Artificial Intelligence bias: What you need to know

While several definitions have surfaced over the last couple of decades, AI or artificial intelligence refers to the branch of computer science that enables a computer or a computer-controlled machine to mimic human action and thought to quickly produce rational and logical outputs. 

Although AI has delivered significant benefits to its early adopters, including increased efficiency, cost reduction, improved customer experience, and revenue growth, among others, however, many concerns have emerged regarding whether it is fair, unbiased, and ethically just. Hence, today’s post is for you if you’re skeptical of artificial intelligence. 

What are the applications of Artificial Intelligence?

Artificial intelligence, in some domains, provides an end-to-end solution by automating business intelligence and analytics processes to solve complex problems efficiently. Given below are a few examples of how machine learning helps improve efficiency:

• Fraud detection and information security: 

Financial institutions like banks and businesses use artificial intelligence to detect fraudulent activities and cyber crimes. These applications of AI can also monitor behavior, detect anomalies, adapt, and respond to dynamically changing threats. To train the AI software, large sets of sample data are fed to the software, including fraudulent and non-fraudulent purchases, to determine whether a transaction is valid based on that data. The software quickly spots fraudulent transactions or anomalous activity with adequate training and calibration. 

• Online customer support and marketing in retail: 

Several websites offer a chat functionality where customers can contact for assistance anytime. The chat functionality is AI-powered and has replaced the need for human resources in the customer support domain. In addition, companies use AI to market a product, provide recommendations, and for sentiment analysis. 

• Fulfillment and Supply-Chain Management: 

The three most significant cost drivers in supply chain management are inventory carrying costs, transportation costs, and labor costs. Artificial intelligence enables company management to address these drivers through rapid augmented decision-making. Companies can reduce inventory and transportation costs by making faster decisions based on historical data and actual demand patterns. The company can also guarantee the product will be available at the right time. Furthermore, AI helps optimize solutions, allowing companies to move products efficiently without extra labor costs.

What is machine learning bias or Artificial Intelligence bias?

Artificial intelligence or machine learning bias occurs when biases in training or assumptions made during the algorithm development process lead to an anomaly in the output of algorithmic learning. 

How are biases in AI caused?

Biases in AI may occur because of one or both of the reasons mentioned below:

• Cognitive biases: 

Cognitive biases emerge from the brain’s attempt to simplify processing information. The bias seeps into the machine learning algorithms when designers unknowingly introduce them into the training algorithms or when a data set used for training consists of these biases.

• Incomplete data: 

In some situations, the data set is incomplete, meaning that the data is not representative of an entire population. This data collected is included in the training algorithm without testing it on the whole world; this causes bias.

What are the types of AI bias?

There are different types of bias that may plague AI systems. They are:

• Algorithm bias:

In machine learning, algorithm bias occurs when the algorithm that performs the calculations is flawed.

• Sample bias:

Sample bias is also known as selection bias. When a data set used to train the machine learning model is not large enough, does not represent an entire population, or does not reflect the whole ethnic make-up of a people, it results in selection or sample bias. Usually, sample bias is likely to occur when one ethnic community is underrepresented compared to other ethnic communities in a region.

• Prejudice bias:

Prejudice bias occurs when the data set train the system consist of some existing prejudices, stereotypes, or faulty assumptions. The introduction of these prejudices results in biases in machine learning.

• Measurement bias:

Bias arises due to the inaccuracy of the data and how it is measured and assessed. 

• Exclusion bias:

Exclusion bias occurs when a crucial data point is missing from the data used. This bias can arise if the modelers do not recognize the data point as being important.

Best Practices Can Help Prevent Machine-Learning Bias

Many reputational and regulatory risks are likely to arise from using partial data. Several best practices have emerged to prevent machine-learning bias, such as:

1. Prepare training data that is representative, large enough, and representative sufficient to counteract stereotype bias and prejudice bias, two common types of bias in machine learning.
2. Check for bias in the results of machine learning systems with algorithms and data sets by testing and validating them.
3. As the systems continue to learn as they work, you need to monitor them to make sure biases do not develop in the future.
4. To examine and inspect models, utilize additional resources, such as Google’s What-if Tool and IBM’s AI Fairness 360 Open Source Toolkit.

Steps to fixing bias in AI systems:

Over time, experts have developed several ways to fix the bias in AI systems, such as:

• Improving human-led processes as new biases in training data are discovered: Building models and evaluating them can reveal biases that have gone unnoticed for a long time. The company can use this knowledge to understand the reasons for prejudice and then reduce the bias through efficient training, process design, and making the right cultural changes.

• Adopt a multidisciplinary approach: 

Proper research and development are vital to minimize the bias in data sets and algorithms. Experts such as ethicists and social scientists should be included in the AI projects as they better understand the nuances of the application area. 

• Diversify your organization: 

Diversity in the artificial intelligence community can help mitigate any unwanted AI biases as the people who belong to the minority community are more likely to identify biases quickly. 

Conclusion:

AI can be a boon for business, and the economy and address society’s most pressing social issues, including bias across the globe. However, people need to trust that these systems will produce unbiased results for that to be possible. Artificial intelligence has the power to aid humans, but only if humans first work together to rid AI of bias.