Addressing Bias and Fairness in Generative AI: Challenges and Solutions

Technologies such as artificial intelligence, the Internet of Things, blockchain, and Machine learning have been addressed a plethora of times So what’s new in this blog? Well, have you come across the term generative AI? As the name suggests it is a subset of artificial intelligence that offers a wide range of high-quality content in regards to text, image, audio, synthetic data, and a lot more. One of the best examples of generative AI is ChatGPT – the open AI chatbot.  

Much like artificial intelligence, generative AI’s ultimate objective is to mimic human intelligence to such an extent that one won’t be able to differentiate whether a human or a person writes it. Do you know what is the best part here, it just takes a matter of seconds and you can have high-quality text, graphics, and videos. 

Generative AI has been in vogue for years. It was introduced way back in the 1960s but again in 2014, it took the limelight with the introduction of generative adversarial networks. Consider it as a machine-learning algorithm where it is possible to create authentic images, videos, and audio of real people in no time. Now what happens behind the scenes is that these algorithms tend to learn from different patterns, trends, and relationships so that it is possible to develop coherent and meaningful content. 

Understanding the working of Generative AI

Now it’s time to understand the overall working of generative AI. The technology uses different types of algorithms.  Here deep learning and neural networks are used for developing new and exciting content. Of course, generative AI takes assistance from existing data to a great extent. The process goes like this:

• Data Collection – Tons and tons of data are collected and gathered into different datasets. For example, if you want to create images of cars then datasets of cars will be created. 
• Training – The generative AI model is meant to collect different datasets. With the help of different techniques such as deep learning, specifically generative models like Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs). So what happens is, it becomes way easier to analyze different patterns and features.   
• Latent Space Representation – A latent space representation is successfully created. Now in simple words, it is a mathematical representation of different patterns and features. 
• Generation – Any new content can be generated, try adding some sampling points in the latent space and decode them.  
• Iterative refinement – Most of the generative AI models are trained through an iterative procedure. So evaluating and adjusting the model parameters is quite a doable job to enhance the quality and realism of the generated content. 

According to brainy insights, generative AI turns out to be one of the fastest-growing segments and in the span of the next ten years, it will increase 20-fold from $8.65 billion in 2022 to $188 billion by 2032. The future seems to be pretty optimistic and predictable. Here I would like to shine some light on some of the top-notch companies that have successfully adopted generative AI. 

1. Pfizer – The company uses generative AI to enhance its productivity to a great extent in multiple verticals from scientific to medical, manufacturing and so more. After the implementation of generative AI, it is possible to accelerate drug discovery, reduce research timelines, and launch more medicines as well as vaccines. 
2. Adobe – Another renowned company that has been integrating generative AI for image editing and design. After the inception of this Gen-AI-based Adobe platform, severe enhancement of content creation, manipulation, streamlined design procedures, and whatnot is possible. 
3. Amazon – Last but certainly not least is Amazon generative AI which assists sellers in creating more and more advanced product listings, and engaging advertisements, and best of all you get Alexa voice assistant. 

How does generative AI impact different industries? 

1. Healthcare 

Generative AI is highly recommended to analyze different medical images and assist doctors in conducting successful diagnoses. The World Health Organization has reported that with the inception of generative AI in the healthcare industry, 50% of all medical errors have been eliminated. Not to mention there has been observed a severe increase in the accuracy and a sudden relief in the eyes of the valued patients.     

2. Finance

The next industry that has been gaining benefit by implementing generative AI technology is the finance industry. By combining different types of finance software with different generative AI algorithms for detecting fraud, identifying different investment opportunities, and whatnot! Generative AI in the fintech industry has been proven a huge success as several tedious tasks such as automating routines, risk mitigations, and optimizing financial operations have been well taken care of.  

Since the finance industry is dealing with lots and lots of data, generative AI turns out to be a huge blessing in disguise. 

3. Transportation

Another industry that has gained benefits from generative AI is transportation. With the technology progressing, the scope of self-driving vehicles has increased to a great extent. And vehicles which are powered by generative AI can successfully navigate roads and make real-time decisions. It may quite interest you to know that generative AI can solve many problems that are next to impossible for humans such as traffic congestion, parking shortages, addressing long commutes, and so more. 

4. Retail and eCommerce 

Another industry vertical that seems to have gained a significant boost with the inception of generative AI is the retail and eCommerce industry. Right from optimizing inventory management to delivering highly tailored products, and visual product searches,  logistics management software plays a crucial role in streamlining operations and enhancing efficiency.

• Optimized inventory management
• High shopping experience
• Visual search

In short one of the most promising and imminent areas to consider is generative AI. Here you are bound to receive a streamlined shopping experience and high profitability of retailers’ online channels. 

5. Entertainment

Last but certainly not least comes entertainment. Yes generative AI is used to create personalized recommendations for movies, TV shows, music-based preferences, and whatnot! It may also fascinate you to know that generative AI has the potential to do some of the work done by creative professionals.  

Bias in Generative AI

Now have you ever come across a term called Bias in generative AI? The concept refers to the presence of systematic errors produced in the generated data which will definitely result in some unfair and discriminatory outcomes. So how is bias in the generative AI model? Well, there are different sources such as:

• Training data – Now this one is a primary source when it comes to bias in AI models. If the training data comprises biases or unrepresentative samples, the model itself will learn and reproduce these biases in the generated data.  
• Model architecture – It may quite interest you to know that bias can be introduced through model architecture. Additionally, the choice of loss functions and regularisation techniques can influence the model’s behavior, potentially introducing or exacerbating biases. 
• Optimization procedures – Now this one includes the choice of optimizing algorithms as well as hyperparameters which surely leads to bias in generative AI models. When you choose a specific learning rate, batch size, and weight initialization, all of this can have a strong impact on the model’s convergence and generalization resulting in biased outcomes. 

How to Take Care of the Situation?

The above mentioned are simply a few examples stating how bias can be introduced, and what’s worst is yet to come. You see there can be a ripple effect resulting in biased, offensive outputs. So what can be done? 

1. Get to know the source 

First and foremost, you have to ensure what is the exact cause here. Once you figure out what’s the issue, start minimizing the potential for biases.    

2. Make sure diversity and representation are established thoroughly

The next step is to make sure the users of generative AI are thoroughly represented in the training data. Try hiring workers from a diverse range of backgrounds, locations, and perspectives, and do not forget to train them to recognize as well as mitigate bias. 

3. Promote an adequate amount of transparency

Strictly start documenting data curation and label different procedures to identify what is the potential source of bias. Do not forget to keep things transparent, especially in regards to how data was selected and labeled.  

4. Document everything for the record

The next step is to document everything possible. Potential sources of bias can be easily identified if data curation and labeling procedures are thoroughly documented. Ensure transparency in regards to how data is selected as well as labeled. 

5. Keep monitoring and updating

Last but certainly not the least is to keep precise monitoring and updating. Ensuring constant monitoring and quick updations is pretty much required especially in today’s ever-changing times. You see constant monitoring ensures the detection of any bias right then and there especially if they missed out while conducting training procedures. At the same time, you are also responsible for improvements based on its needs.    

Final Words

Addressing biases is not easy but it is surely a doable job. This is all for now! I hope you enjoyed reading the post. Generative AI is extremely in vogue these days. So are you ready to get the most out of it? Also if you enjoyed reading the post, feel free to share with your peers and help us out in reaching more and more readers.