Beni Chugh

The Draft Directions propose a Statement of Purpose (SoP) to guide the actions of Regulated Entities (RE) when responding to suspected mule activity.

Our comments to the Draft Guidance on Regulatory Expectations for Data Governance

This paper provides a conceptual framework for citizen- centricity, breaking it down into system- level design features. It provides case studies of three SP- DPIs in the Indian context, and surfaces crucial learnings about their design.

As algorithmic systems increasingly shape financial decisions, this blog explores how they can de-humanize individuals by reducing them to data points.

In this blog, we trace the emerging contours of India’s domestic approach to governing AI in finance and identify the priorities that should shape the next phase of policy thinking, in light of alarm bells triggered by Mythos, and OpenAI’s cybersecurity initiative, Daybreak.

The growing prominence of Responsible and Trustworthy AI (RTAI) frameworks reflects increasing recognition of these harms. Contemporary RTAI approaches recommend safeguards across the lifecycle of AI systems to ensure explainability, transparency, contestability, auditability, and human oversight, while reducing the potential for bias and discrimination.

In this response, we present our comments to the paper. We divide our comments into three sections.

Static safeguards, targeting discrete acts of fraud, will only spur fraudsters to probe other vulnerabilities

In this response, we present four recommendations to the Draft Amendments.

The paper explores what RTAI means in the context of digital lending. The first section compiles principles of RTAI along with its essential components. The next section maps relevant tools for each principle. These tool recommendations can help lenders implement RTAI practices in their operations.