Distinguishing fact from bias is harder now than ever before. This is the core problem the biaskllr/AI process aims to solve by providing a sophisticated tool to analyze and highlight media bias in news articles, product reviews, and even social media posts. biaskllr combines cutting-edge artificial intelligence with decades of journalism experience to empower readers with unbiased knowledge.
In this article, we delve into how it works and the rigorous process behind its media bias detection capabilities.
The biaskllr/AI Process
The core of biaskllr’s functionality lies in its advanced AI engine, which has been meticulously trained to detect various forms of bias. Here’s a step-by-step breakdown of how biaskllr works:
- Data Collection and Preprocessing—The process begins with data collection. biaskllr’s AI is trained on a diverse dataset, encompassing sources from different categories such as politics, healthcare, finance, technology, entertainment, and more. This extensive training helps the AI recognize and differentiate subtle patterns of bias.
- Initial Analysis—Once an article is fed into the system, biaskllr’s AI performs an initial analysis, scanning the text for bias indicators. This involves examining the language used, the framing of information, and the presence of certain keywords and phrases known to convey bias.
- Bias Detection Algorithms—A the heart of biaskllr’s technology is its sophisticated bias detection algorithms. These algorithms evaluate the frequency and severity of biased statements within the article. They consider the context in which information is presented and identify any potential omissions or manipulations.
“Our AI looks at factors like sentiment, word choice, and context,” said CCO and co-founder Aron Vaughan. “It’s designed to pick up on even the most nuanced forms of bias that might slip past a human reader.”
Bias Score™
Our proprietary mathematical formula can score incidents of bias in a way that distinguishes between many points of origin and intent. It knows when a quotation in an article results from framing bias instead of when the language inside a quote was simply the right way to report the article.
Human Oversight and Validation
While AI plays a crucial role, human oversight is equally important. For the pre-analyzed articles posted on the site every day, this extra step comes into play. Experienced journalists at biaskllr review the AI’s findings to validate accuracy and provide additional context where necessary. This combination of AI precision and human insight ensures the reports are both thorough and reliable.
“Our journalists cross-check the AI’s analysis,” said Corey Noles, founder and CEO. “They bring a level of intuition and experience that enhances the overall accuracy of our reports. Moving forward on the results of a 2023 Stanford University study regarding doctors and AI, we believe this works best when human journalism experts and AI work together. That combination is more powerful and accurate than either is alone.”
Generating the Report
After the article has been thoroughly analyzed, biaskllr generates a detailed report. This report outlines the detected biases, provides concrete examples from the text, and explains why certain elements are considered biased. It also includes a Bias Score™, which quantifies the level of bias on a scale from 0 (bad) to 100 (good).
“The report is designed to be user-friendly, understandable by anyone with an eighth-grade reading level, and thorough enough for academics,” notes Noles. “We want readers to understand not just that an article is biased, but how and why it’s biased. In the future, we hope to offer the ability to switch between consumer, journalist, editor, and academic voices.”
Continuous Improvement
The final step in biaskllr’s process is continuous improvement. The AI model is regularly updated with new data, ensuring that it remains accurate and relevant. Feedback from users and the ongoing work of biaskllr’s team of journalists contribute to refining the algorithms and improving the service.
“We’re always learning and evolving,” said Vaughan. “Media and biases change over time, and our AI needs to keep up with those changes. That’s why we place such a strong emphasis on continuous improvement.”
There’s No Such Thing as Zero Bias
When using biaskllr, it’s important to remember that identifying and eliminating all media bias is not possible—but we’ve gotten close. In an effort to quantify bias, we employed the scientific method in product development.
In creating the system, navigating the layers of bias that color everything in our day was a challenge.
“We did our best to train out the inherent biases of the system, and ensure our own did not come into play,” Noles said. “It involved getting approvals from a diverse group of individuals on our team from varying political, social, economic, and educational backgrounds. Just the creation was a fun experiment in testing our own bias.”
While bias detection and verification are not a perfect science, this tool is arguably the closest answer ever.
