Algorithmic Bias

What is algorithmic bias?

Algorithmic bias or AI bias refers to the unfair or discriminatory outcomes resulting from the use of algorithms. It’s a series of systematic and repeatable errors in a computer system that favors one group of people in ways that don’t match the intent of the algorithm functions. 

With artificial intelligence (AI) and machine learning (ML) spread across industries, algorithmic bias is a growing concern for all. While algorithms are intended to bring efficiency, they are not immune to flaws in human design. Therefore, it’s necessary to understand the biases that algorithms can develop over time. 

Algorithmic biases occur due to many reasons - biased data, design flaws, and conscious or subconscious human prejudice throughout the development stage, among others. 

AI & machine learning operationalization (MLOps) software helps monitor and mitigate the potential risks of bias proactively. This software helps prevent the consequences that disrupt the well-being of different societal groups.

Algorithmic Bias Software

Software, die algorithmic bias als Funktion oder Begriff erwähnt.

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Algorithmic bias examples

Algorithmic bias has many forms. It can vary from racial bias and gender prejudice to inequality and age discrimination. However, these biases can be unintentional as well. For instance, if a facial recognition algorithm is trained using a set of data that isn’t inclusive, it won’t work effectively for all groups of people. Since these biases are unintentional, they are often difficult to identify before they have been programmed into a system.

Here are some more algorithmic bias examples:

• Racial bias in healthcare systems:  In healthcare, algorithmic biases may include providing the same quality of medical facilities but charging more money from one group of people.
• Gender bias in hiring systems: Algorithmic bias can also exist in hiring systems where one gender is preferred over the other. Any hiring decision based on gender bias and not merit is unfair.
• Racial bias in criminal systems: Algorithms that predict the chances of an offender reoffending may discriminate against a certain group. The prediction may be biased against criminals belonging to a particular race, making them liable for a longer sentence. 

Types of algorithmic bias

Algorithmic bias can take many forms and occur due to different factors. Five common types of biases can exist in an algorithm:

• Data bias arises when the data used to train an algorithm doesn’t represent all sets of people and demographics. It will result in the algorithm producing unfavorable outcomes based on non-inclusive data. This type of bias can exist in hiring, healthcare, and criminal systems.
• Sampling bias occurs when the training dataset is used without randomization. It can also occur if the dataset is not a representation of the population the algorithm is intended for. It can lead to inaccurate and inconsistent results in a system. This can exist in a banking system where an algorithm predicts loan approvals based solely on high-income groups.
• Interaction bias exists when a system interacts differently with users due to their characteristics or demographics. It results in inconsistent treatment and unfair outcomes for people in a specific group. This type of bias can be found in facial recognition systems that may recognize one race easier than the other.
• Group attribution bias happens when data teams assume the truth about an individual based on the group they may or may not be a part of. This bias may occur in admission systems that favor candidates from certain educational backgrounds and institutions over others.
• Feedback loop bias can occur when the biased results generated by an algorithm are used as feedback to refine it further. This practice can amplify the biases over time, resulting in a bigger disparity between different groups. For instance, if an algorithm is suggesting certain jobs to men, it may further consider applications from male candidates only.

Best practices to prevent algorithmic bias

While the tech industry has a long way to go before eliminating bias in algorithms, there are a few best practices to keep in mind to prevent it.

• Design with inclusion: When AI and ML algorithms are designed with inclusion in mind, they won’t inherit biases. Setting measurable goals for algorithms will result in consistent performance across all use cases, i.e., all groups, irrespective of age, gender, or race.
• Test before and after deployment: Before the deployment of any software system, thorough testing and evaluation can identify biases that the algorithm may have unintentionally inherited. Once the deployment is complete, another round of testing can help identify anything that was missed in the first iteration.
• Use synthetic data: AI algorithms must be trained on inclusive data sets to avoid discrimination. Synthetic data is the statistical representation of real data sets. Algorithms that are trained on synthetic data will be safe from any inherited biases of real data. 
• Focus on AI explainability: AI explainability allows developers to add a layer of transparency to AI algorithms. This helps in understanding how AI generates predictions and what data it uses to make those decisions. By focusing on AI explainability, the expected impact and potential biases of an algorithm can be identified. 

Through the best data science and machine learning platforms, developers can connect data to create, deploy, and monitor machine learning algorithms.