The Shifting Privacy Left Podcast

This week, I welcome philosopher, author, & AI ethics expert, Reid Blackman, Ph.D., to discuss Ethical AI. Reid authored the book, "Ethical Machines," and is the CEO & Founder of Virtue Consultants, a digital ethical risk consultancy. His extensive background in philosophy & ethics, coupled with his engagement with orgs like AWS, U.S. Bank, the FBI, & NASA, offers a unique perspective on the challenges & misconceptions surrounding AI ethics.
In our conversation, we discuss 'passive privacy' & 'active privacy' and the need for individuals to exercise control over their data. Reid explains how the quest to train data for ML/AI can lead to privacy violations, particularly for BigTech companies. We touch on many concepts in the AI space including: automated decision making vs. keeping "humans in the loop;" combating AI ethics fatigue; and advice for technical staff involved in AI product development. Reid stresses the importance of protecting privacy, educating users, & deciding whether to utilize external APIs or on-prem servers.
We end by highlighting his HBR article - "Generative AI-xiety" - and discuss the 4 primary areas of ethical concern for LLMs:

1. the hallucination problem;
2. the deliberation problem;
3. the sleazy salesperson problem; &
4. the problem of shared responsibility

Topics Covered:

• What motivated Reid to write his book, "Ethical Machines"
• The key differences between 'active privacy' & 'passive privacy'
• Why engineering incentives to collect more data to train AI models, especially in big tech, poses challenges to data minimization
• The importance of aligning privacy agendas with business priorities
• Why what companies infer about people can be a privacy violation; what engineers should know about 'input privacy' when training AI models; and, how that effects the output of inferred data
• Automated decision making: when it's necessary to have a 'human in the loop'
• Approaches for mitigating 'AI ethics fatigue'
• The need to backup a company's stated 'values' with actions; and why there should always be 3 - 7 guardrails put in place for each stated value
• The differences between 'Responsible AI' & 'Ethical AI,' and why companies seem reluctant to talk about ethics
• Reid's article, "Generative AI-xiety," & the 4 main risks related to generative AI
• Reid's advice for technical staff building products & services that leverage LLM's

Resources Mentioned:

• Read the book, "Ethical Machines"
• Reid's podcast, Ethical Machines

Guest Info:

• Follow Reid on LinkedIn

Send us a text

Copyright © 2022 - 2024 Principled LLC. All rights reserved.

This week, we welcome Lipika Ramaswamy, Senior Applied Scientist at Gretel AI, a privacy tech company that makes it simple to generate anonymized and safe synthetic data via APIs. Previously, Lipika worked as a Data Scientist at LeapYear Technologies, and was the Machine Learning Researcher at Harvard University's Privacy Tools Project.

Lipika’s interest in both machine learning and privacy comes from her love of math and things that can be defined with equations. Her interest was piqued in grad school and accidentally walked into a classroom holding a lecture on Applying Differential Privacy for Data Science. The intersection of data combined with the privacy guarantees that we have available today has kept her hooked ever since.

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Thank you to our sponsor, Privado, the developer-friendly privacy platform
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There's a lot to unpack when it comes to synthetic data & privacy guarantees, as she takes listeners on a deep dive of these compelling topics. Lipika finds elegant how privacy assurances like differential privacy revolve around math and statistics at their core. Essentially, she loves building things with 'usable privacy' & security that people can easily use. We also delve into the metrics tracked in the Gretel Synthetic Data Report, which assesses both 'statistical integrity' & 'privacy levels' of a customer's training data.

Topics Covered:

• The definition of 'synthetic data,' & good use cases
• The process of creating synthetic data
• How to ensure that synthetic data is 'privacy-preserving'
• Privacy problems that may arise from overtraining ML models
• When to use synthetic data rather than other techniques like tokenization, anonymization, aggregation & others
• Examples of good use cases vs poor use cases for using synthetic data
• Common misperceptions around synthetic data
• Gretel.ai's approach to 'privacy assurance,' including a focus on 'privacy filters,' which prevent some privacy harms outputted by LLMs
• How to plug into the 'synthetic data' community
• Who bears the responsibility for educating the public about new technology like LLMs and potential harms
• Highlights from Gretel.ai's Synthesize 2023 conference

Resources Mentioned:

• Join Gretel's Synthetic Data Community on Discord
• Watch Talks on Synthetic Data on YouTube

Guest Info:

• Connect with Lipika on LinkedIn

Send us a text

Copyright © 2022 - 2024 Principled LLC. All rights reserved.

In this conversation with Markus Lampinen, Co-founder and CEO at Prifina, a personal data platform, we discuss meaty topics like: Prifina’s approach to building privacy-respected apps for consumer wearable sensors; LLMs (Large Language Models) like Chat GPT; and why we should consider training our own personal AIs.
Markus shares his entrepreneurial journey in the privacy world and how he is “the biggest data nerd you’ll find.” It started with tracking his own data, like his eating habits, activity, sleep, and stress, an then he built his company around that interest. His curiosity about what you can glean from one's own data made him wonder how you could also improve your life or the lives of your customers with that data.

