Podcast appearances and mentions of katharine jarmul

(05:46) Brought to you by Swimm.io.⁠⁠⁠⁠⁠⁠⁠⁠Start modernizing your mainframe faster with ⁠⁠Swimm⁠⁠.Understand the what, why, and how of your mainframe code.Use AI to uncover critical code insights for seamless migration, refactoring, or system replacement.Feeling uneasy about how your personal data is used, and wondering if companies are doing enough to protect it?In this episode, Katharine Jarmul, author of “Practical Data Privacy,” dives deep into one of the most critical and rapidly evolving topics today. Discover how data privacy impacts you as a user and what organizations should be doing to protect your information responsibly. Learn why simply blaming users isn't the answer and how we can build a more trustworthy technological future.Key topics discussed:Understanding Data Privacy: The meaning of data privacy and how it links to autonomy, trust, and choiceMore Than Just PII: The full scope of sensitive data needing protectionThe “Spying” Phone Feeling: How too much data collection can be used to infer sensitive detailsOrganizational Responsibility: Shifting data protection burden from users to companies building and deploying technologyPrivacy by Design: Embedding privacy into tech right from the startEssential Data Governance: Why knowing your data is key to privacyPractical Privacy Techniques: Pseudonymization, anonymization, data masking, and morePrivacy Enhancing Technologies: Exploring tools like differential privacy, federated learning, and encrypted computationAI & Privacy Challenges: Using AI responsibly with sensitive informationNavigating Privacy Laws: Understanding GDPR, data sovereignty, and global regulationsBuilding a Privacy Culture: Fostering a culture of learning, psychological safety, and risk awareness around privacyTune in to learn how we can build a safer, more responsible, and trustworthy digital future for everyone.  Timestamps:(01:20) Career Turning Points(02:14) Data Privacy Landscape(07:45) PII (Personally Identifiable Information)(11:33) Data Privacy Risk in Current Technologies(14:13) Data Utility vs Privacy(19:01) Privacy by Design(24:19) Data Governance(29:06) Retention Schedule(31:10) Data Privacy Practices & Techniques(34:09) Privacy Enhancing Technologies(38:52) Fostering Data Privacy Practice & Culture(47:05) The Legal Aspects of Data Privacy(51:10) AI and Data Privacy(56:08) 3 Tech Lead Wisdom_____Katharine Jarmul's BioKatharine Jarmul is a Principal Data Scientist at Thoughtworks Germany and author of the recent O'Reilly book Practical Data Privacy . Previously, she has held numerous roles at large companies and startups in the US and Germany, implementing data processing and machine learning systems with a focus on reliability, testability, privacy and security.She is a passionate and internationally recognized data scientist, programmer, and lecturer. Katharine is also a frequent keynote speaker at international software and AI conferences.Follow Katharine:LinkedIn – linkedin.com/in/katharinejarmulNewsletter – https://probablyprivate.comYouTube – @ProbablyPrivate

Katharine Jarmul is a AI/ML privacy and security expert, and the author of Practical Data Privacy. She joins us to chat about whether we are solving the "right" problems with AI/ML/data science, exploring what "safe", "responsible", and "ethical" AI means, and much more.

This interview was recorded for the GOTO Book Club.gotopia.tech/bookclubRead the full transcription of the interview hereKatharine Jarmul - Principal Data Scientist at Thoughtworks & Author of "Practical Data Privacy"Alyona Galyeva - Principal MLOps & Data Engineer at ThoughtworksRESOURCESKatharinetwitter.com/kjamlinkedin.com/in/katharinejarmulkjamistan.comprobablyprivate.comAlyonagithub.com/alyonagalyevalinkedin.com/in/alyonagalyevaDESCRIPTIONIntegrating privacy-enhancing technologies into software applications is an imperative step for safeguarding user data and adhering to regulatory requirements in the realm of software development. However, prior to implementation, it is vital for development teams to grasp the potential pitfalls associated with incorporating privacy technology. They must also appreciate the significance of iterative processes and the necessity of collaborative efforts to ensure compliance.Furthermore, achieving the delicate equilibrium between privacy and utility is of paramount importance. Organizations must meticulously fine-tune privacy settings, tailoring them to suit specific use cases.Additionally, alongside this core evaluation criterion, considerations such as speed and computational efficiency may enter the equation, demanding expertise in privacy engineering for successful implementation at scale.Katharine Jarmul, the author of "Practical Data Privacy," spoke to Alyona Galyeva from PyLadies Amsterdam, during which she unveiled a slew of open-source libraries and practical examples for implementing privacy technology. Katharine also explored how developers can proactively guarantee that their data science projects prioritize security by design and uphold privacy by default.The interview is based on the book "Practical Data Privacy"RECOMMENDED BOOKKatharine Jarmul • Practical Data PrivacyKatharine Jarmul & Jacqueline Kazil • Data Wrangling with PythonKatharine Jarmul & Richard Lawson • Python Web ScrapingYehonathan Sharvit • Data-Oriented ProgrammingZhamak Dehghani • Data MeshEberhard Wolff & Hanna Prinz • Service MeshPiethein Strengholt • Data Management at ScaleMartin Kleppmann • Designing Data-Intensive ApplicationsTwitterInstagramLinkedInFacebookLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

