How to Tackle the Bias in Expert Systems

It is commonly perceived that Artificial Intelligence can potentially eliminate human bias. While that is true, however, it can only be done if we work towards ensuring fairness and objectivity in AI systems as well. 

Artificial Intelligence algorithms learn to consider only the variables that ascertain their predictive accuracy, which is how they reduce the subjectivity in the human interpretation of data.  

There is also evidence that shows how machine learning algorithms improve the process of decision making, causing it to be fair and objective. 

All this aside, there is also extensive evidence that suggests that Artificial Intelligence models can embed human and societal biases and actually deploy them at scale. Ouch! 

The main source of the problem here, however, is not the algorithm itself but the data that underlies it. Since the creators of models are humans themselves, the AI models are often trained on data that contains human decisions or reflects second-order effects of societal inequities.

In order to aid the spread of Artificial Intelligence and enable people to trust it, it is essential to first minimize the bias that persists in the systems. The reduction of bias is critical for AI to reach its maximum potential– to drive profits for business, productivity growth in the economy, and also tackle some major societal issues. 

Those striving to maximize fairness and minimize bias from AI could consider several paths forward:

Consider the Context in Artificial Intelligence

analyze context

There are certain situations in which AI can help correct for bias, while on the other hand there are instances where it could actually exacerbate it, which is why it is highly important to be aware of the context. When deploying AI, you have to anticipate the domains that are prone to unfair bias and have a history or examples of biased systems or skewed data. For this, you will have to stay up to date on instances where AI has actually reduced bias and where it has struggled. 

Establish Test Runs 

test runs

Setting up practices that test for and mitigate the bias in AI systems has proven to be quite beneficial. It does, however, require you to draw on various technical tools and procedures that can highlight the potential sources of bias while also revealing the aspects of data that have the biggest impact on the output. 

Operational strategies can include improving data collection through more cognizant sampling and using internal teams or third parties to audit data and models. 

Furthermore, it is also important to remain transparent regarding the process and metrics as it can help your consumers understand the steps that were taken to ensure objectivity.

Be aware of Potential Human Biases 


It can be helpful to engage in factual conversions regarding the biases that are present in human decision making. 

AI can reveal the nuances of the human decision-making process, uncovering the long-standing biases that may have gone unnoticed in the past. When models trained on recent human decisions or behavior show bias, organizations should then move towards finding solutions and improving the human-driven processes in the future. 

Analyze the Union of Humans and Machines

humans and ai

It is important to scrutinize the relationship between humans and machines to fully explore how they can work best together and bridge the gap between them.  Analyze the situations and cases where automated decision making is acceptable and actually beneficial compared to the cases where the involvement of humans is necessary. You may find that some systems work best to reduce bias when there is a combination of AI and humans, working in cahoots. 

Techniques in this vein include “human-in-the-loop” decision making, where algorithms provide recommendations or options, which humans double-check or choose from. In such systems, transparency about the algorithm’s confidence in its recommendation can help humans understand how much weight to give it.

Such an approach will help you plan better and optimize the contributions of both humans and machines

Invest in Diversifying the Artificial Intelligence Field 

ai field

Many have pointed to the fact that the AI field itself does not encompass society’s diversity, including gender, race, geography, class, and physical disabilities. A more diverse AI community will be better equipped to anticipate, spot, and review issues of unfair bias and better able to engage communities likely affected by bias. This will require investments on multiple fronts, but especially in AI education and access to tools and opportunities.

Concluding Points:


 The domain of Artificial Intelligence has opened doors to unprecedented opportunities. It has embedded itself across the entire marketing landscape making it significantly more effective, consequently, allowing marketers to focus back on the strategic parts of marketing and deliver the greatest possible revenue impact.

If you know how to minimize potential bias and successfully utilize the powers of AI, you have mastered the art of business growth! 

Speaking of business growth, let us now introduce you to Lucrative! Lucrative is an autonomous AI that is equipped with the ability to fully optimize all aspects of your campaign, yielding unprecedented profits and growth. It is the one-stop solution to all your marketing concerns! 

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