Tips to address bias in Artificial Intelligence (AI)

AI is becoming more and more prevalent in the world.

It can be found in a variety of industries, from healthcare to finance, and it’s been shown to be more accurate than humans are at making tons of decisions.

However, there is a bias problem associated with this technology because AI systems are created by people who have their own biases or prejudices.

One important thing to remember when it comes to AI is that computers are more likely than humans to make mistakes when they don’t understand things or data goes in that was never taken into account by the creators of the system.

Biases in AI could lead to bias in the work produced by machines.

This blog article explores how to address bias in AI systems.

Topics covered in this blog:

·        What is AI?

·        How has AI been used?

·        How bias affects various industries

·        Tips on how to avoid biases associated with AI

What is AI?

AI is any machine or program that has been designed to replicate human cognitive functions. This includes learning, problem-solving, planning, and more.

In the context of this article, “AI” relates to computers, which are often only able to perform a very narrow set of tasks but can be advanced enough to handle these tasks with speed and efficacy beyond what humans can achieve.

As AI systems are becoming more prevalent in our lives, it’s important for us to be aware of how AI is used, biases in AI and how to avoid these biases.

How has AI been used?

AI has been used in a variety of industries from healthcare to finance, agriculture and so on.

#1. Healthcare:

Use of electronic health records, or EHR to save time, reduce medical errors and improve standardization.

#2. Finance:

Use of Machine learning for predictions and recommendations for stock markets and other financial products.

#3. Retail:

Use of AI for recommendations and personalising online shopping that helps improve user experience. 

#4. Utilities:

Use of AI such as Smart meter an energy management system that can optimize power usage and predict peak demand times.

#5. Agriculture:

Use of AI to detect crop diseases early on by studying the traits of every plant/crop to give recommendations.

How bias affects various industries

It is important to be aware of biases when developing AI systems, as they can affect the work produced by the machines.

Below are the biases that can creep into different industries:

#1. Bias in Hiring

Bias in hiring such as salary for females in STEM-related professions was predicted to be lower salary offers than others.

This is a result of machine learning systems looking for patterns in past data (in this case, CVs) which has biased hiring practices and is influenced by the learning algorithms. 

#2. Bias in Policing:

Bias in policing algorithms, where law enforcement uses data from previous crimes and predict where crimes are likely to occur.

If police patrols are assigned predominantly in neighbourhoods with higher crime statistics, then these ‘over-policed’ neighbourhoods could be treated as sources of criminality.

#3. Bias in finance:

Bias in Finance, where algorithms for mortgages were mostly white males, it might lead to an increase in loans to men.

If the algorithms were implemented for use and a diverse range of mortgage recipients were found, then the algorithm will produce significantly different results.

#4. Bias in healthcare:

Bias in healthcare, where algorithms are used to assess whether or not a patient needs certain treatments.

If the patient is of a minority group and their data was not represented in the model build, it might result in that person receiving fewer treatments than other people with the same disease and symptoms.

#5. Bias in education:

Bias in education, where the learning algorithms are using previous test scores and grades to determine if a student is ready for college or not.

If students from certain ethnicities were under-represented in those data sets, then AI may recommend that some of these underrepresented students do not go on to college.

Tips on how to avoid biases associated with AI

In order to ensure that AI systems are used in a way appropriate to us human beings, there is a need to incorporate a diverse range of people in the development and operation of AI systems.

The following tips can be used for avoiding biases associated with AI.

#1 Create a Framework:

It is important to create a framework that makes it easy for people to understand how the predictions of AI systems are reached, including those not directly involved in the development or operation of such systems.

#2 Be aware of Impact on people:

It is also important to think about how a large number of people will be affected by actions taken by an AI system and consider whether there are any negative repercussions resulting from this.

Be aware of the fact that some people may not be able to deal with decisions made by AI systems, so a framework (as mentioned above) should be in place to help them understand what is happening and why.

#3 Examine the input data:

It is important to be aware of the type and quality of data that will be used in an AI system. Data has a huge impact on predictions, so having good quality data saves you time, effort and money when getting predictive results.

#4 Train the Data:

It is important to train datasets in order to make sure that any bias present does not affect your AI system’s predictions.

