How To Teach Ethics To Machines: The Fight For Good AI

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1. What are some ethical concerns with AI? There are a number of ethical concerns with AI. One major concern is that AI will automate many jobs, leading to mass unemployment. Another concern is that AI will be used to create fake videos and photos, which could be used to spread disinformation and sow discord.…… Continue reading How To Teach Ethics To Machines: The Fight For Good AI

Definition of “Designed”

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A short post on knowing when our product development can move to Sprint. When a squad member finishes an item in Sprint, they go through a list the team has written called the definition of done. It’s how they know the work should be absolutely ready for release. In the Product Development squad at Keypath…… Continue reading Definition of “Designed”

Machine Learning in EdTEch analytics

MACHINE by Tim Clapham, Mike Tosetto is licensed under CC-BY-NC-ND 4.0

As Machine Learning moves past the peak of inflated expectations, I am getting involved in dragging it towards the slope of enlightenment. Hype cycle for AI Recently I’ve been doing a lot more concept work to integrate Machine Learning into our product’s repertoire. This is all related to the learning analytics work we’re doing, and…… Continue reading Machine Learning in EdTEch analytics

The vulnerable PM

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My two years of being an EdTech Product Manager have given me so much pause for thought relating to my confidence and approach. Being vulnerable as a PM in your work and putting yourself out there has really helped make better products. It’s tough though on some days you need thick skin padded with perseverance.…… Continue reading The vulnerable PM

Which analytics for when?

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Data. It helps guide your Product squad to a higher confidence for their predicted outcomes. It starts to pull the squad away from guess work and gut checks being the only source of truth. Whether you’re looking to codify a UAT (User Acceptance Testing) report, or understand your DAU (Daily Active Users) on a platform,…… Continue reading Which analytics for when?

When RICE is too sticky – a product process reflection

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Recently I ran some UAT (User Acceptence Testing) on one of the products I’m guiding through the process and wanted to share some tips on how I used Airtable to synthesize and pull meaning quickly from the data. I will cover my mistakes and reflect on their remedies concluding why using the RICE framework early…… Continue reading When RICE is too sticky – a product process reflection

“Coffee and…”, your new meeting type for remote work

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We’ve found a new meeting style at Keypath recently and it’s had some great feedback and value. Struggling as a team to complete simple non-cognitive tasks? Simple task list things which we’ve all been asked to do seem really difficult to pick up and actually complete? What we’ve found at Keypath is a new meeting…… Continue reading “Coffee and…”, your new meeting type for remote work

Categorized as Musings

Entroponic’ cohort analysis

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How does entropy effect your cohort analysis? If over time the shape of a user’s experience becomes less about their onboarding, and more about their general use, should after a while all cohorts become a general cohort, and it be less important when they on-boarded or when they signed up? Does that mean maybe after…… Continue reading Entroponic’ cohort analysis

Engagement is queen [god save the…]”

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So as a product manager involved in designing our learning dashboard work around “actionable data by default”, I’m struggling with data events being so simple, and the learning design being so thorough. We want actionable data to allow a student and their tutor to take action and guide them towards more learning success. But if…… Continue reading Engagement is queen [god save the…]”