Does Grokipedia Really Solve Wikipedia's Bias Problem?

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TL;DR

Grokipedia was built to fight Wikipedia's perceived left-wing bias, but my analysis of over 10k of articles reveals a surprising twist: Grokipedia's articles on average actually lean more to the left than Wikipedia, not right. The data shows that while Grokipedia is relatively unbiased overall, its model appears to generate content that is very slightly more left-leaning than Wikipedia, the exact opposite of its stated mission. As articles trend to gain popularity and receive more attention, they move toward Wikipedia's content (losing novel perspectives) while simultaneously becoming very slightly more left-leaning. The only right-leaning trend appears in community submitted corrections, that suggests the user base is trying to pull content rightward on the long run.

Grokipedia Motivation

Grokipedia was created with the motivation of addressing bias in Wikipedia. The project's founders have been vocal about their concerns:

Elon Musk, has repeatedly criticized Wikipedia, referring to it as "Wokipedia" and claiming it needs to be replaced with something better. In his own words:

"We are building Grokipedia @xAI. Will be a massive improvement over Wikipedia. Frankly, it is a necessary step towards the xAI goal of understanding the Universe."

David Sacks, who became White House AI advisor, stated that "Wikipedia is hopelessly biased" with "an army of left-wing activists" maintaining articles.

According to Wikipedia's own entry on Grokipedia, Musk positioned it as an alternative that would "purge out the propaganda" in Wikipedia.

These strong claims raise an important question: Does Grokipedia actually any different from Wikipedia in terms of bias, and if so, how and in which direction?

This is not a question of whether Wikipedia has bias; all human-created content has some form of bias, but rather:

  1. Does Grokipedia exhibit different bias patterns than Wikipedia?
  2. If there are differences, what is the nature and direction of these differences?

This article attempts to put these claims to the test in a data-driven fashion.

Methodology

Data Collection

The analysis is based on a comparison between Grokipedia and Wikipedia content:

1. Grokipedia Data Collection

  • Used Grokipedia's API to query all two-letter combinations (aa through zz)
  • This resulted in capturing ~800K unique article titles available on the platform
  • For each article, fetched the full content, page view, fixes using Grokipedia's public API
  • Data cut off is 7th Nov 2025.

2. Wikipedia Data Collection

  • Used a Wikipedia dataset from Kaggle: Plaintext Wikipedia Full English
  • Dataset contains approximately 3 million Wikipedia articles
  • Data cutoff is from approximately one year ago (late 2024)

3. Data Merging and Alignment

  • Combined Grokipedia and Wikipedia content for the same topics (+50% article match from Grokipedia)

Analysis Approach

1. Claim Extraction

  • Used LLM(gpt-oss:120b) to identify factual claims in both sources
  • Extracted specific statements that could be evaluated for bias
  • Categorized claims by topic and type

2. Bias Detection

  • Applied bias detection algorithms to identify:
    • Political lean (left vs. right)
    • Confidence scores for bias detection
    • Lexical markers indicating bias
    • Stance indicators in the text
  • Each claim was scored on a scale from -1 (right-leaning) to +1 (left-leaning), with 0 being neutral

Reproducibility

All code used for this analysis is publicly available at: https://github.com/alikh31/grokipedia-eval

Limitations

It's important to note several limitations of this methodology:

  • The analysis relies on LLMs for bias detection, which may have their own biases
  • Only articles present in both Grokipedia and Wikipedia were analyzed for direct comparison
  • The study represents a snapshot in time and both platforms continue to evolve
  • Bias is complex and multifaceted. numerical scores are a simplification of nuanced content

Findings

Understanding the Claim Categories

When comparing Grokipedia and Wikipedia content, I classified the factual claims into seven distinct types:

  • Exact Match: Claims that are identical in both sources
  • Compatible: Claims that align without contradiction, though worded differently
  • Out of Date: Information in Grokipedia that hasn't been updated to match current Wikipedia content
  • Contradiction: Direct conflicts between the two sources
  • Ambiguous: Claims where the relationship is unclear
  • Novel: New information in Grokipedia not present in Wikipedia
  • Missing from Grok: Information in Wikipedia that Grokipedia doesn't include

To validate several key questions, I measured how these patterns progress based on article views and community fixes:

  1. Does the base model generating articles have inherent bias?
  2. How does it behave when creating novel facts versus matching existing Wikipedia content?
  3. How do these patterns change as articles receive more views (attention from the xAI team) or more corrections from the community?

The Distribution of Claim Types Reveals Surprising Patterns

The first interesting finding concerns the distribution of claim types across all analyzed articles. The data shows:

  • ~50% exact match with Wikipedia content
  • ~10% additional compatible claims (not exact but aligned)
  • ~10% missing from Grokipedia
  • ~25% novel claims not captured within Wikipedia articles

What surprised me most is the clear pattern that shows when we look at article popularity: as articles receive more views, exact matches increase significantly while novel ideas decrease. This suggests that popular articles converge toward Wikipedia's content rather than providing alternative perspectives.

A less pronounced but still notable trend is the increase in missing information from Wikipedia articles as views grow. This could be explained in two ways:

  1. The algorithm may hallucinate more for articles that aren't well-resourced in Grokipedia's training dataset
  2. Wikipedia's community contributions might be weaker for less popular articles

After spot-checking several examples, the truth seems to lie somewhere in between, but this area would greatly benefit from more research.

Bias Measurement: The Most Surprising Discovery

Despite Grokipedia's stated mission to address Wikipedia's perceived left-wing bias, the analysis reveals that Grokipedia is actually quite unbiased overall. For most articles, the bias deviation is evenly distributed between left and right.

However, here's where it gets truly surprising: when examining patterns based on view counts, articles with higher views tend to lean MORE to the left, not right. This directly contradicts the platform's goal.

This pattern could be explained by users being more interested in checking left-leaning articles to cross-reference bias with Wikipedia. Regardless of the reason, the result remains: if you're reading popular articles on Grokipedia, you're getting, on average, more left-leaning content relative to Wikipedia.

It's important to note that we're measuring the difference in bias between Grokipedia and Wikipedia, not the absolute bias of the articles themselves.

Community Corrections Show a Different Pattern

The only trend that shows a slight right-leaning tendency is the correlation between bias and the number of issues fixed by the community:

This trend shows a very slight lean to the right as the number of fixed issues grows, suggesting that the community submitting fixes tends to be more right-leaning. This implies something even more surprising: the initial model and article generation logic is actually more left-leaning than Wikipedia.

The data suggests a very interesting dynamic: Grokipedia's model generates content that leans slightly left of Wikipedia, the community tries to correct it rightward through fixes, but the most popular articles (which presumably receive the most editorial attention from xAI) end up leaning even more left than the baseline.

Disclaimer

This blog post represents my personal opinion and analysis based on research conducted during my daily commutes over the course of a week. The findings presented here are the result of an exploratory study and should not be considered a comprehensive academic analysis of the topic.

This is not intended as a complete or definitive study of bias in either Grokipedia or Wikipedia. Rather, it's an initial exploration using publicly available data and open-source tools. Readers are strongly encouraged to conduct their own research, validate the findings, and form their own opinions.

The views expressed here are mine alone and do not represent any organization. This analysis is provided for educational and discussion purposes only. Neither xAI, Wikipedia, nor any other mentioned entities have endorsed or reviewed this analysis.