S3E14: 'Why We Need Fairness Enhancing Technologies Rather Than PETs' with Gianclaudio Malgieri (Brussels Privacy Hub)

The Shifting Privacy Left Podcast

Content provided by Debra J. Farber (Shifting Privacy Left). All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Debra J. Farber (Shifting Privacy Left) or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

1y ago 47:38

MP3•Episode home

Today, I chat with Gianclaudio Malgieri, an expert in privacy, data protection, AI regulation, EU law, and human rights. Gianclaudio is an Associate Professor of Law at Leiden University, the Co-director of the Brussels Privacy Hub, Associate Editor of the Computer Law & Security Review, and co-author of the paper "The Unfair Side of Privacy Enhancing Technologies: Addressing the Trade-offs Between PETs and Fairness". In our conversation, we explore this paper and why privacy-enhancing technologies (PETs) are essential but not enough on their own to address digital policy challenges.
Gianclaudio explains why PETs alone are insufficient solutions for data protection and discusses the obstacles to achieving fairness in data processing – including bias, discrimination, social injustice, and market power imbalances. We discuss data alteration techniques such as anonymization, pseudonymization, synthetic data, and differential privacy in relation to GDPR compliance. Plus, Gianclaudio highlights the issues of representation for minorities in differential privacy and stresses the importance of involving these groups in identifying bias and assessing AI technologies. We also touch on the need for ongoing research on PETs to address these challenges and share our perspectives on the future of this research.
Topics Covered:

What inspired Gianclaudio to research fairness and PETs
How PETs are about power and control
The legal / GDPR and computer science perspectives on 'fairness'
How fairness relates to discrimination, social injustices, and market power imbalances
How data obfuscation techniques relate to AI / ML
How well the use of anonymization, pseudonymization, and synthetic data techniques address data protection challenges under the GDPR
How the use of differential privacy techniques may led to unfairness
Whether the use of encrypted data processing tools and federated and distributed analytics achieve fairness
3 main PET shortcomings and how to overcome them: 1) bias discovery; 2) harms to people belonging to protected groups and individuals autonomy; and 3) market imbalances.
Areas that warrant more research and investigation

Resources Mentioned:

Read: "The Unfair Side of Privacy Enhancing Technologies: Addressing the Trade-offs Between PETs and Fairness"

Guest Info:

Connect with Gianclaudio on LinkedIn
Learn more about Brussles Privacy Hub

Send us a text

Privado.ai
Privacy assurance at the speed of product development. Get instant visibility w/ privacy code scans.
TRU Staffing Partners
Top privacy talent - when you need it, where you need it.
Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.
Copyright © 2022 - 2024 Principled LLC. All rights reserved.

Chapters

1. Shifting Privacy Left Podcast (00:00:00)

2. Unpacking Fairness and Data Protection (00:06:54)

3. Navigating Privacy-Enhancing Technology Shortcomings (00:25:00)

4. Modernizing Privacy With Fairness Technology (00:36:03)

63 episodes

#Tech #Entrepreneur #Business #News #Tech News #Privacy #Security #Engineering #Innovation #Ethics #Design #Debra J. Farber (Shifting Privacy Left Media #Development #DevOps #Data Science #Privacy Engineer #Architecture