Katharine Jarmul and I chat about the Biden AI Executive Order. Enjoy!

In this podcast episode, host Ole Olesen-Bagneux interviews Katharine Jarmul - Senior Data Scientist at ThoughtWorks and author of "Practical Data Privacy" (O'Reilly) - where she offers a unique perspective on the evolving field of data privacy and its integration with technology. The conversation traces Katharine's journey from her early days at the Washington Post to her current interest in data privacy in machine learning. She emphasizes the importance of understanding the role of data in society and in people's lives, and delves into the concept of privacy engineering - explaining its multidisciplinary nature and the need to strike a balance between data utility and user privacy. The episode concludes with a discussion on the democratization of data and the future of data privacy, where Katharine envisions a world where data users have more say and a better understanding of how their data is used, which could reshape the way we approach societal challenges.

In this exclusive live talk, Luiza Jarovsky discusses Katharine Jarmul's new book "Practical Data Privacy" and topics in the context of data protection, privacy engineering, and AI, such as:PII, pseudonymization & anonymizationPrivacy attacks - what are they?Applying PETs in the context of AI applicationsFederated learningand moreKatharine is a Principal Data Scientist at Thoughtworks and the author of the book "Practical Data Privacy." She is a passionate and internationally recognized data scientist, programmer, and lecturer. I am so happy to host this talk with her and spread her knowledge and enthusiasm on privacy issues even more.Luiza Jarovsky is a lawyer, CEO of Implement Privacy, and author of Luiza's Newsletter.Read more about Luiza's work at https://www.luizajarovsky.comSubscribe to Luiza's Newsletter: https://www.luizasnewsletter.comCheck out the courses and training programs Luiza is leading at https://www.implementprivacy.comFollow Luiza on social media:LinkedIn: https://www.linkedin.com/in/luizajarovskyTwitter: https://www.twitter.com/luizaJarovskyYouTube: https://youtube.com/@luizajarovsky

Katharine Jarmul is a privacy activist and data scientist focused on privacy and security in data science workflows. She's a principal data scientist at Thoughtworks and has worked at various companies in the US and Germany before that. She is also a frequent keynote speaker at software and AI conferences. Katharine has recently published “Practical Data Privacy” (O'Reilly, 2023), in which she provides a deep dive of Privacy Enhancing Technologies (“PET”), including detailed answers to increasingly common questions: How can we actually anonymize data? How does federated learning work? Can we already leverage Homomorphic Encryption to run analysis or work with data even while it is encrypted? How can we compare and pick the most appropriate PETs? Can we use open source libraries? In our discussion: Can we bring Privacy Enhancing Technologies down to earth for smaller companies to understand and apply them on a regular basis? Are they otherwise the monopoly of Big Tech, and does this mean that a company like Meta ends up becoming the unlikely poster child for Privacy by Design? Can we really speak of a common ethical framework for AI or GenAI? How does a US/Western Europe ethical framework fit within African or Asian cultures? Can we break the convenience barrier when it comes to individual control? References: Katharine Jarmul, Practical Data Privacy (O'Reilly, 2023) Katharine Jarmul on LinkedIn Katharine Jarmul on X Ethics in eCommerce Summit Shoshana Zuboff, The Age of Surveillance Capitalism

