Data Democratization: Stories about data, AI and privacy

Nancy Nemes is an AI expert with 20 years of hand-on experience in implementing frameworks as a leader at Microsoft and Google. She is the founder of HumanAIze, an organization that aims to make AI more inclusive. If you are looking for some practical advice on how to implement responsible AI at the moment, then look no more – In this episode you will learn about all the necessary steps to perform ethical AI. What are the most important ethical AI best practices? What does the regulation aspect mean for the financial sector? What kind of skills are necessary to adapt to this AI-driven environment? How to identify the right metrics for understanding privacy and fairness when developing AI systems? Which kind of privacy enhancing technologies should you implement? Who are the experts that you should gather? Learn the answers to these by following the best, expert, practices!

Nicolas Passadelis is a lawyer and privacy expert who has been leading the Data Governance team at Swisscom for over four years. In this episode of the Data Democratization Podcast, Nicolas shares his best practices of data governance and provides real-life examples of how Swisscom's data ethics board works. The episode contains an amazing collection of actionable takeaways and great answers to the big questions of data governance. For example, how to implement data governance in an enterprise setting successfully? How to automate and scale compliance? How to self-assess your data protection practices? What is data ethics? How to create a data ethics framework? How to create a data ethics board? What are the six ethics principles for assessment at Swisscom? Listen to the episode and reach out to us with your own solutions to these questions by sending a voice recording to podcast@mostly.ai! Read the transcript! 

Paul Tiwald has been part of the MOSTLY AI team since the beginning. He is the mastermind behind the team's research into fairness and the idea of fair synthetic data. In this episode, you will hear about: what it's like to work in the field of artificial intelligence (spoiler: it's really fun!)how the idea of fair synthetic data came uphow to create machine learning models that are private and fair by designwhy is it so challenging to remove bias from an algorithmwhat are proxy variables, and why are they dangerouswhat is the definition of fairness, and why do we need one in the first placehow should companies start implementing fairness and ethical approaches into their AI developmentwhy it's impossible to fix bias without fair synthetic data and algorithmic fairness

We talked to one of Europe's most renowned GDPR and data experts, who shared his insights about the regulatory landscape in Europe and elsewhere and gave excellent advice for lawmakers and companies. In this episode, you will hear more about:recent regulatory developments, including Schrems II, GDPR, and the new AI-regulation proposal from the European Court of Justicewhy regulation is necessary and timely for AIwhat companies should do in light of these recent developmentshow, according to Axel's prediction, we'll end up with 20 GDPR-like lawshow to make regulation less complex and easier to comply withhow to develop a successful data strategy and to turn data privacy into a competitive edge through know-how, proactivity, and risk assessments why 95% of data transfers became illegal after Schrems II and that companies need to map all of their cross-border data transfers to comply with regulationshow synthetic data can help, since it's exempt from GDPR and Schrems II

In this action-packed episode, John Frushour, Deputy Chief Information Security Officer at New York-Presbyterian Hospital, shares his best frontline data stories. And data stories don't get any more frontline than his. From fighting the opioid crisis to the COVID-19 emergency in New York, John and his team accomplished superhuman achievements with data. In this episode, you will hear more about:the most useful leadership, data, and technical skills for CISOshow DevOps teams operate and how they mature with data science and automationbalancing data protection with data access in a healthcare settingadvanced analytics use cases: tracking opioids and sensor data in the ICUthe COVID-19 challenge in New York 2020 from a CISOs perspectiveusing synthetic data for AI/MLAI and data regulations from a CISO's perspective in 2021how to increase diversity in cybersecurity teamstips for keeping your data safe and secure and for finding the best tequila in town

Ryan is a Senior Data Scientist at Spotify with extensive experience in developing greatcustomer-centric products with the power of data. He is a data consultant and universitylecturer, passionate about data science and understanding the business side of things.In this episode you will hear about:How to start building data infrastructure in your organization, no matter the size.How to embed data best practices in your team and how to get the most value out of data science.Actionable advice on how to scale machine learning in your organization through making data accessible and automating data access processes.Data protection is a true differentiator in today’s market. You don’t need to usepersonal data for data science and fake users are better than real ones for testing yourideas.

Punit Bhatia was the Privacy and Protection Officer at ING and has tons of experience in managing privacy and data protection in large international environments. In this episode, Punit shares his best strategic advice for those looking to strategize privacy in their organization. You will hear about:the three biggest privacy challenges for enterprises and their solutionswhose responsibility should privacy be in an organization for best results why synthetic data is the way forward in data-sharinghow to approach AI from a privacy and data protection standpointwhat should companies expect in the privacy space in 2021 and beyond

In the fourth episode of the Data Democratization pod, we are talking to Sang Shin, serial entrepreneur, investment expert about his adventures in data monetization and he'll share his vision for the future of the data and privacy space. Topics discussed in the episode:How Been Choice, a privacy protection and data monetization app was banned by AppleWhat is the data privacy landscape like in Southeast Asia in 2021What are some notable privacy tech innovationsHow synthetic data made a datathon possible with data from both the private and public sectorWhat the future holds for tech innovations and why the open data movement is gaining momentum

In this episode, we will be exploring best practices around digital transformations in banking. Rebecca has decades of experience in leading financial institutions and led major transformations during her career. She has a unique, executive-level perspective to share with us about processes, data management, and how AI can be used for fairness. You can find Rebecca through her website.

Our second guest on the show is Nada Bseikri, a privacy pro from Apple Bank, New York. Nada has a unique perspective: she has both a legal and a software engineering background. Thanks to this double role, she can share unique insights into the field of data privacy. Nada shares her best tips for making privacy go beyond compliance, and we'll find out why privacy carrots are important.