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Saturday Keynote at #ODSCWest Carrie Grimes Bostock - Data Science & Automated Decision Making. Automation has inputs, model(s) and potential outputs for action.
What is the goal of your models? Interpretability is important, but not the only thing. Other measures exist, but understanding your model is important to prevent outcomes, override recommendations, and to connect models to other systems - or stacks of models & systems. #ODSCWest
Example: placing racks in the Santa Clara data centre. Considerations, restrictions on space, power, future use of space, etc. Also: People install racks. They need to be able to intervene based on info that the placement model doesn't have. #ODSCWest 
Next up: Katie Malone @multiarmbandit From Presidential campaign trail to Enterprise - Building a #DataScience team @CivisAnalytics
Effective #data-driven teams: different in existing organizations than start-ups. Need to find the best places to build in data value without overturning everything else. Challenges: personal-level analytics, resource allocation, security, privacy. #ODSCWest
Business & data teams will see problems differently. Data teams need to be comfortable with trade-offs in business. Metrics can be ill-defined. Speed can be better than perfection. Important to understand adequate accuracy of models. #ODSCWest Oh yeah, and security.
Just about to kick off the keynotes at #ODSCWest - also to come today: job fair, hands-on workshops & networking. First speaker: @karpathy
Key element of #AI is that neural networks are code. It's just written by machines - #Code2point). Still need to be maintained, develop tests, refactoring and organization. #ODSC
Now up: Zachary Brown, Lead Data Scientist at S&P Global on #DataScience and #OpenEducation for the enterprise. Dayjob: #AI, Sidehustle: building #digital skills using #opensource #ODSCWest @School_GC
#DigitalLiteracy - Why? Effective work today requires a lot of associated learning. @linuxfoundation @github #VersionControl #Visualization -- continuous learning is critical. Optimal tools & processes are probably not the ones you are using now (Matlab vs. Scikit-Learn)
Need an evolving understanding for what tools are available today to get your work done. If you want your business people to work with your #datascience team, they need to understand what data is and what it can do. Also helps to avoid chasing buzzwords and bling.
Next up: Effective & ethical #AI design needs #HumanCentric #design w/ @JamesGuszcza - Data Science: written by Bill Cleveland in 2001 - coined the term. @BellLabs . Greater Statistics as a concept and going beyond. #ODSCWest
Need to think about #tech with #users to avoid #artificalstupidity (remember #unintentedoutcomes?) #HumanCentricDesign (See Don Norman - Design Everyday Things).
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Thread: Building an ethical foundation for the AI-driven future @PeriscopeData
First question: What value do you bring to your org. as a data professional? #techskills #businessoutcomes -- likely responses. Then Facebook & #unintendedoutcomes #ODSCWest
First question: What value do you bring to your org. as a data professional? #techskills #businessoutcomes -- likely responses. Then Facebook & #unintendedoutcomes #ODSCWest
Industry starting to confront #ethics in terms of daily applications of #AI algorithms. #Bias based on data - in the case of courts, potentially hundreds of years of biased decisions as part of the training set.
"I'm just an engineer" not an appropriate response. People using #AI products might not understand what or how decisions and predictions are being made, but will be making real world decisions based on them. YOU and your team might be the only ones who do.
Kicking off day 1 of #ODSCWest. I'll be tweeting through the next few days and pinging #GCDigital folks for interest or potential follow-up. #opensource #DataScience #openeducation @School_GC
First up: @asuonline will be hosting an educational analytics conference in March 2019 focusing on what we can learn from learner data and how to structure learning to better meet learner needs. #ODSCWest
#AI differential - being able to tailor products to specific users == better able to predict value to and from users. E.g. cost to deliver value to user vs. cost to company to deliver that value. Allows ruthless competitive focus - @DataRobot #ODSCWest
