A Human-Centered approach will lead to a better Responsible AI framework

The rise of Responsible AI and ChatGPT prominence among enterprises and regulatory bodies raises pertinent questions.

New Update

In a landmark event in the US, several Big Tech companies came together recently to officially commit to the ethical use of AI to ensure safety, security and transparency to all stakeholders. Here in India, telecom regulator TRAI has expressed an urgent need to form an independent group and create a framework to promote and regulate the development of Responsible AI across all industry sectors.


What is Responsible AI and why has it gained so much importance among enterprises and regulatory bodies? The term has existed for some time now; technology and best practices have constantly been discussed and evaluated by authorities the world over ever since instances of bias were discovered in AI-based enterprise systems. There have been occasions where algorithms were found to be biased in their recommendations, insights and predictions. This ranged from hiring anomalies, erroneous face recognition caused by faulty profiling, to violation of user privacy. In every instance, a human has been affected. Responsible AI is a practice that endeavors to ensure that people are not negatively impacted by errors that may exist or get amplified in AI systems.

ChatGPT, for instance, gave everybody a first-hand experience of the power of the technology and its possibilities. It also gave glimpses of imperfections like gender bias, stereotyping and the need for certain guardrails to be put in place. In a ‘hyper-Generative AI’ world, it is the need of the hour to build a framework that ensures that every stakeholder’s interests are protected while providing enriching experiences to end users.

The biggest technology companies in the world have taken the people-first approach and committed to Responsible AI. That’s a cue in itself to consider bringing AI solutions under the ethical ambit. Businesses that are still skeptical about Responsible AI should take the time to consider the following reasons why guardrails are of utmost importance for successful AI deployments – within a workplace, for technology partners and customers.

  • Ethical considerations: AI-powered solutions have allowed businesses to automate several processes. However, biases in the algorithms could lead to unfair outcomes that will get amplified over time, if left unchecked. ‘Fairness’ is not only about the algorithmic model but also the entire business process. Responsible AI requires organizations to ensure fairness, transparency, and accountability throughout the AI lifecycle. Documenting scenarios where fairness could be compromised and incorporating ethical guidelines can help organizations prevent biased decision-making and discriminatory practices.
  • Legal and regulatory compliance: This aspect usually trails behind technology, which is rapidly advancing and getting deployed as well. The adoption of AI requires the development of legal and regulatory frameworks to govern its use. Responsible AI practices can help organizations stay compliant with these regulations, or guardrails, reducing legal risks and potential liabilities that could otherwise prove costly for a business. Proactively address privacy and data protection, ensure transparency and organizations will be able to maintain their reputation in an evolving regulatory landscape.
  • Mitigate bias: AI systems can inadvertently perpetuate biases present in training data, which could lead to discriminatory outcomes. Besides, language models are not a one-time process. Responsible AI emphasizes the need to identify and remove such bias in AI algorithms. This requires organizations to use AI models that are trained on diverse datasets. They also need to be regularly audited and documented for bias, and fine-tuned to minimize disparities. Constantly checking the model verification process and its underlying assumptions can help detect any mismatch between the model and the real world.
  • Trust: This is a vital factor in the successful adoption of AI-driven solutions. Customers should be able to trust an organization’s AI application. Responsible AI practices will help organizations build and maintain this trust by being transparent about data usage and the decision-making processes of AI systems. Trust and transparency will help build customer confidence and provide a competitive advantage. A trust score, for instance, of AI applications could help customers compare products on the basis of fairness, explain ability, security, privacy, safety, etc. Such a score would help them evaluate the maturity of applications before adopting them.
  • Sustainability: Organizations have a responsibility to consider the broader social impact of their AI applications. Responsible AI enables organizations to align their AI strategies with societal values and contribute positively to the communities they serve. By prioritizing social impact, organizations can leverage AI to address critical challenges such as climate change and resource allocation. Embracing responsible AI practices will also help organizations enhance their reputation and achieve long-term sustainability goals.
  • Risk management: AI systems are not immune to errors, vulnerabilities, or adversarial attacks. Responsible AI practices involve robust risk management strategies to identify and mitigate potential risks associated with AI deployment. Organizations should prioritize security, privacy, and system resilience checks to ensure that their AI systems are protected against potential threats. Organizations can safeguard their operations by addressing these risks proactively to protect their stakeholders and ensure business continuity.

AI-powered applications have an impact on the professionals working on them as well as the society at large that benefits from the solutions offered by them. Therefore, it is very important to have checks and balances in place to ensure that services are free of bias, fair, and not discriminatory. Failure to have such a human-centric approach to rectify anomalies in AI models will hurt businesses, and their reputations and possibly lead to regulatory action. Adding the component of Responsible AI, like a vital check post, will help organizations build applications for customers that are based on fairness, trust, and transparency.

-By Jayachandran Ramachandran, Senior Vice President - Artificial Intelligence Labs, Course5 Intelligence

chatgpt responsible-ai bard