6 Ways to Eliminate Bias in AI Systems

Artificial Intelligence and its growing use have a stirred debate going on about fairness and bias in many sensitive areas such as healthcare, criminal justice, and hiring. One myth arising today is will AI’s decisions be less biased than human ones? To write a new road map for the future with the emerging fast pace of business applications, the time has come!

With abundant opportunities for businesses and a wave of disruption, AI is coming up in the market with both. And it will not be just reserved for the back office or factory work, it will take place and develop through the process in the organisation up to the highest level or position. To address these ethical concerns, organisations are launching a range of initiatives to avoid downsides.

To manage business risks, one needs to integrate with the new technologies!

AI Grows, Ethics Concerns emerge!

Diverse ethical risks are imposed with AI systems, as its capable adoption increasingly grows in businesses. Companies today are making AI ethics a priority because of its biasness attitude in the decision-making, as well as balancing the risks and benefits is vital while using AI.

For enabling people to trust these systems, eliminating or reducing bias in AI is a vital prerequisite.

For the economy through productivity & growth, for the society through contributions, to tackling pressing societal issues, and to drive benefits for businesses, shown by the research of MGI & others, this can or will be critical, if AI is to reach its potential.

One needs to consider several paths forward for those striving to minimize bias and maximize fairness from AI could. Top six ways:

1. Humans & Machines can best work together! This need to be explored to the deep

This includes use-cases and considering situations because when automated decision-making is acceptable vs. when humans should always be involved. Some promising systems for reducing bias use a combination of humans and machines. Techniques in this vein include ‘human-in-the-loop decision making, which humans double-check or choose from, where algorithms provide recommendations or options.

In such systems, humans must necessarily understand how much weight to give AI for transparency about the algorithm’s confidence in its recommendation can help.

2. More investment in the AI field & its diversification

AI community will be better equipped to anticipate, spot, and review issues of unfair bias and better able to engage communities that are likely affected by bias, a more diverse. Including gender, race, caste, geography, class, name, role, and physical disabilities, many have pointed to the fact that the AI field itself does not encompass society’s diversity,

Getting access to tools and opportunities will require investments on multiple fronts, but especially in AI education.

3. To mitigate and test for bias, establish practices & processes in AI systems

Drawing on a portfolio of tools and procedures will be required for tackling unfair bias. The components that most heavily influence the outputs are technical tools described above which can highlight potential sources of bias and reveal the traits in the data.

  • To audit data and models, Operational strategies can include improving data collection through more cognizant sampling and using internal ‘red teams’ or third parties.
  • Finally, to promote fairness and any associated trade-offs, transparency about processes and metrics can help observers understand the steps taken.

4. Adopt a multidisciplinary approach & make more data available by investing in bias research


More investment in these efforts will be needed, while significant progress has been made in recent years in technical and multidisciplinary research. Working on these issues is being sensitive to privacy concerns and potential risks, business leaders can also help support progress by making more data available to researchers and practitioners across organizations.

As the field progresses and practical experience in real applications grows, a key part of the multidisciplinary approach will be to continually consider and evaluate the role of AI decision-making.

5. For potential biases in human decisions, engage in fact-based conversations

How AI can help by surfacing long-standing biases that may have gone unnoticed, as AI reveals more about human decision making, leaders can consider whether the proxies used in the past are adequate.

When models trained on recent human decisions or behavior show bias, organizations should consider how human-driven processes might be improved in the future.

6. Be aware of those contexts in which there is a high risk for AI to intensify bias

It is important to anticipate domains when deploying AI such as those with previous examples of biased systems or with skewed data that are potentially prone to unfair bias. Organizations will need to stay up to date to see where AI systems have struggled and how and where AI can improve fairness.

  • For examining human biases, better AI, analytics, and data could become a powerful new tool. This could take the form of running and effective algorithms for the organizations alongside human decision-makers, evaluating performances, comparing results, and examining possible explanations for differences. To emerge in several organizations rapid examples of this approach are starting.
  • It should not simply cease using the algorithm but should consider how the underlying human behavior needs to change, similarly, if an organization realizes an algorithm trained on its human decisions (on prior human decisions or data-based) shows bias.
  • By applying the most relevant tests for bias to human decisions, perhaps organizations can benefit from the recent progress made on measuring fairness, too.

Some of the emerging work has focused on ‘model cards for model reporting processes and methods, such as datasheets for data sets which create more transparency about the testing, construction, and intended uses of AI models and data sets.

For reducing discrepancies or eliminating or minimizing bias in AI systems, innovative techniques such as decoupled classifiers have proven useful for different groups, also human judgment is still needed to ensure AI in decision-making!