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    data crucible

    Explore " data crucible" with insightful episodes like and "S2E39: 'Contextual Responsive Intelligence & Data Minimization for AI Training & Testing' with Kevin Killens (AHvos)" from podcasts like " and "The Shifting Privacy Left Podcast"" and more!

    Episodes (1)

    S2E39: 'Contextual Responsive Intelligence & Data Minimization for AI Training & Testing' with Kevin Killens (AHvos)

    S2E39: 'Contextual Responsive Intelligence & Data Minimization for AI Training & Testing' with Kevin Killens (AHvos)

    My guest this week is Kevin Killens, CEO of AHvos, a technology service that provides AI solutions for data-heavy businesses using a proprietary technology called Contextually Responsive Intelligence (CRI), which can act upon a business's private data and produce results without storing that data.

    In this episode, we delve into this technology and learn more from Kevin about: his transition from serving in the Navy to founding an AI-focused company; AHvos’ architectural approach in support of data minimization and reduced attack surface; AHvos' CRI technology and its ability to provide accurate answers based on private data sets; and how AHvos’ Data Crucible product helps AI teams to identify and correct inaccurate dataset labels.  

    Topics Covered:

    • Kevin’s origin story, from serving in the Navy to founding AHvos
    • How Kevin thinks about privacy and the architectural approach he took when building AHvos
    • The challenges of processing personal data, 'security for privacy,' and the applicability of the GDPR when using AHvos
    • Kevin explains the benefits of Contextually Responsive Intelligence (CRI): which abstracts out raw data to protect privacy; finds & creates relevant data in response to a query; and identifies & corrects inaccurate dataset labels
    • How human-created algorithms and oversight influence AI parameters and model bias; and, why transparency is so important
    • How customer data is ingested into models via AHvos
    • Why it is important to remove bias from Testing Data, not only Training Data; and, how AHvos ensures accuracy 
    • How AHvos' Data Crucible identifies & corrects inaccurate data set labels
    • Kevin's advice for privacy engineers as they tackle AI challenges in their own organizations
    • The impact of technical debt on companies and the importance of building slowly & correctly rather than racing to market with insecure and biased AI models
    • The importance of baking security and privacy into your minimum viable product (MVP), even for products that are still in 'beta' 

    Guest Info:



    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, & learn

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