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Data Science Algorithms and Models

Addressing Biases in Data Science Algorithms and Models

Table of Contents

Artificial intelligence systems have been known to show technological improvements in business processes. They have drastically changed the way we make decisions. We know the popularity of AI tools and how their results benefit people. But, the algorithms used in data science can also produce biased results, sometimes. That might have a little to a huge impact on the organization or the community.

The inaccurate results often must be corrected so that professionals can use them correctly. Poor data can affect marketing campaigns, setup, cost analysis, etc. According to research, 76% recognized the need for a centralized approach to addressing data bias rather than relying on individual departments to handle it separately.

There are ways in which we can control data bias. Take Data Science Training to learn more about preventing and controlling such biases.

The rest of the article explains data bias and how we can address it.

What is a Data Bias?

Data bias in machine learning and other data science algorithms refers to the errors resulting from overweight or overrepresented data. This results in inaccurate and erroneous data that leads to biased model results. Due to data biases, a machine learning algorithm cannot capture the true relationship between the data and the learning curve of an ML algorithm. It leads to incorrect model performance and skewed results.

Data biases can be classified as follows:

Specification biases

The specification biases arise from the overweighted choices and specifications. It usually has results directed towards specific data groups.

Measurement bias

Biases resulting from incorrect measurement of data are called measurement bias. This can be caused due to inaccurate data records, faulty calibration, inaccurate measuring instruments, etc. Sometimes, it can lead to observer bias, which means the observer fails to grasp the model’s results.

Sampling bias

Sampling biases, also known as selection biases, can arise when there is underrepresentation or overrepresentation of specific data sets. As a result, the model performs for specific data groups, which we do not want. Sampling bias can be caused by self-selection, data set, or survivorship bias.

Annotator bias

Biases resulting from personal choice and labeling from an annotator are known as Annotator bias. Human annotators might judge the data on their basis and pass it on to the machine learning models.

Inherited bias

Machine learning algorithms are often linked with one another to produce results. An output from a model can be used as an input for another data science algorithm. When one algorithm produces biased results, it affects the linked algorithms, too. That’s called an inherited bias that a model captured from another model.

Ways to Address Data Bias

Although, if carefully planned and executed, some measures can be taken to mitigate such biases. Here are the steps to reduce such biases.

  • Identify the sources of bias

Look closely at the data sets and models you use. There might be possible errors in the data collection, sampling errors, quality issues on data, human choices, incorrect assumptions on algorithms, or contextual factors. Apart from this, the algorithms and tools also play a big role. All you need to do is identify the source of bias affecting your results.

  • Determining the right Machine Learning model

The data sets play a huge role in impacting the results. The first step is to determine carefully the datasets you have taken. It is the parameters that affect the results of the analysis. Results become biased towards a few parameters if the datasets are incomplete. Modifying them or changing the sets is more helpful in getting accurate results. Tools such as LIME (Local Interpretable Model-Agnostic Explanations) and SHAP(Shapley values) can help determine the cause behind inaccurate results.

  • Proper Documentation

Clear documentation of the data helps analyze the factors. Which parameters are influential and which ones are least important can only be known through documentation. The data presented properly is the key to getting good documentation. This ensures clarity among the data. Only that data is then put into machine learning algorithms, which are known to be influential. So, documentation is essential in getting the correct data for machine learning models and algorithms.

  • Evaluate model performance for various categories

After thoroughly examining the data sets, now is the time to analyze the model results. Sometimes, models give the best results on various parameters and categories. Sometimes, the models could perform better in the other few categories. This creates a bias towards specific categories and might not be useful. So, programmers must ensure the model performs well in all the categories. It would be best to find out the bias’s root cause.

  • Spread more awareness

Organizations understand the importance of ethics in the workplace. So, it is crucial to make people aware of the biases that AI models and algorithms might produce. They can inform people regarding the steps taken to combat the biases. This builds trust among people, and they might not misuse the data results.

  • Collaborate with various Stakeholders

Teams can collaborate with groups of experts, stakeholders, and other tech professionals. They can help analyze the risk with AI models and help to mitigate them in advance. They should be involved in designing, developing, deploying, and evaluating AI Systems. Their input, feedback, and consent are even more important to consider. Respect their rights, interests, and expectations. Aim to strive for healthy relationships among each other to build trust and transparency.

  • Stay Updated!

Technology evolves with time, and so do AI and ML algorithms. Continuous learning and education help to deal with complex biases and algorithms. Therefore, tech professionals must stay updated regarding the latest developments and updates in AI algorithms and data science concepts. Connect with experts regarding the best practices and standards in AI ethics and social responsibility.

Conclusion

Data biases need to be addressed as soon as possible. The results might have an adverse impact. If we can work on it, it will make the systems fair, not just the machine learning algorithms. When organizations come together to solve such issues, they help many greatly. Discover Data Science Courses.

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