Hugo speaks with Katharine Jarmul about privacy and security in data science and machine learning. Katharine is a Principal Data Scientist at Thoughtworks Germany focusing on privacy, ethics, and security for data science workflows. Previously, she has held numerous roles at large companies and startups in the US and Germany, implementing data processing and machine learning systems with a focus on reliability, testability, privacy, and security. In this episode, Hugo and Katharine talk about What data privacy and security are, what they aren't and the differences between them (hopefully dispelling common misconceptions along the way!); Why you should care about them (hint: the answers will involve regulatory, ethical, risk, and organizational concerns); Data governance, anonymization techniques, and privacy in data pipelines; Privacy attacks! The state of the art in privacy-aware machine learning and data science, including federated learning; What you need to know about the current state of regulation, including GDPR and CCPA… And much more, all the while grounding our conversation in real-world examples from data science, machine learning, business, and life! You can also sign up for our next livestreamed podcast recording here (https://lu.ma/4b5xalpz)! LINKS Win a copy of Practical Data Privacy, Katharine's new book! (https://forms.gle/wkF92vyvjfZLM6qt8) Katharine on twitter (https://twitter.com/kjam) Vanishing Gradients on YouTube (https://www.youtube.com/channel/UC_NafIo-Ku2loOLrzm45ABA) Probably Private, a newsletter for privacy and data science enthusiasts (https://probablyprivate.com/) Probably Private on YouTube (https://www.youtube.com/@ProbablyPrivate)

A changing regulatory environment has made it more important than ever for organizations to embed privacy in their data infrastructure. Doing so, however, can be complicated — that means data scientists have an vital role to play in ensuring privacy is a key concern from both a technical and commercial perspective.  Thoughtworker and data scientist Katharine Jarmul is eager to help fellow data scientists master privacy principles and techniques. Her new book, Practical Data Privacy, covers everything from the fundamentals of governance and anonymization through to advanced approaches to data privacy like federated learning and encrypted computation. In this episode of the Technology Podcast, Katharine joins hosts Rebecca Parsons and Birgitta Böckeler to discuss the book and explain why data scientists need to be on the frontline in the fight for privacy.  Find Practical Data Privacy on Amazon: https://www.amazon.com/Practical-Data-Privacy-Enhancing-Security/dp/1098129466    

Today I had the pleasure of interviewing Katharine Jarmul. Kathrine recently published Practical Data Privacy a book that is extremely relevant with the fast expansion of LLMs and AI Products. In this episode we touch on her experience as a data journalist, how she thinks companies are handling data privacy with the expansion of new AI tools, and how someone can prepare themselves for a career in the data privacy space.Podcast Sponsors, Affiliates, and Partners:- Pathrise - http://pathrise.com/KenJee | Career mentorship for job applicants (Free till you land a job)- Taro - http://jointaro.com/r/kenj308 (20% discount) | Career mentorship if you already have a job - 365 Data Science (57% discount) - https://365datascience.pxf.io/P0jbBY | Learn data science today- Interview Query (10% discount) - https://www.interviewquery.com/?ref=kenjee |  Interview prep questionsKatherine's Links:LinkedIn - linkedin.com/in/katharinejarmulCompany - kjamistan.comBlog - blog.kjamistan.comTwitter - https://twitter.com/kjamBook - https://amzn.to/3NhmBHl

We talked about: Katharine's background Katharine's ML privacy startup GDPR, CCPA, and the “opt-in as the default” approach What is data privacy? Finding Katharine's book – Practical Data Privacy The various definitions of data privacy and “user profiles” Privacy engineering and privacy-enhancing technologies Why data privacy is important What is differential privacy? The importance of keeping privacy in mind when designing systems Data privacy on the example of ChatGPT Katharine's resource suggestions for learning about data privacy Links: LinkedIn: https://www.linkedin.com/in/katharinejarmul/ Twitter: https://twitter.com/kjam Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