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Thank you to our sponsor, Privado, the developer-friendly privacy platform
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We discuss how to approach building a privacy-first platform to unlock the value and use of IOT / sensor data. It began with the concept of individual ownership: who should actually benefit from the data that we generate? Markus says it should be individuals themselves.
Prifina boasts a strong community of 30,000 developers who align around common interests - liberty, equality & data - and build and test prototypes that are gathering and utilizing the data working for individuals, as opposed to corporate entities. The aim is to empower individuals, companies & developers to build apps that re-purpose individuals' own sensor data to gain privacy-enabled insights.

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Listen to the episode on Apple Podcasts, Spotify, iHeartRadio, or on your favorite podcast platform.
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Topics Covered:

• Enabling true, consumer-grade 'data portability' with personal data clouds (a 'bring your own data' approach)
• Use cases to illustrate the problems Prifina is solving with sensors
• What are large language models (LLM) and chatbots trained on them, and why they are so hot right now
• The dangers of using LLMs, with emphasis on privacy harms
• How to benefit from our own data with personal AIs
• Advice to data scientists, researchers and developers regarding how to architect for ethical uses of LLMs
• Who's responsible for educating the public about LLMs, chatbots, and their potential harms & limitations

Resources Mentioned:

• Learn more about Prifina
• Join Prifina's Slack Community: Liberty.Equality.Data

Guest Info:

• Follow Markus on

Send us a text

Copyright © 2022 - 2024 Principled LLC. All rights reserved.

This week, I continue my conversation with Jim Nasr, CEO of Acoer about privacy and using distributed ledger technology (DLT). We discuss his work leading The HBAR Foundation's Privacy Market Development Fund and the trends he sees across grant applicants. We also chat about the collapse of FTX and the ripple effect it’s had on the crypto space.

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Thank you to our sponsor, Privado, the developer-friendly privacy platform
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Jim tells us about the types of innovations The HBAR Foundation seeks to fund; why privacy & security usability is an imperative; uses cases for decentralized identifiers (DIDs) and new "DID methods" like PKH. We also discuss FTX's collapse and how to provide real transparency and data regulation in DLT technology.

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Listen to the episode on Apple Podcasts, Spotify, iHeartRadio, or on your favorite podcast platform.
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Topics Covered:

• The HBAR Foundation’s search for projects to fund that enhance privacy usability
• Exciting privacy use cases Jim has seen using Hedera's DLT, including those that enable high-value, privacy-preserving transactions
• What went wrong with FTX and what we can learn from it's collapse
• How decentralized identity can enable the next iteration of web privacy
• The tech behind MetaMask's Snap software that allows anyone to safely extend capabilities of their wallet

Resources Mentioned:

• Learn about Acoer
• Learn about The HBAR Foundation
• Read about The HBAR Foundation's Privacy Market Development Fund

Jim Nasr’s Info:

• Follow Jim on LinkedIn
• Follow Jim on Twitter

Follow the SPL Show:

• Follow us on Twitter
• Follow us on LinkedIn
• Check out our website

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Copyright © 2022 - 2024 Principled LLC. All rights reserved.

Victor Platt is a Senior AI Security and Privacy Strategist who previously served as Head of Security and Privacy for privacy tech company, Integrate.ai. Victor was formerly a founding member of the Risk AI Team with Omnia AI, Deloitt’s artificial intelligence practice in Canada. He joins today to discuss privacy enhancing technologies (PETs) that are shaping industries around the world, with a focus on federated learning.

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Thank you to our sponsor, Privado, the developer-friendly privacy platform
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Victor views PETs as functional requirements and says they shouldn’t be buried in your design document as nonfunctional obligations. In his work, he has found key gaps where organizations were only doing “security for security’s sake.” Rather, he believes organizations should be thinking about it at the forefront. Not only that, we should all be getting excited about it because we all have a stake in privacy.

With federated learning, you have the tools available to train ML models on large data sets with precision at scale without risking user privacy. In this conversation, Victor demystifies what federated learning is, describes the 2 different types: at the edge and across data silos, and explains how it works and how it compares to traditional machine learning.We deep dive into how an organization knows when to use federated learning, with specific advice for developers and data scientists as they implement it into their organizations.

Topics Covered:

• What 'federated learning' is and how it compares to traditional machine learning
• When an organization should use vertical federated learning vs horizontal federated learning, or instead a hybrid version
• A key challenge in 'transfer learning': knowing whether two data sets are related to each other and techniques to overcome this, like 'private set intersection'
• How the future of technology will be underpinned by a 'constellation of PETs'
• The distinction between 'input privacy' vs. 'output privacy'
• Different kinds of federated learning with use case examples
• Where the responsibility for adding PETs lies within an organization
• The key barriers to adopting federated learning and other PETs within different industries and use cases
• How to move the needle on data privacy when it comes to legislation and regulation

Resources Mentioned:

• Take this outstanding, free class from OpenMined: Our Privacy Opportunity

Guest Info:

• Follow Victor on LinkedIn

Follow the SPL Show:

• Follow us on Twitter
• Follow us on LinkedIn
• Check out our website

Send us a text

Copyright © 2022 - 2024 Principled LLC. All rights reserved.