bias
The datasets chosen for training may disproportionately represent certain viewpoints, regions, or ideologies. For instance, Western media sources may dominate a dataset, marginalizing non-Western perspectives.
Algorithmic Bias
The architecture and optimization techniques used in LLMs can unintentionally prioritize certain types of patterns or narratives.
Example: Language models may over-represent frequently discussed topics and under-represent less popular but equally important viewpoints.
User Interaction Bias
When users interact with an LLM, their input can shape the responses. For example, leading questions or biased prompts can result in skewed outputs.
Objectivity in LLMs is not merely the absence of bias; it involves nuanced considerations.
Factual Accuracy vs. Interpretative Nuance
Facts may not always speak for themselves; how they are framed and contextualized can shape perceptions. For instance, stating casualty figures in isolation can imply different narratives depending on the framing.
Moral and Ethical Standards
While objectivity aims to avoid taking sides, universally accepted principles, such as international law and human rights, often serve as the foundation for evaluating claims and actions.
Conflicting Sources
On divisive topics like the Israel-Palestine conflict, different sources often provide conflicting accounts of events. Determining which sources are credible and why requires careful vetting.
At Fabled Sky Research, we take a multi-pronged approach to mitigate bias in our LLM
Data Curation
Our training data includes a wide array of verified and credible sources, such as international reports, peer-reviewed research, and diverse media outlets from different regions.
We exclude overtly inflammatory, unverified, or one-sided content unless explicitly labeled as opinion or perspective.
Cross-Referencing for Verification
Every piece of information is cross-referenced against a diverse pool of independent sources. Claims are only included as factual if at least 90% of credible sources agree on their validity.
Example: If 10 credible organizations report differing casualty figures for an event, the LLM will contextualize the range and indicate areas of uncertainty.
Transparency in Output:
The LLM clearly identifies the level of consensus and the sources behind its information, allowing users to evaluate the reliability of the output.
Example: Statements are labeled with tags such as “verified fact,” “widely reported claim,” or “minority perspective.”
Linguistic Neutrality
We train the LLM to avoid emotionally charged language or phrasing that implies judgment. For instance, instead of describing an event as a “massacre” or “defense,” it might state: “X number of casualties were reported during [event], with sources attributing responsibility to [parties involved].”
Dynamic Updates
The LLM is updated continuously with new data, ensuring its knowledge base reflects the latest information and consensus. Regular updates also allow the correction of previously identified biases.
We recognize that despite best efforts, no system is entirely, 100%, free from bias. To address this:
Regular Audits
Independent reviewers periodically assess the LLM’s outputs for potential bias or inaccuracies. These audits focus on identifying patterns of underrepresentation or overemphasis.
Feedback Mechanism
Users can flag responses they perceive as biased or inaccurate, enabling iterative improvements.
Stress Testing with Controversial Topics
The Israel-Palestine conflict serves as a stress test to push the LLM’s limits in maintaining neutrality on a highly polarized topic.
While our objective is to minimize bias, there are inherent limitations to any LLM
Impossible to Satisfy All Perspectives
On deeply divisive topics, some parties may perceive bias if the model does not align with their narrative. Objectivity often requires stating inconvenient truths.
Example: Critiquing settlement expansion may be seen as biased against Israel, while emphasizing Hamas’s violence may be perceived as biased against Palestinians.
Evolving Definitions of Objectivity
As societal norms and knowledge bases evolve, what is considered objective today may shift. Our LLM is designed to adapt to these changes but cannot preempt future perspectives.
Why Transparency Matters
We believe the key to trust is transparency. By openly sharing our methodologies, challenges, and limitations, we invite users to engage critically with the LLM’s outputs. Our commitment to transparency includes:
- Clearly labeling data sources and levels of verification.
- Providing detailed explanations of how outputs are generated.
- Maintaining openness to critique and refinement.
The Role of Bias in Complex Topics
A topic like the Israel-Palestine conflict is inherently sensitive, with deeply entrenched perspectives. Our LLM does not aim to resolve personal debates but to provide:
A Reliable Foundation
Fact-based, cross-referenced information to inform understanding.
Nuanced Context
Insights that acknowledge complexities without oversimplification.
Equitable Representation
Proportional attention to all major narratives, weighted by evidence, rather than perceived “fairness”.
Why We Chose the Israel-Palestine Conflict
We deliberately selected the Israel-Palestine conflict as the focus of this initiative because of its complexity and global relevance. This conflict involves deeply rooted historical, cultural, and political dimensions, with narratives that are often polarized along ideological lines. By tackling this topic, we aim to achieve several objectives:
Showcase the Model’s Robustness:
Demonstrating the LLM’s ability to process and present balanced, factual insights on a contentious issue illustrates the strength of our technology.Foster Public Understanding:
We believe that access to accurate, unbiased information is crucial for understanding this conflict and its broader implications for global peace and human rights.Highlight the Potential of AI in Sensitive Contexts:
By successfully navigating such a divisive topic, we aim to inspire confidence in the potential of AI to handle other complex issues with similar nuance and objectivity.
Our Commitment to Ethical AI
At Fabled Sky, we are committed to developing AI technologies that serve humanity responsibly. This means acknowledging the challenges of achieving true objectivity in AI while striving to meet that ideal through rigorous methodologies. Our work on this information hub is a reflection of our belief that AI can play a transformative role in fostering understanding, reducing bias, and promoting informed decision-making.
An Invitation to Explore
We invite you to explore the Israel-Palestine Conflict Information Hub as a resource for understanding this critical issue and as a demonstration of the capabilities of our LLM. By engaging with this platform, you are witnessing a real-world application of a technology designed to transcend human limitations in knowledge retention and objectivity.
If you have questions about our methodology or would like to learn more about Fabled Sky Research, we welcome your inquiries. Together, we can build a future where AI serves as a trusted partner in the pursuit of truth and understanding.