Análisis (vuestro anfitrión, solo en el escenario) del estado de la ciencia en términos de gobernanza ética de los datos y la inteligencia artificial, y la forma en la que ésta se relaciona con el marco legal de la protección de datos.  Tomamos como referencia las ponencias del reciente Ethics in eCommerce London Summit (16 de mayo de 2023), algunas píldoras extraídas del reciente Congreso Internacional de la Asociación Profesional Española de la Privacidad, los testimonios de varios líderes en Inteligencia Artificial (OpenAI, IBM) ante el senado estadounidense (17 de mayo de 2023) con relación a la necesidad de regular la incipiente tecnología y las noticias recientes en torno al mercado competitivo de la inteligencia artificial generativa y los movimientos estratégicos en las diferentes capas que conforman el ecosistema de comercio electrónico internacional.  Referencias: Ethical Commerce Alliance Ethics in eCommerce London Summit 2023 Ponentes en el ECALondon2023: Nina Müller, Stephanie Hare, Harry Farmer, Catherine King, David Manheim, Carlo Baratti, Diana Spehar, Katharine Jarmul, Wathagi Ndungu, Will Pickett, Alessandro Lovisetto, Francesco Bottigliero, Rhiannon Hanger, Borja Santaolalla, Andreas Wagner, Rodger Buyvoets, Andreas Wagenmann, Ana García, Ramiro Alvarez, Sergio Maldonado. Marc Steen: Ethics for People Who Work In The Tech Industry Erin Meyer: The Culture Map José Luis Flórez: Hacia un marco legal y ético de la Inteligencia Artificial (Masters of Privacy, abril de 2020) Marco de trabajo de ética de los datos del gobierno británico *Definición: La ética de datos tiene como objetivo garantizar que el uso y explotación de los datos se lleve a cabo de una manera que respete la privacidad, la autonomía y los derechos de las personas, y promueva la equidad, la transparencia y la responsabilidad.

Katharine Jarmul (Principal data scientist at Thoughtworks and author of Practical Data Privacy (O'Reilly, 2023)) and I chat about all things data privacy. She brings battle-tested experience and unique perspectives in the areas of ML/AI privacy, AI risk, regulation, and much more. I learned a ton, and I hope you do too! LinkedIn: https://www.linkedin.com/in/katharinejarmul/ Twitter: https://twitter.com/kjam Probably Private newsletter: https://probablyprivate.com/ ----------------------- If you like this show, give it a 5-star rating on your favorite podcast platform. Purchase Fundamentals of Data Engineering at your favorite bookseller. Check out my substack: https://joereis.substack.com/

This week, I'm joined by Katharine Jarmul, Principal Data Scientist at Thoughtworks & author of the the forthcoming book, "Practical Data Privacy: Enhancing Privacy and Security in Data." Katharine began asking questions similar to those of today's ethical machine learning community as a university student working on her undergrad thesis during the war in Iraq. She focused that research on natural language processing and investigated the statistical differences between embedded & non-embedded reporters. In our conversation, we discuss ethical & secure machine learning approaches, threat modeling against adversarial attacks, the importance of distributed data setups, and what Katharine wants data scientists to know about privacy and ethical ML.Katharine believes that we should never fall victim to a 'techno-solutionist' mindset where we believe that we can solve a deep societal problem simply with tech alone. However, by solving issues around privacy & consent with data collection, we can more easily address the challenges with ethical ML.  In fact, ML research is finally beginning to broaden and include the intersections of law, privacy, and ethics. Katharine anticipates that data scientists will embrace PETs that facilitate data sharing in a privacy-preserving way; and, she evangelizes the un-normalization of sending ML data from one company to another. Topics Covered:Katharine's motivation for writing a book on privacy for a data scientist audience and what she hopes readers will learn from itWhat areas must be addressed for ML to be considered ethicalOverlapping AI/ML & Privacy goalsChallenges with sharing data for analyticsThe need for data scientists to embrace PETsHow PETs will likely mature across orgs over the next 2 yearsKatharine's & Debra's favorite PETsThe importance of threat modeling ML models: discussing 'adversarial attacks' like 'model inversion' & 'membership inference' attacksWhy companies that train LLMs must be accountable for the safety of their modelsNew ethical approaches to data sharingWhy scraping data off the Internet to train models is the harder, lazier, unethical way to train ML modelsResources Mentioned:Pre-order the forthcoming book: "Practical Data Privacy"Subscribe to Katharine's newsletter: Probably PrivateGuest Info:Follow Katharine on LinkedInFollow Katharine on Twitter Privado.ai Privacy assurance at the speed of product development. Get instant visibility w/ privacy code scans.Shifting Privacy Left Media Where privacy engineers gather, share, & learnBuzzsprout - Launch your podcast 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.

