Artificial intelligence (Artificial intelligence) is not about improving our lifestyles with better movie recommendations, restaurant suggestions, conflict resolution chat robots, and more. The power, potential, and capabilities of artificial intelligence are increasingly being exploited in all industries and areas that no one was likely to think about. In fact, artificial intelligence is being researched and implemented in areas such as healthcare, criminal justice, supervision, hiring, closing the pay gap, and more.

In such complex uses, justice in the whole process or ecosystem is inevitable.

As humans, we rely on the machines of answers, solutions, and information because of their objective answers. In fact, we know that machines would not take sides and provide us with insights that would help us make better decisions or draw conclusions. However, fairness with machine learning (ML) and Artificial intelligence algorithms all boil down to the way they are trained.


This eye-opening message explores the possibilities of artificial intelligence to take sides and produce prejudices and understand whether we could combat such cases. Known in `science as` `prejudice ”, this message applies to all-purpose fair and universal ML algorithms.

Let’s get started.

If you ask your friend how specific a movie was, chances are they will offer an opinion on their tastes and preferences, their intellectual inclinations, their life experiences, their personal influences, and more. Instead of offering you objective insights into what the film was about, its pros and cons, and more, they suggest whether you should watch it or skip it. While their opinions could be accurate, they are all due to bias.

While human bias is great, machines need to be completely airtight in their handling, training, and delivery of outputs. If this sounds too complicated, understand this variance and prejudice Artificial intelligence systems are anomalies that result from prejudices and assumptions and completely distort the results.

Bias is introduced mostly in training phases, where (either intentionally or unknowingly) AI experts enter amounts after amounts of data with certain tendencies and preferences. Such preferences, when introduced into artificial intelligence models, also influence algorithms to make similar decisions.

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As experts in artificial intelligence or computer science, extreme care must be taken in detecting and prejudicing systems prejudices and removing them from airtight results.

To get a better picture, there are two main types:

  • cognitive bias, which refers to feelings or judgments towards a particular person or group according to how they are perceived in society
  • prejudices due to lack of data, where the lack of sufficient data puts the artificial intelligence model at risk of familiarity with prejudices, as it is no longer exposed to situations or scenarios that could disprove an already existing perception or idea of ​​something.

When such prejudices and opinions are introduced, models of artificial intelligence become biased or biased towards a particular race, gender, institution, school of thought, or anything else that must primarily be comprehensive.

As we mentioned, the bias of artificial intelligence could be introduced unintentionally or voluntarily. Without addressing these aspects, it is understood that bias may arise from any three sources or sometimes from all three sources.

The results provided by the artificial intelligence model are only as effective and accurate as the data entered. During AI training, the data used to train the algorithm plays a crucial role in model bias. The data should be of sufficient sample size, represent different real-world scenarios, and be free of any underlying social and personal prejudices.

Machines have no power or ability to reinforce materials for their fairness, and they learn from everything that is fed to them. Therefore, it is very important that data scientists are aware of the data they use to train models. When artificial intelligence affects mortgages and the payment of claims, prejudices can prevent people from getting loans if the system is biased. The worst part is that such scenarios also go unnoticed in real life, as audits hardly detect and address such concerns.

Perhaps most of the bias is due to the people. In deciding what is fair and what is not, there must also be a general or fairly impartial approach in the decision-making process as well. Who decides what is fair and what is biased? What are the parameters of this analysis? Such complex questions need to be answered, and therefore companies should bring in experts from different fields and provide a contextual work environment based on data and results. While accurate results are inevitable, we also need objective and targeted results.

As we mentioned, algorithms do not have the ability to introduce new prejudices, but once they exist in a system, algorithms can explode into large parts. If the model is trained with images of only women in kitchens and men in the garage, it would smooth out women in the kitchen and men in the garage, distorting future results. To avoid this, the models must have instructions that dictate them to avoid such information or to limit the results to certain search volumes.

The good news is yes. Artificial intelligence systems can be without prejudice and prejudice, which shows how pure data sets are and how careful you as an educator are, voluntarily and unintentionally, by bringing delusions into an artificial intelligence system. However, there is also bad news.

We do not see artificial intelligence systems to be completely neutral at any time, because, after all, it is people who create algorithms and data, and in order to eliminate all anomalies completely, we must constantly work to reduce people’s parties. , starting with our thought processes.

Instead, we can come up with different validations and tests to test the results and bring in protocols and best practices to train artificial intelligence models.

Based on the previous paragraph, we have compiled a list of ways to remove the prejudices of the AI ​​and ML models. Let’s look at them separately.

So now you know the three sources that cause bias in your AI and ML models. Once this information is in hand, you must first examine the data sets extensively and understand the different types of prejudices that are likely to infiltrate the system. You will then need to assess its impact and ensure that there is no bias in capturing or detecting the data. When you use historical data to train your model, you also need to make sure that the data you use is without pre-set prejudices. These factors can eliminate prejudice to a significant extent.

Representative data should be comprehensive, and this is the primary responsibility of the data scientist to gather data that is consistent with the problem, the market segment, the population that uses it to resolve real-world conflicts, and more.

Bias is often included in models in the data cleansing steps when data is selected from data sets. To avoid this error, companies should ensure that their data cleansing and selection processes are properly documented. Relevant documents, third parties or others

stakeholders can come with their fresh eyes and see opportunities to stray in. This paves the way for transparency and improves artificial intelligence models.

Model training does not stop in laboratories with training data. Real concerns arise when models are exposed to the world as they resolve real conflicts. Because there are differences in the performance of the model in laboratories with training data and in the actual data on their performance, experts should continuously monitor the performance of the model and optimize it to achieve efficiency. This means the constant detection and removal of neutrality. This eliminates organizations that get a bad reputation because of skewed results.

When artificial intelligence dominates our daily lives, we are at a stage of technological development where we need to be careful about our investment and the models we build. In addition, when everything boils down to the quality of information training, we recommend that you contact us for artificial intelligence training purposes regarding quality data sets. Our team employs data science veterans who take care of removing prejudices and paving the way for an objective data training process.

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Author Bio:

Vatsal Ghiya is a serial entrepreneur with over 20 years of experience in healthcare artificial intelligence software and services. He is the CEO and founder of the company Saip, which enables the scaling of platforms, processes and people as needed for companies with the most demanding machine learning and artificial intelligence initiatives.


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