India's data protection bill and impact on data product design

Making Data Science Work: Session 7

In a few months, India will pass the GDPR-equivalent law - the Personal Data Protection Bill (PDPB). Data science teams tend to be the most extensive users of data; their work impacts the users at much larger scale than the traditional analytics. This raises a number of questions:

  1. What should data scientists know about the PDPB law? What are the implications on data science?
  2. Does the law say anything about impact beyond privacy? What, if any, are the FAccT (Fairness, Accountability, Transparency) requirements under the current law?
  3. How does one cope with ambiguity and diversity in the privacy requirements across geographies?
  4. What kind of new processes and mechanisms should data scientists now plan for and build?
  5. What do you foresee coming down the line - algorithmic accountability, product liability?

Panelists:
1. Sreenidhi Srinivasan, Senior Associate, Ikigai Law
2. Shivangi Nadkarni, Co-founder & CEO, Arrka

This conversation is relevant for companies and practitioners building for compliance, including:
1. Vendor contracts
2. Local storage, including rules and processes that apply to third parties
3. Legal access to data by cloud providers

References:
1. Privacy of Business Data - A Case Study from Tally Solutions [Editor Note: We will be doing a deep dive into this in the next session]
2. Ikigai Law. A simple primer on the PDP bill in the form of FAQs (here);
3. Ikigai Law. A high-level note highlighting key requirements with a rundown of action items (here);
4. Ikigai Law. A checklist for compliance under five heads -(i) understanding scope and preparing for the law; (ii) accountability; (iii) fair and lawful processing; (iv) data principals’ rights; and (v) transferring data outside India (here);
5. Ikigai Law. A piece on data inventories being the first step towards compliance (here)
6. Scribble Data. PDP Checklist (here)

Previous session: The previous session was held on 29 July. Summary of the session is available on https://hasgeek.com/fifthelephant/operationalizing-responsible-ml/

Participation is via Zoom. Link will be shared with registered participants. Or, you can watch the livestream on this page.

About the curators: Venkata Pingali and Indrayudh Ghoshal of Scribble Data have curated this session. Scribble Data is a Bangalore/Toronto startup, active in the data community.

About Privacy Mode: Privacy Mode is an emerging umbrella for practitioners working on privacy engineering and privacy-tech. This session is sponsored by Privacy Tech and produced by The Fifth Elephant. Privacy Mode and The Fifth Elephant have collaborated to host a privacy engineering conference in January 2021 - https://hasgeek.com/fifthelephant/privacy-engineering-conference/

For further inquiries about this session, or The Fifth Elephant, contact 7676332020 or write to fifthelephant.editorial@hasgeek.com