# #247: Navigating the Ethical Waters of Data and AI - Insights from NAB's Head of Privacy and Data Ethics

Page: https://stenobird.com/podcast/data-futurology-leadership-and-strategy/247-navigating-the-ethical-waters-of-data-and-ai-insights-from-nab-s-head-of-privacy-and-data-ethics
Text version: https://stenobird.com/podcast/data-futurology-leadership-and-strategy/247-navigating-the-ethical-waters-of-data-and-ai-insights-from-nab-s-head-of-privacy-and-data-ethics.md
Podcast: [Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science](https://stenobird.com/podcast/data-futurology-leadership-and-strategy)
Published: 2023-10-04T00:04:55+00:00
Episode link: https://podcasters.spotify.com/pod/show/datafuturology/episodes/247-Navigating-the-Ethical-Waters-of-Data-and-AI---Insights-from-NABs-Head-of-Privacy-and-Data-Ethics-e2a4iim
Audio file: https://anchor.fm/s/3fab060/podcast/play/76744726/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2023-9-4%2F735def99-e26d-94e9-e074-29ec626af572.mp3
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Duration seconds: 2799

## Resource

Data ethics and privacy are not just regulatory hurdles but essential frameworks for building public trust and ensuring long-term technological value. Jade Haar explains how to move beyond compliance toward a proactive culture of fairness and data minimization.

## Highlights
- Main idea: Privacy and data ethics serve as a 'human rights flag' to protect individuals in an era of massive data breaches
- Failure mode: Assuming datasets are unbiased; the starting point for any analysis must be the assumption that bias exists
- Practical takeaway: Implement data minimization by only collecting what is strictly necessary for downstream analysis to reduce risk
- Main idea: Ethical frameworks should be viewed as an 'enablement' tool that builds user confidence rather than a bottleneck for innovation
- Practical takeaway: Transparency in both successful and failed use cases is critical for maintaining community trust and awareness

## Topics

Data Ethics, Privacy Law, Artificial Intelligence, Data Governance, Algorithmic Bias, Data Minimization, Information Security, Digital Trust

## Chapters
- 1:00 — Defining the Scope of Data Ethics: An introduction to the intersection of privacy, data relevance, and the ethical treatment of collected information.
- 4:30 — The Origin of a Privacy Advocate: Jade Haar discusses her background in social justice and how her values shaped her approach to privacy as a human right.
- 8:10 — Corporate Engagement with Technology: A look at how major institutions like NAB, AWS, and Microsoft engage with technological advancements and stakeholders.
- 11:40 — Algorithmic Transparency: Discussing the evolution of modeling in banking and the importance of helping people understand algorithmic decisions.
- 15:00 — The Double-Edged Sword of Innovation: Exploring the balance between technological optimism and the necessity of informed consent.
- 18:30 — Frameworks for Fairness and Impact: The importance of conducting impact assessments and using established frameworks to navigate fairness and transparency.
- 25:30 — Mitigating Bias in Model Training: Strategies for ensuring representative data collection and identifying inherent biases in training sets.

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## Transcript

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