Responsible Generative AI: Key Factors for AI Safety and Trust

BharatGPT: Leading the Way in Secure and Ethical AI

Generative AI systems have made remarkable strides in recent years, seamlessly producing highly realistic images, videos, and even generating natural language and music.  

However, amidst these impressive advancements, one must acknowledge the challenges that current generative AI systems encounter. To unlock their full potential, these challenges need to be met with innovative solutions and a forward-thinking approach. I’ll divide this article into three parts. 

1. First, we’ll talk about the present challenges and solutions that Gen AI has! 
2. Key factors for the AI Safety.
3. BharatGPT: A Responsible AI platform.

It’s been years since I delved into the conversational AI space, and during this journey, certain issues have become glaringly evident, demanding immediate attention. 

As CoRover took shape, these concerns only intensified, solidifying the need for proactive solutions. It’s not just about recognizing the challenges; it’s about driving the conversation toward effective resolutions that align with the evolving landscape of conversational AI. It’s a commitment to shaping the future of AI interactions. Let’s look at a few of them, one point at a time.

Nuances in AI Bias

Many generative AI systems are trained on biased or incomplete data, which can lead to biased or inaccurate output. Can you imagine the impact on something as vital as healthcare? It’s like having a compass that occasionally points in the wrong direction. Bias in medical data can lead to disparities in diagnosis and treatment.

Researchers are developing techniques to mitigate bias in generative AI systems, employing adversarial training to ensure that the output is unbiased across different groups.

Scalability

Generative AI systems are computationally intensive and require large amounts of data to train effectively. But do you see the problem? Think of those areas where resources are limited, or where there’s a lack of digital systems in developing countries. 

To address this issue, we can explore ways to make AI systems more efficient, such as using transfer learning to transfer knowledge from one system to another or using compression techniques to reduce the size of the models and a lot more. I’m proud to share that CoRover.ai is positively impacting over 1 billion lives through its technology solutions and is actively exploring avenues to scale this impact even further.

The Problem of Interpretability

Many generative AI systems are black boxes, meaning that it can be difficult to understand how the system is producing its output. It might create a problem in healthcare where it is important to understand how the system arrived at its diagnosis or treatment recommendation.

Techniques like attention mechanisms can be used here. This is the same concept that is being used behind the famous “Transformers” Architecture, the backbone of today’s AI systems.

Uncertainty in AI

Many generative AI systems produce output that is probabilistic in nature, meaning that there is a certain level of uncertainty associated with the output. This can be a problem in areas where the output needs to be highly accurate and reliable, such as in medical diagnosis or financial forecasting.

Using Bayesian inference to model the probability distribution over the output is one of the go-to solutions for this.

The Challenge of Fostering Creativity

While generative AI systems can produce highly realistic and convincing output, they may only sometimes be capable of true creativity. This is because creativity often involves the ability to break free from established patterns and generate unexpected output, which current models are not capable of. 

Researchers aim to enhance the creativity of generative AI systems by implementing reinforcement learning techniques. It’s like an artist learning through active trial and error pushing the boundaries of its creative expression. 

The Problem of User Experience

Generative AI systems are created for human use, serving as tools for creative expression or interfaces for human interaction. However, the user experience may be constrained by factors like system speed, output quality, and user control. With 64% of business owners seeing AI as a customer relationship enhancer, solving this challenge becomes crucial. We can deploy interactive interfaces to enhance user experience.

The Problem of Security

AI systems are susceptible to attacks. For instance, imagine a scenario where an attacker purposely manipulates the input to generate unexpected or malicious output. This poses a significant issue in areas like autonomous vehicles or critical infrastructure, where the repercussions of a successful attack can be severe. To address this concern, adversarial training is being implemented.

No doubt, we face challenges, but asking better questions leads to better answers. While some challenges can be eliminated, others can be mitigated, ultimately enhancing the effectiveness of AI. One such method is the AI Safety Net which recently gained popularity in AI Safety Summit, 2023.

AI Safety Net

The following are the key factors that could serve as the foundation for the AI Safety: 

Explainability: Refers to the ability to understand how a generative AI system is producing its output. By making the system more transparent and interpretable, it is possible to identify and address issues such as bias and uncertainty. 

Inclusivity: Refers to the need to ensure that generative AI systems are designed to be accessible to a wide range of users, regardless of their background or abilities. Businesses should involve diverse stakeholders in the design and development process, such as domain experts, ethicists, and representatives from marginalized communities.

Purpose: Refers to the need to ensure that generative AI systems are designed with a clear and specific purpose in mind. This can help to address issues such as unintended consequences and misuse of the system. And overall, it’s important to know what we are creating and why!

Responsible use of data: Refers to the need to ensure that generative AI systems are designed to respect user privacy and data protection. One way to achieve this is by using techniques like federated learning or differential privacy.

Our role as creators is more than just solving problems. It’s about guiding the innovation ship carefully, making sure each step leads to a better and more responsible tech future. I could continue this dialogue, but for now, let’s pause and reflect on the exciting possibilities ahead! I am eagerly anticipating what the future holds, especially in conversational AI, where we wouldn’t want any bias in the model.

BharatGPT: Solving AI Challenges Responsibly

BharatGPT is designed to overcome these challenges by integrating advanced technologies and methodologies. With a foundation rooted in responsible AI, BharatGPT ensures secure, accurate, grounded, contextual, and relevant outputs through rigorous data scrutiny and adversarial training, making it reliable for various sensitive applications. It employs efficient algorithms to enhance scalability, even in resource-constrained environments. Interpretability is prioritized, leveraging techniques like attention mechanisms to ensure transparency in decision-making processes.

BharatGPT also emphasizes explainability, allowing users to understand how outputs are generated. It maintains high standards of data privacy and references, ensuring the responsible use of information. Designed with a clear and good purpose, BharatGPT avoids copyright issues and promotes inclusivity by being accessible to diverse users. By fostering creativity through reinforcement learning and enhancing user experience with interactive interfaces, BharatGPT stands out as a versatile and robust generative AI platform.

By Ankush Sabharwal 

Founder & CEO of CoRover.ai & BharatGPT