Data Mesh Radio Patreon - get access to interviews well before they are releasedEpisode list and links to all available episode transcripts (most interviews from #32 on) hereProvided as a free resource by DataStax AstraDB; George Trujillo's contact info: email (george.trujillo@datastax.com) and LinkedInTranscript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here. You can download their Data Mesh for Dummies e-book (info gated) here.Debra's LinkedIn: https://www.linkedin.com/in/privacyguru/Debra's Shifting Privacy Left Podcast: https://shiftingprivacyleft.com/Katharine's LinkedIn: https://www.linkedin.com/in/katharinejarmul/Katharine's book: https://www.oreilly.com/library/view/practical-data-privacy/9781098129453/Samia's LinkedIn: https://www.linkedin.com/in/samia-rahman-b7b65216/Quick acronyms to know: PETs - privacy enhancing technologies; SMEs - subject matter expertsScott Note Warning: there is some nerding out about how awesome it could be if some advanced privacy approaches and PETs were implemented at a broad scale across the industry to protect individual's privacy. It's pretty early days so warning about getting your hopes up :)In this episode, guest host Debra Farber, privacy expert and host of the Shifting Privacy Left podcast facilitated a discussion with Katharine Jarmul, the author of the upcoming book Practical Data Privacy and Principal Data Scientist at Thoughtworks (guest of episode #157) and Samia Rahman, Director of Data and AI Strategy and Architecture at life sciences company Seagen (guest of episode #67).Scott note: given this is a newer area, I wanted to share my takeaways rather than trying to reflect the nuance of the panelists' views. This will be the standard for panels going forward.Scott's Top...

Learning from Machine Learning, a podcast that explores more than just algorithms and data: Life lessons from the experts. This episode we welcome Vincent Warmerdam, creator of calmcode, and machine learning engineer at SpaCy to discuss Data Science, models and much more. @learningfrommachinelearningResources to learn more about Vincent Warmerdam:https://calmcode.io/https://youtu.be/kYMfE9u-lMohttps://youtu.be/S7vhi6RjBZAhttps://github.com/koaningReferences from the Episode:You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place https://amzn.to/3Jt1qjXThe Future of Operational Research is Past https://ackoffcenter.blogs.com/files/the-future-of-operational-research-is-past.pdfSupervised Learning is great - it's data collection that's broken https://explosion.ai/blog/supervised-learning-data-collectionDeon - An ethics checklist for data scientists https://deon.drivendata.org/Hadley Wickham - https://hadley.nz/Katharine Jarmul - https://www.linkedin.com/in/katharinejarmul/?originalSubdomain=deVicki Boykis - https://vickiboykis.com/Brett Victor - https://youtu.be/8pTEmbeENF4Resources to learn more about Learning from Machine Learning:https://www.linkedin.com/company/learning-from-machine-learning/https://www.linkedin.com/in/sethplevine/https://medium.com/@levine.seth.p

Data Mesh Radio Patreon - get access to interviews well before they are releasedEpisode list and links to all available episode transcripts (most interviews from #32 on) hereProvided as a free resource by DataStax AstraDB; George Trujillo's contact info: email (george.trujillo@datastax.com) and LinkedInTranscript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here. You can download their Data Mesh for Dummies e-book (info gated) here.Katharine's LinkedIn: https://www.linkedin.com/in/katharinejarmul/Practical Data Privacy (Katharine's book in early release): https://www.oreilly.com/library/view/practical-data-privacy/9781098129453/Katharine's newsletter: https://probablyprivate.com/'Privacy-first data via data mesh' article by Katharine: https://www.thoughtworks.com/insights/articles/privacy-first-data-via-data-meshdanah boyd [sic] website: https://www.danah.org/In this episode, Scott interviewed Katharine Jarmul AKA K-Jams, Principal Data Scientist at Thoughtworks.Some key takeaways/thoughts from Katharine's point of view:Increasing privacy around data does NOT mean you have to give up value.Instead of data privacy being a blocker, it can turn nos to yeses because there is a better ability to restrict illegal/unethical use. Regulatory and legal people want to say yes, so give them the ability to do so.There are lots of tools available to enhance your data privacy now. This isn't a pipe dream. That said, don't look to replace person-to-person conversations and decisions with tech. You'll learn when to use what on your journey, it's okay to iterate :)Empower the people who know the data best with privacy tooling. Don't make them build it themselves either. They will know best most of the time - but obviously provide them a path if they...

