How to Avoid AI Feedback Loops

The ‘ant mill’ reflects one of the critical challenges in artificial intelligence – feedback loops. An ant mill occurs when army ants lose track of their pheromone trail and follow each other in an endless, often fatal, loop.

This mirrors the potentially destructive behavior that can occur in AI systems. Just as ants blindly follow one another, leading them astray, AI, too, can create a similar cycle of reinforcement, where it continuously feeds on its own biased or narrow data.

These ‘digital ant mills’ can steer AI technologies away from their intended purpose, trapping them in a cycle of repetitive and unproductive behaviors.

Understanding ant mills can shed light on the complexities of AI feedback loops, so we can create more trustworthy, adaptable, and effective AI systems.

3 Examples of AI Bias

In terms of bias, you can see this occurring in the following scenarios:

Credit Scoring Systems

Certain financial institutions use AI for credit scoring. If an AI system is trained on historical data that includes biased lending practices (e.g., fewer loans given to certain demographics), it may perpetuate this bias. The AI’s decisions reinforce the data pattern, creating a feedback loop.

Over time, this can lead to systemic discrimination against specific groups, further entrenching financial inequalities.

Healthcare Diagnostics

AI algorithms are increasingly used to diagnose diseases from medical imaging. If the training data predominantly includes images from a certain demographic group, the AI might become less accurate in diagnosing conditions in patients from underrepresented groups.

This can lead to a feedback loop where the AI continues to improve for the majority group while becoming less effective for others.

Job Recruitment

AI-powered recruitment tools can develop biases based on the data they are fed. For example, if an AI system is trained on resumes from a field historically dominated by men, it might inadvertently learn to prefer male candidates.

As it screens candidates based on this bias, the data reinforcing this preference accumulates, creating a biased feedback loop.

Scenario: AI-Powered Content Recommendation System

Right now, it’s very tempting to pivot towards AI, especially when your rival launches a shiny new AI tool and gets lots of press coverage. But remember the ants above and their unfortunate death spiral. We want to avoid this. Let’s take a look.

Imagine a biased AI feedback loop in a content recommendation system, like on a streaming service.

Initial Setup and Goal:

Suppose an online streaming platform implements an AI system to recommend videos to users. The goal is to increase user engagement by suggesting content that aligns with individual preferences.

Data and Learning Process:

  1. Initial User Data

The AI system begins by analyzing user data, including previously watched videos, search history, and user ratings.

  1. Recommendation Algorithm

Based on this analysis, the AI recommends content it predicts the user will like.

Creating a Biased Feedback Loop

  1. Limited Exposure

This starts as users are predominantly shown content similar to what they’ve previously engaged with. For instance, if you’ve watched science fiction movies, the AI keeps suggesting similar genres.

  1. User Interaction Reinforces Bias

As users interact with these recommendations (watching, liking, rating), the AI system interprets this as positive feedback, reinforcing its belief that these choices are what the user prefers.

  1. Data Homogeneity

However, over time, the AI’s learning is primarily based on this increasingly homogeneous set of user interactions. The system is not exposed to data that could challenge or broaden its understanding of the user’s preferences.

Consequences of a Biased Feedback Loop

This leads to the following consequences:

Echo Chamber Effect

Users start to see a very narrow range of content, creating an echo chamber. This limits the diversity of content they are exposed to and potentially locks them into a very specific content profile.

Stagnation of AI Learning

The AI system’s learning stagnates. It becomes very good at recommending a certain type of content but fails to recognize or suggest other genres or styles that the user might enjoy.

Loss of User Interest

Over time, users might become disengaged due to the lack of variety in content, impacting the platform’s goal of increasing user engagement.

Breaking the Loop

So, how can you prevent this?

To break the loop, introduce ways to challenge its own patterns.

This could involve:

  • Including a “randomized” or “explore new genres” feature in recommendations.
  • Periodically asking users to rate content outside their usual preferences.
  • Implementing algorithms designed to detect and correct for such biases.

In this scenario, the feedback becomes biased as the AI system’s learning is continually reinforced by limited and homogeneous user interaction data. This leads to a narrow content recommendation strategy that fails to evolve, demonstrating how AI can get trapped in a figurative loop, reinforcing existing patterns without gaining new insights.

Next Steps: How to Avoid AI Feedback Loops

To ensure your AI systems avoid creating biased loops, users – particularly those involved in designing, managing, or interacting with these systems – can take several practical steps:

Continuously incorporate diverse and representative data into the AI system. This includes data from different sources, demographics, and perspectives. In addition, look to regularly update the training dataset to reflect current and evolving trends, rather than solely relying on historical data.

By implementing regular audits of the AI system, you can identify any biases or skewed patterns in its decision-making or recommendations. Use external or third-party tools and experts for unbiased auditing.

Establish a ‘human-in-the-loop’ system where critical decisions or recommendations made by the AI are reviewed by your team, especially in early stages or critical applications. Make sure to train staff to recognize and address potential biases in AI outputs.

Next, perform routine tests to check for biased outcomes, and validate the AI’s recommendations against real-world outcomes to ensure they are accurate and unbiased.

Finally, implement mechanisms for users to provide feedback on AI decisions or recommendations. Use this feedback to adjust and improve the AI system. You can also give users the option to flag results or decisions that seem biased or inappropriate.

By following these steps, you can minimize biased feedback loops in AI systems and escape potential digital ant mills.


Ant mills and complex AI feedback loops highlight the importance of vigilant oversight in self-reinforcing systems. Just as army ants can unwittingly spiral into a fatal loop, we need to ensure that AI systems don’t become trapped in biased behavior.

Minimize the potential for biased feedback loops in AI: diversify data inputs, conduct thorough audits, and implement human oversight. This ensures your AI systems avoid the pitfalls of biased feedback loops and puts your business on the right path.