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The Ethics of AI: Addressing Bias and Fairness in Machine Learning

Updated: Mar 23, 2023

Machine learning (ML) has transformed technology to unimaginable levels. Machine learning has enabled companies to automate some of their services and decision-making processes. Automation allows business owners to enhance operational efficiency and cut-down labor costs.

Nevertheless, bias in ML affects an AI system outcome, leading to discrimination or unfair practice. For this cause, ML engineers must employ tactics that eliminate bias and promote fairness when creating ML models.

In this article, we look at why it is necessary to address bias and fairness in machine learning using a case study. More so, we discuss ways to avoid bias in ML. Lastly, we provide the problems related to machine learning and their solutions.

Let’s dive in.

The Value of Addressing Bias and Fairness in Machine Learning

Algorithms may produce biased outcomes if machine learning engineers ignore fairness when inputting data into a model. Unfair machine learning can lead to unintended discrimination based on class, sexual orientation, gender, and race. Here is a case study to help us understand machine learning bias and its effects.

Case Study: COMPAS

Source: Proplublica

The United States court systems adopted the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), an AI to predict the possibility of offenders committing crimes in the future. COMPAS focuses on age, employment, and previous arrests to predict the relapse possibility for offenders. The AI’s intended use in courts was to enable judges to decide the jail term for offenders.

Nonetheless, a 2016 report by ProPublica showed that COMPAS was biased against black people. The AI indicated that black offenders were more likely to commit similar crimes in the future. The biased outcome stemmed from the training data showing high crime rates for black people. Therefore, the system caused judges to have a racial bias when sentencing criminal defenders.

Reasons for Addressing Fairness and Bias in Machine Learning

Machine learning bias can lead to unintended discrimination against a group of people. For instance, COMPAS led to unfair judgments against blacks in the United States. Promoting fairness in machine learning will correct the training data to reduce discrimination against race or gender.

More so, fairness in machine learning helps design safe and responsible AI systems. AI systems are created in such a way that they can make decisions with minimal human interactions. If the machine learning process is fair, the AI system will make accurate and safe decisions. For example, a fairness machine learning process for a court system can promote justice in judgments.

Ways to Avoid Bias in Machine Learning

Machine learning engineers try to eliminate bias and promote fairness in the machine learning process. The engineer can collect and clean training data to ensure the model produces desired outcomes. Below are some of the methods of avoiding bias in machine learning:

  • Engaging a variety of data labelers and data scientists during the machine learning project.

  • Using multi-pass annotation for projects with a probability of skewed data outcomes.

  • Evaluating machine learning models using tools like IBM’s Ai Fairness 360.

  • And combining multiple source training data to ensure information diversity.

Challenges in Machine Learning and Their Solutions

ML engineers face problems when training machines to perform tasks with precision. Understanding the challenges can help you know how to develop an algorithm that serves the intended purpose. Here is a list of the issues in machine learning and their solutions.

Poor quality of data

The standard of information you use in machine learning determines the output you receive. If you commence with low-class data, you will receive an inefficient and defective algorithm. For instance, using inconsistent data in machine learning leads to an algorithm with inaccurate actions.

The solution for this is to employ data quality tools. The tools can remove typos, missing entries, redundancies, and formatting errors to improve the quality of your information. It will help your machine learning project yield the desired results.

Irrelevant features

An excellent set of features is essential in machine learning for a quality algorithm. Machine learning engineers make the mistake of concentrating on structures that do not improve an algorithm’s workability. For example, kilobytes are an element of an email, and they cannot help differentiate spam from genuine email.

When feeding data features into a machine, ensure they can help create a perfect algorithm. For example, valuable features of an email are the subject line, content, words, and links. Reducing irrelevant elements allows you to get an effective final product.

Lack of Training data

Training data is among the most valuable components of machine learning. Machine learning models require training data representing what you intend them to do for you specifically. A bigger volume of training data or examples allows your model to master the algorithm better.

Nonetheless, you may lack sufficient training data or examples for your model. For example, you may not have many spam and legitimate emails to feed your model. For this reason, you can have a model that is inefficient in detecting spam emails.

The solution for this challenge is to concentrate on getting sufficient training information for your model. It is advisable to hire a fully-managed data sourcing service to help you with your training data needs. The professionals have access to loads of quality training data for your model.


Although machine learning brings high returns on investments to a company, the initial project can be unaffordable. A machine learning model customized to an enterprise’s needs can be expensive. For this reason, most small businesses cannot manage to implement machine learning models.

However, small business owners should not avoid machine learning models since they can employ no-code AI. The emerging technology enables anyone to create a model using an existing template like that of WordPress.

Data Security

Data insecurity is a prevalent challenge in the IT industry. Machine learning is not immune to hackers who will infiltrate your organization’s data for selfish reasons. For instance, a hacker can send a link with a virus to corrupt a company’s computers and wipe away all the information. If your training data is wiped away, your machine-learning project may end.

The remedy for data insecurity is to discourage employees from opening unauthorized links. Furthermore, you can build firewalls to protect your organization’s network from hackers. The move will protect your training data for the machine learning model.

Final Thoughts

Bias is the biggest stumbling block in machine learning since it can lead to discrimination and unfair practices. The case of COMPAS shows that ML bias can cause judges to make decisions that discriminate against a specific race. Emphasizing ML bias and fairness helps engineers to appreciate the need to embrace to reduce discrimination in ML models.

Issues such as lack of training data, low-class training data, data insecurity, unaffordability, and irrelevant features can block you from creating an effective ML model. The best way to overcome such challenges is to work with a fully-managed data service. The experts have the experience, tools, and workforce required to streamline your ML project.

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