On the one hand, this means that you need to check whether existing data is biased. On the other hand, you need to ensure that any new information gathered in the future is done with care and sufficient controls are put in place, so it does not create bias.

#5 Use Synthetic data (Human Generated):

Human-generated data can help in training AI systems because they represent a complex environment that is hard for machines to understand.

The use of human-generated content also ensures that the system being trained has access to as much information as possible so it can create a more accurate representation of the real world.

#6 Check that AI algorithms are free from bias:

The process for identifying and addressing bias in algorithms involves reviewing the process used when designing it and ensuring that key points that result in bias are addressed as part of this process.

The more advanced the AI system, the harder this process will be, so it is important to have a thorough and well-thought-out plan before implementation. Relying on informal or vague methods for detecting bias cannot provide you with complete assurance that bias will not affect your AI systems.

#7 Test, test, and retest:

It is crucial to perform a wide range of tests on an AI system before it is put into use.

Different tests should be carried out with different input data in a variety of environments to see how the AI capabilities respond and produce different outputs.

These tests will help improve performance by highlighting weaknesses in an AI system and improve the AI capabilities over time.

#8 Transparency and Interpretability:

To be able to effectively understand how an AI is reaching any decisions, it needs to be built with a level of transparency that allows us to trace how information flows through the system.

Make sure that any AI decision-making results can be easily interpreted by humans. It is important to allow human beings to easily interpret decisions made by AI systems because it ensures regulatory compliance with local and international laws.

#9 Have an Ethical Committee:

When deciding what tools should be used within a system, it is important to have an ethical committee that discusses how AI can impact people’s lives in different ways.

This committee must come up with a way to test out these systems, so they are sure they don’t have any bias and won’t do any harm.

#10 Automate detection of bias:

Creating an AI system to detect biases in other systems is an interesting idea, but it comes with some big challenges that need to be addressed.

For instance, there may be instances where the results are not clear cut so the human assigned to inspect these results needs to have a high level of expertise and must be able to understand the purpose of the system so they can inform stakeholders accordingly.

#11 Keep records on decisions:

It is important to record and store data about decisions made by an AI system because they will be used in training further versions of the same system.

These records are also important for auditing purposes because they provide a clear account of what actions were taken by an AI system at any given time.

#12   Be prepared:

Certain biases are difficult to avoid completely, but it is still important to try.

It goes without saying that an organisation operating an AI system should carefully evaluate the actions taken by its systems and seek solutions that will minimise possible harm.

The organisation needs to plan measures for dealing with errors in advance, in order to minimise the negative consequences they will inevitably have on society.


In conclusion, it is important to understand that biases can creep in at any stage of the AI development process.

To avoid these unavoidable biases from affecting your company’s bottom line, take a proactive approach and consider implementing some or all of the above tips.

The more you are aware of how bias might be creeping into your AI systems, the better chance you have for minimising its negative impacts on society.

What are your thoughts on avoiding bias in AI?  What are other ways that biases can affect AI?

Have you ever experienced bias in AI? How did it happen?

Hope you found this article useful.

Aruna Patam

Aruna Patam

Aruna heads the AI & Data Science practice at HCL Technologies for Asia Pacific and Middle East region based at Sydney.

Her current focus is on how to use AI and Data Science at scale in order to solve business challenges across a variety of domains including financial services, insurance, retail, utilities, telco among others. Apart from this, other areas she is highly passionate about are Women in AI, AI ethics, responsible AI and how AI is helping organisations meet ESG expectations.

Aruna has spent the last 21+ years delivering high impact solutions using data analytics, artificial intelligence, machine learning and analytics platforms. She has led teams and implemented solutions such as fraud detection, anti-money Laundering, credit risk, privacy, data protection, regulation, compliance etc.

She is a thought leader, blogger, speaker, mentor and a vlogger creating engaging videos with the goal of educating and providing awareness on AI to the business and wider community.

She regularly shares AI related information aimed at demystifying AI and detailing the ever-expanding AI scope for business transformation.

Visit her website to know more about her contribution to AI outside of work

If you are interested to know more on AI or want to chat, please feel free to connect with her on LinkedIn