Artificial Intelligence
15 min read
Bias in Computer Vision: How to Detect Bias in Computer Vision
Written by
Chinar Movsisyan

As artificial intelligence (AI) continues to impact the world around us, computer vision has emerged as a critical application of this technology. From self-driving cars to security systems and robotics, computer vision allows machines to interpret and analyze visual data in ways that were once the exclusive domain of human perception. But as these systems become more widespread, concerns about bias in AI, including computer vision, are mounting. If we want these technologies to have a positive impact on society, it's essential to address these biases head-on. In this blog post, we'll explore the nature of bias in computer vision, why it's a problem, and what we can do to overcome it.

What is Bias in Computer Vision?

Computer vision algorithms have revolutionized the way we interpret and analyze visual data. With a range of tasks, from recognizing objects and classifying images to detecting faces and reconstructing scenes, these algorithms have become a vital tool in a variety of fields. But the magic behind these algorithms lies in the vast amounts of training data they require. This data provides the algorithm with the necessary context to learn visual patterns and recognize relevant features, ensuring accuracy and precision.

However, as we rely more and more on these algorithms, it's become clear that the data they are trained on isn't always perfect. In fact, it can introduce biases that impact the fairness and accuracy of the algorithm. Bias in computer vision refers to a systematic error or inaccuracy in the algorithms or models used for image recognition, classification, and analysis. And it's not just an abstract concept – it can have real-world consequences.

Take facial recognition algorithms, for example. If a dataset primarily consists of images of white males, the algorithm may struggle to recognize people of different races or genders accurately. The same holds for models trained to recognize certain objects or patterns in certain environments – they may struggle in unfamiliar contexts. This can be disastrous in applications such as autonomous vehicles, medical diagnosis, and security systems.

Common Types of Bias in Computer Vision Models

As computer vision models and AI algorithms continue to be deployed in real-world applications, scientists and ethicists have been paying more attention to the potential sources of bias in these systems. The primary causes of these biases stem from the lack of diversity and representativeness in the training data sets. If the data sets used to train the models are biased towards certain demographics or scenarios, the models may not be able to generalize well to other situations or populations. This is a challenging problem to solve because of the dynamic nature of the real world.

Environmental Biases: Autonomous Vehicles and Delivery Robots

Biases can occur due to simple mistakes, such as mislabeling images, leading to label bias and resulting in errors in the model's predictions. Another critical type of bias is environmental bias, which arises when algorithms are trained on data sets that lack a diverse range of environmental contexts.

Take autonomous vehicles, for example. These vehicles rely on computer vision algorithms to detect and identify objects in their surroundings, including other vehicles, pedestrians, and traffic signals. However, if the algorithms are trained on datasets that do not include a diverse range of environmental contexts, the vehicles may struggle to recognize and respond appropriately to objects or obstacles that appear outside of those contexts. In other words, the algorithms may lack the flexibility to adapt to unfamiliar or unexpected situations. For example, if the algorithms are primarily trained on data sets that consist of well-maintained roads in urban or suburban areas, the vehicles may not be able to respond appropriately to obstacles or hazards that appear outside of those contexts. Rural areas with unpaved or poorly maintained roads can present significant challenges for these algorithms, as they may not be able to recognize hazards such as potholes or steep inclines. Additionally, they may struggle to recognize and respond appropriately to animals or wildlife that may cross the road unexpectedly, something that is more common in rural areas.

Similarly, delivery robots, which are increasingly being used for last-mile delivery in urban environments, also rely on computer vision algorithms to navigate through their surroundings and avoid obstacles. For instance, delivery robots may have difficulty navigating through crowded sidewalks or detecting obstacles in low-light conditions, compromising their performance. To improve the robot's performance, it is crucial to train the algorithms on datasets that contain a variety of different lighting conditions, allowing the robot to adapt to different environments and perform more reliably. Moreover, if the algorithms are primarily trained on data sets that represent sunny and clear weather, delivery robots may struggle to navigate through environments covered in rain or snow. During such weather conditions, the cameras and sensors may be affected, leading to inaccuracies in object recognition and navigation. These inaccuracies can potentially result in failed deliveries or other negative outcomes.

Data Bias Leads to Racial Bias

Data bias is another common type of bias observed in computer vision models. Data bias occurs when the dataset used to train a machine learning model is not representative of the real-world data it is expected to encounter. This can lead to inaccurate and unfair results, particularly for underrepresented groups. For example, image recognition systems that associate images of people of color with negative stereotypes or incorrectly identify them as criminal suspects can perpetuate harmful racial biases. These biased models can lead to real-world harm, including racial profiling and discrimination, and can exacerbate existing social disparities.