The world of AI is changing fast. And the AI / ML space is a bit out of the ordinary for software developers. Typically in software, we can prove that given a certain situations, the code will always behave the same. We can point to where and why a decision is made. ML isn't like that. We set it up and then it takes on a life of its own. Regulators and governments are starting to step in and make rules over AI. The EU is one of the first to do so. That's why it's great to have Ines Montani and Katharine Jarmul, both awesome data scientists and EU residents, here to give us an overview of the coming regulations and other benefits and pitfalls of the AI / ML space. Links from the show Katharine Jarmul on Twitter: @kjam Katharine's site: kjamistan.com Ines Montani on Twitter: @_inesmontani Explosion AI: explosion.ai EU proposes new Artificial Intelligence Regulation: nortonrosefulbright.com The EU's leaked AI regulation is ambitious but disappointingly vague: techmonitor.ai EU ARTIFICIAL INTELLIGENCE ACT: eur-lex.europa.eu/legal-content Facial Recognition Technology Ban Passed by King County Council: kingcounty.gov On the Opportunities and Risks of Foundation Models paper: arxiv.org thoughtworks: thoughtworks.com I don't care about cookies extension: chrome.google.com Everybody hates “FLoC,” Google's tracking plan: arstechnica.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Sentry Error Monitoring, Code TALKPYTHON SignalWire Talk Python Training

Who do you think is more affected by the lack of privacy in today's applications? Kunumi.ai kicks off the second season of Volume in a special episode with Katharine Jarmul from Cape Privacy. We talked about her work, and how one goes from studying mathematics to building products that use machine learning for privacy applications. By Kunumi.ai

Privacy and data protection are not just a job for lawyers or professionals who specialize in privacy - not anymore. Technology plays an important role in ensuring personal data can remain private. Ensuring that personal data is secure but useful requires a level of skill found in data scientists.In this episode of Serious Privacy, Paul Breitbarth and K Royal searched for just such a skilled individual,Katharine Jarmul, the Head of Product at Cape Privacy, and a data scientist. Cape Privacy is a New York-based company assisting others with machine learning, data security and adding value to data. Katharine explains what data science actually is, how to keep data private, useful and valuable at the same time, and how to create synthetic data appropriately. Also a big question when it comes to powerful technology revolves around the ethics and the investment of individual technologists in the ethics of privacy.Join us as we discuss these topics and more, such as GPT-3, “this person does not exist,” the work of Cynthia Dwork, and differential privacy vs the generative model. As often happens in an episode, certain topics in privacy are revisited, mainly because they are wicked problems with no identified solution. One such topic Katharine discussed is bias in machine learning and approaches to solving bias once identified. Throughout this episode, we reference quite a few resources that we will provide the links - as always. ResourcesIAPP article on AI and synthetic data: https://iapp.org/news/a/accelerating-ai-with-synthetic-data/Federated / Collaborative Learning Introduction: https://federated.withgoogle.com/Encrypted Learning with TF-Encrypted (also can be used in a collaborative setting where we are sharing data): https://medium.com/dropoutlabs/encrypted-deep-learning-training-and-predictions-with-tf-encrypted-keras-557193284f44Europe - Ethics guidelines for trustworthy AI https://ec.europa.eu/futurium/en/ai-alliance-consultation Social MediaTwitter: @privacypodcast, @EuroPaulB, @heartofprivacy, @trustarc, @kjam, @capeprivacyInstagram @seriousprivacy

In this conversation Oracle Community Manager Javed Mohammed speaks with three AI-ML experts. Autonomous technologies such as artificial intelligence (AI) and machine learning (ML) are on the tip of every tongue in tech. But what is the difference between AI and ML? What are interesting use cases? What is “under the hood” of AI/ML and the algorithms that power ML models? We have three Subject Matter Experts who share their insights into a fascinating and at times humorous conversation. Charlie Berger, Sr. Director of Product Management for Machine Learning, AI and Cognitive Analytics at Oracle. Heli Helskyaho, CEO Miracle Finland  Oracle ACE Director, Groundbreaker Ambassador. Author. Doctoral student, University of Helsinki. Also known as HeliFromFinland. Katharine Jarmul, Head of Product at Cape Privacy, she is a Privacy activist, AI dissenter, machine learning engineer. Author and teacher for O'Reilly. Listen to learn about what makes AI and ML solutions powerful as well as the challenges we face from them. Charlie, Heli and Katharine explain some of the fundamentals about this revolutionary technology but also share personal stories which make for a memorable Podcast. Read the complete show notes here.

