Algorithmic Trust – Challenges and methods to overcome AI bias

Algorithmic Trust

Artificial Intelligence (AI) has been widely adopted across various industrial verticals starting from healthcare to logistics. The major reason for this widespread adoption is AI’s capabilities to automate business processes. Automation has proved to be tremendously helpful for companies in terms of reducing effort, cost and time. 79% of the executives believe that AI has made their jobs easier and more efficient. But the capabilities of AI just don’t stop right here.

Data-driven decision-making is one of the main characteristics of Artificial Intelligence. With that being said, enabling AI-powered systems to be trustworthy is becoming increasingly important as AI is most likely to become the mainstream technology for companies in the upcoming years. Artificial intelligence is already being used to make important business decisions, monitor patient’s health 24/7, finding the best candidate that would fit a certain role, etc. All of these processes are dependent on algorithms.

Algorithms are simply a set of instructions written in programming languages and fed into an AI system. These algorithms are usually developed by humans. Hence it is essential to ensure that the developed algorithm is not biased in any way possible.

What is biased AI?

The definition of biased AI is pretty self-explanatory – an AI that is designed in a way to make decisions based on given instructions (algorithms) which might be unfair to certain groups of people.

An example of biased AI is Facebook ads. In 2019, Facebook was found to be violating the laws of the US constitution by encouraging customers to specifically target people based on gender, race and religion. Job advertisements were focused on gender depending on the roles. Open positions for nurses and secretaries have been targeting women majorly. Whereas, jobs roles like janitor and drivers were mainly shown to men of color from minority backgrounds. Moreover, ads related to real estate were only shown to white people.

This happened because the AI was instructed in such a way to filter and categorize people based on the information that they provide. Based on the information, the platform forms a certain pattern. This is how every AI system basically works. But in this case, this pattern leveraged the existing social inequalities for promotional and marketing purposes.

According to research by Stanford, Automated speech recognition systems were found to be exhibiting racial disparities. The voice assistant was not able to correctly identify  35% of the words of a native American whereas it was unable to identify only 19% of the words from white users.

As mentioned earlier, though an AI system is capable of making decisions, the underlying rules are developed by a human. People select the data that an algorithm must use and they decide how the data affects the outcome of the algorithm. Hence, data scientists need to analyze and inspect the developed AI modules to find problems and any potential bias and address these issues.

Challenges in addressing AI bias

Though there is a definition for AI bias, it is not the standard definition. Every company has its own set of rules and metrics to measure fairness. This leads to having various definitions for AI bias. One way to address these biases is to implement responsible AI that can understand ethics and is robust, secure, maintained, compliant and explainable.

With the lack of standards to follow, companies must carefully analyze what kind of information would cause such biases in the algorithms that they use. For instance, consider a scenario where a clothing company is advertising seasonal sales. In cases like this, the company has to target people based on their gender and age as it would be redundant to show ads about women’s clothing to men or vice versa. This cannot be considered as biased as it is the nature of these advertisements.

Another challenge in addressing this issue is the evolution of data and rules. AI models do not only access new data but also work with years of data. The set of standards and rules that were followed a few years back will most likely be evolved and hence the outcome may not be as expected. This will be the case for the present data as well in a few years.

Many non-governmental organizations like IEEE, World Economic Forum, MIT, etc are working towards developing a standardized definition for AI bias and principles and guidelines for companies to follow to fight against AI bias.

Ways to reduce AI bias and increase algorithmic trust

AI plays a vital role in making important decisions and is becoming increasingly smarter these days. It is even becoming self-learning where it can find data across the internet and learn from it. Google’s Multitask Unified Model (MUM), is one of the best examples of self-learning AI. MUM is essentially an AI module that is built for Google’s search functionality. It can identify relevant data based on the users’ search across all of the internet and present it to the internet. With the rapid development in the field of AI, effective and immediate measures have to be taken to prevent AI bias and develop trustworthy algorithms. Below are some of the ways to reduce AI bias while there are still no well-defined standards to define it.

Identify vulnerabilities

Different sectors like retail, banking and healthcare work with different data sets and different algorithms. Hence the AI bias issue they face is also not the same. Understand the category of your business and then try to analyze the possible vulnerabilities. Define these vulnerabilities in your AI systems and calculate financial, operational and reputational costs.

Have control over data

A constant method to control data will never work. The amount of data that the companies handle is just daunting. To keep up with the growing trends, you have to adopt techniques that can effectively provide control over the data. Pay keen attention to older data that are brought in from third-party sources.

Validate data

While validating the data, consider all details from a customer perspective – geographical location, gender, age to have a clear view in identifying biases. Have a dedicated team to look for any issues and biases in the algorithm. There are also tools like Biaz Analyzer to automate business processes that are effective in identifying bias and are also cost-efficient.

Ensure diversity in teams

Recognizing bias often differs with different people’s perspectives. Make sure that your teams have enough diversity – people from various backgrounds, regions, colors and religions. This will help in identifying potential biases. A diverse team should also have business experts across different verticals such as lawyers and accountants. This will provide their point of view in identifying biases and addressing them.

It is high time that all the companies understand the importance of fighting against AI bias and move towards developing trustworthy algorithms. Social inequalities cannot be completely eliminated, but companies can play their part to contribute towards reducing these inequalities like color, minority, religion and nationality. The complete potential of AI is still unknown. The technology could probably be in its very beginning stages. In the next decade, the adoption of AI will grow tremendously and the value of the global AI market is estimated to reach around $15.7 trillion. As we explore the potential of AI, companies need to know and analyze if their AI is unbiased in every way and if it provides valid and trustworthy information.

Coder-Engineer in the works. Infinitely curious like a feline. Would like to know what’s beyond the singularity someday.
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Coder-Engineer in the works. Infinitely curious like a feline. Would like to know what’s beyond the singularity someday.
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Coder-Engineer in the works. Infinitely curious like a feline. Would like to know what's beyond the singularity someday.
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