New Delhi, Jan 1 (IANS): RBI Deputy Governor M. Rajeshwar Rao on Monday said that development and deployment of AI models in the banking sector need close human supervision commensurate with the risks that can materialise from employing the technology.
He said while some of the concerns over AI pertain to design specific risks such as biases and robustness issues, others are more traditional and user specific such as data privacy, cybersecurity, consumer protection and preserving financial stability.
He said that these issues could be placed into three broad categories -- data bias and robustness, governance and transparency.
“As the adoption of AI is increasing, global efforts to develop regulatory frameworks to help guide the use of AI applications, are also increasing and greater cooperation in this process would be required.
“Our collective endeavour should be to embrace this evolution with mindfulness and a sense of responsibility, while committing to a future where technology serves as an enabler for the society at large,” Rao said during his address at the annual conference of the Indian Economic Association.
He said that the banking sector evolves, emerging technologies and AI will play a significant role in the process.
“There will be a need to ensure a supportive regulatory framework to harness its benefits while being mindful of any potential adverse impacts and therefore robust governance arrangements and clear accountability frameworks are important when AI models are deployed in high-value decision-making use cases,” Rao said.
He also emphasised the need for human oversight to address complex or ambiguous cases and to ensure that ethical considerations are taken into account.
“This would also ensure that any unintended consequences and governance issues are detected in a timely manner and addressed,” he said.
He added that the entities using AI would have to undertake rigorous validation and testing to ensure that the algorithm performs well under different conditions and is not overly sensitive to minor changes in input data.
“Regularly updating the model's training data to include a broad spectrum of conditions, ensuring adaptability to changes in the economic landscape and maintaining robust performance over time is also critical,” he said.
He added that incorporating these aspects will help in developing the public trust if we truly want to exploit the transformative potential of AI.