Before the COVID-19 crisis, we were already acutely aware of the need for a broader conversation around data privacy: look no further than the Snowden revelations, Cambridge Analytica, the New York Times Privacy Project, the General Data Protection Regulation (GDPR) in Europe, and the California Consumer Privacy Act (CCPA). In the age of COVID-19, these issues are far more acute. We also know that governments and businesses exploit crises to consolidate and rearrange power, claiming that citizens need to give up privacy for the sake of security. But is this tradeoff a false dichotomy? And what type of tools are being developed to help us through this crisis? In this episode, Katharine Jarmul, Head of Product at Cape Privacy, a company building systems to leverage secure, privacy-preserving machine learning and collaborative data science, will discuss all this and more, in conversation with Dr. Hugo Bowne-Anderson, data scientist and educator at DataCamp.Links from the showFROM THE INTERVIEWKatharine on TwitterKatharine on LinkedInContact Tracing in the Real World (By Ross Anderson)The Price of the Coronavirus Pandemic (By Nick Paumgarten)Do We Need to Give Up Privacy to Fight the Coronavirus? (By Julia Angwin)Introducing the Principles of Equitable Disaster Response (By Greg Bloom)Cybersecurity During COVID-19 ( By Bruce Schneier)

Katharine Jarmul of DropoutLabs discusses security and privacy concerns as they relate to Machine Learning. Host Justin Beyer spoke with Jarmul about attack types and privacy-protected ML techniques.

Katharine Jarmul of DropoutLabs discusses security and privacy concerns as they relate to Machine Learning. Host Justin Beyer spoke with Jarmul about attacks that can be leveraged against data pipelines and machine learning models; attack types – adversarial example, model inference, deanonymization; and how they can be utilized to manipulate model outcomes; the dangers of […]

Today on The InfoQ Podcast, Wes talks with Katharine Jarmul about privacy and fairness in machine learning algorithms. Katharine discusses what’s meant by Ethical Machine Learning and some things to consider when working towards achieving fairness. Katharine is the Co-Founder at KIProtect a machine learning security and privacy firm based in Germany and is one of the three keynotes at QCon.ai. Why listen to this podcast: - Ethical machine learning is about practices and strategies for creating more ethical machine learning models. There are many highly publicized/documented examples of machine learning gone awry that show the importance of the need to address ethical machine learning. - Some of the first steps to prevent bias in machine learning is awareness. You should take time to identify your team goals and establish fairness criteria that should be revisited over time. This fairness criteria then can be used to establish the minimum fairness criteria allowed in production. - Laws like GDPR in the EU and HIPAA in the US provide privacy and security to users and have legal implications if not followed. - Adversarial examples (like the DolphinAttack that used subsonic sounds to activate voice assistants) can be used to fool a machine learning model into hearing or seeing something that’s not there. More and more machine learning models are becoming an attack vector for bad actors. - Machine learning is always an iterative process. - Zero-Knowledge Computing (or Federated Learning) is an example of machine learning at the edge and is designed to respect the privacy of an individual’s information. More on this: Quick scan our curated show notes on InfoQ https://bit.ly/2TD3nSd You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. bit.ly/24x3IVq Subscribe: www.youtube.com/infoq Like InfoQ on Facebook: bit.ly/2jmlyG8 Follow on Twitter: twitter.com/InfoQ Follow on LinkedIn: www.linkedin.com/company/infoq Check the landing page on InfoQ: https://bit.ly/2TD3nSd

Katharine Jarmul is co-founder of KIProtect, a data security and privacy company for data science workflows in Berlin. She's been using Python since 2008 to solve and create problems. She helped form the first PyLadies chapter in Los Angeles in 2010, and co-authored an O'Reilly book along with several video courses on Python and data. She enjoys following the latest developments in machine learning, natural language processing, data privacy and ethics and workflow automation infrastructure. Together we first talked about her journey from journalism to software development. We then drifted toward her mentor and her willingness to give back to the communities. We spoke about diversity and finally tackled the topic of security and privacy.Here are the links of the show:https://twitter.com/kjam https://de.linkedin.com/in/katharinejarmulhttp://kjamistan.comhttps://kiprotect.comhttp://www.pyladies.comhttps://pydata.org/berlin2018http://heartofcode.orghttps://www.thestrangeloop.comhttps://www.swisscyberstorm.comhttps://gotober.comCreditsMusic Something Elated by Broke For Free (CC BY 3.0)Your hostSoftware Developer‘s Journey is hosted and produced by Timothée (Tim) Bourguignon, a crazy frenchman living in Germany who dedicated his life to helping others learn & grow. More about him at timbourguignon.fr.Want to be next?Do you know anyone who should be on the podcast? Do you want to be next? Drop me a line: info@devjourney.info or via Twitter @timothep.Gift the podcast a ratingPlease do me and your fellow listeners a favor by spreading the good word about this podcast. And please leave a rating (excellent of course) on the major podcasting platforms, this is the best way to increase the visibility of the podcast:Itunes - https://apple.co/2DWk5CWStitcher - http://bit.ly/2U7G931GoogleMusic - http://bit.ly/2ALx8E0Spotify - https://spoti.fi/2BLtV9pThanks!Support the show (http://bit.ly/2yBfySB)