One way that data bias can contribute to racial bias in computer vision is through the underrepresentation of people of color in the training data. This can lead to inaccurate results when the model encounters diverse real-world data. In medical imaging, for example, studies have shown that computer vision systems built to detect skin cancer in patients can perform less accurately for people with darker skin tones. This can result in delayed or inaccurate diagnoses, which can have serious consequences for patients, particularly those from underrepresented groups.

Tools and Real-World Examples of Bias Detection

A sub-industry has emerged, known as "MLOps," with the aim of developing the essential tools required to construct more dependable machine learning models. Such tools are instrumental in identifying and mitigating various types of bias that can impact the precision and impartiality of computer vision models. For instance, some of these tools analyze the datasets on which models are trained, measuring factors like demographic parity that can lead to biased outcomes. Other tools assess the performance of models across diverse scenarios and populations to determine how they perform and what biases they may exhibit.

Manot is designed to assist computer vision engineers in mitigating bias throughout the development of their computer vision models. It achieves this by continuously monitoring the model's performance during both the pre-production and production stages. Manot is equipped to observe and monitor computer vision models as they operate in the real world, and can quickly identify areas where the model is performing poorly. These instances are known as outliers, as they do not conform to the expected behavior of the model. By collecting these outliers, Manot detects biases and provides new and smart data to be added to the model’s training dataset, in order to improve its performance. By doing this at the pre-production stage, teams can deploy their models with greater confidence that they will function reliably, as they have already been tested on novel datasets. However, it is important to remember that the real world is a dynamic and complex environment, and there are always bound to be scenarios that the model is not equipped to handle. To solve these biases it is also essential to continue monitoring the model’s performance in production and catching outliers that cause biased actions from the model.

The Importance of Model Ethics in Computer Vision

The potential for biased and discriminatory outcomes makes model ethics particularly important in the field of computer vision. By promoting model ethics in computer vision, we can ensure that AI models are developed and used in a way that is transparent, fair, and non-discriminatory. This can involve measures such as ensuring that the training data is diverse and representative, providing clear explanations of how the model makes decisions, and implementing mechanisms for auditing and monitoring the model's performance over time.

Transparency is particularly important in computer vision because it can help identify potential biases and inaccuracies in the model. By providing clear explanations of how the model makes decisions, we can identify potential sources of bias and take steps to correct them. This can help build trust in the technology and ensure that it is used in a way that aligns with societal values and expectations.

Finally, safety is also a key consideration in computer vision, particularly in applications such as autonomous vehicles or medical diagnosis. By promoting model ethics, we can ensure that computer vision models are developed and used in a way that prioritizes safety and minimizes potential risks.

Best Practices for Data Ethics in Computer Vision

So, what are the best practices for data ethics in computer vision? Here are a few key principles that data scientists and developers should keep in mind:

Monitoring: Use mechanisms for monitoring and auditing the computer vision model's performance over time, and be accountable for any negative impacts that may result from its use.
Be transparent about how data is collected, used, and shared, and provide clear explanations of how the computer vision model makes decisions.
Fairness and bias: Work to mitigate bias and ensure that the computer vision model is fair and equitable across diverse populations.
Involve stakeholders: Involve stakeholders such as users, data scientists, and ethicists in the development and deployment of computer vision systems to ensure that ethical considerations are taken into account.


Computer vision is a powerful technology that has the potential to have a big effect on our world. However, as we continue to rely more on these systems, it's imperative to address the biases that can impact their performance and fairness. Bias in computer vision can result in real-world consequences, from facial recognition systems that fail to recognize people of different races or genders accurately to autonomous vehicles that struggle to navigate unfamiliar environments. Therefore, it is essential to develop tools and techniques that can detect and mitigate bias in computer vision models throughout their development and deployment. One such tool is what we’ve developed at Manot, which can assist computer vision engineers in identifying biases and providing new and smart data to improve model performance. However, the real world is dynamic and complex, and biases can arise from a wide variety of sources. As such, continued monitoring of computer vision models is crucial to ensure they function reliably and fairly in diverse scenarios. Ultimately, by addressing biases in computer vision, we can be more confident that the AI models we deploy in the real world will have a positive impact on their users, and society overall.

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