What are the biggest challenges currently facing data security and privacy? What does the GDPR mean for civilians, working data scientists and businesses around the world? Is data anonymization actually possible or a pipe dream? Find out in Hugo's conversation with Katharine Jarmul, a data scientist, consultant, educator and co-founder of KI protect, a company that provides real-time protection for your data infrastructure, data science and AI. Links from the show KI Protect, providing real-time protection for your data infrastructure. What is GDPR? The summary guide to GDPR compliance in the UK by Matt Burgess for Wired Apple's differential privacy approach For more, see our page here

The O’Reilly Programming Podcast: Wrangling data with Python’s libraries and packages.In this episode of the O’Reilly Programming Podcast, I talk with Katharine Jarmul, a Python developer and data analyst whose company, Kjamistan, provides consulting and training on topics surrounding machine learning, natural language processing, and data testing. Jarmul is the co-author (along with Jacqueline Kazil) of the O’Reilly book Data Wrangling with Python, and she has presented the live online training course Practical Data Cleaning with Python.Discussion points: How data wrangling enables you to take real-world data and “clean it, organize it, validate it, and put it in some format you can actually work with,” says Jarmul. Why Python has become a preferred language for use in data science: Jarmul cites the accessibility of the language and the emergence of packages such as NumPy, pandas, SciPy, and scikit-learn. Jarmul calls pandas “Excel on steroids” and says, “it allows you to manipulate tabular data, and transform it quite easily. For anyone using structured, tabular data, you can’t go wrong with doing some part of your analysis in pandas.” She cites gensim and spaCy as her favorite NLP Python libraries, praising them for “the ability to just install a library and have it do quite a lot of deep learning or machine learning tasks for you.” Other links: Check out the video Building Data Pipelines with Python, presented by Jarmul. Check out the video Data Wrangling and Analysis with Python, presented by Jarmul. Jarmul is one of the founders of the group PyLadies, which focuses on helping more women become active participants and leaders in the Python open source community.

The O’Reilly Programming Podcast: Wrangling data with Python’s libraries and packages.In this episode of the O’Reilly Programming Podcast, I talk with Katharine Jarmul, a Python developer and data analyst whose company, Kjamistan, provides consulting and training on topics surrounding machine learning, natural language processing, and data testing. Jarmul is the co-author (along with Jacqueline Kazil) of the O’Reilly book Data Wrangling with Python, and she has presented the live online training course Practical Data Cleaning with Python.Discussion points: How data wrangling enables you to take real-world data and “clean it, organize it, validate it, and put it in some format you can actually work with,” says Jarmul. Why Python has become a preferred language for use in data science: Jarmul cites the accessibility of the language and the emergence of packages such as NumPy, pandas, SciPy, and scikit-learn. Jarmul calls pandas “Excel on steroids” and says, “it allows you to manipulate tabular data, and transform it quite easily. For anyone using structured, tabular data, you can’t go wrong with doing some part of your analysis in pandas.” She cites gensim and spaCy as her favorite NLP Python libraries, praising them for “the ability to just install a library and have it do quite a lot of deep learning or machine learning tasks for you.” Other links: Check out the video Building Data Pipelines with Python, presented by Jarmul. Check out the video Data Wrangling and Analysis with Python, presented by Jarmul. Jarmul is one of the founders of the group PyLadies, which focuses on helping more women become active participants and leaders in the Python open source community.

A discussion with Katharine Jarmul, aka kjam, about some of the challenges of data science with respect to testing. Some of the topics we discuss: experimentation vs testing testing pipelines and pipeline changes automating data validation property based testing schema validation and detecting schema changes using unit test techniques to test data pipeline stages testing nodes and transitions in DAGs testing expected and unexpected data missing data and non-signals corrupting a dataset with noise fuzz testing for both data pipelines and web APIs datafuzz hypothesis testing internal interfaces documenting and sharing domain expertise to build good reasonableness intermediary data and stages neural networks speaking at conferences Special Guest: Katharine Jarmul.