Team Name SuperSonar Track Banking Use Case  develop a Next-Best-Action framework, combining customer segmentation and product propensity models to prioritize relevant marketing recommendations for each customer. The project aims to improve personalization, campaign conversion, and marketing efficiency by replacing separate campaign and product assessments with a unified decisioning approach. Technology SAS Viya, BigQuery Region CA Team lead Ghazal Reshad Team members Rachael Ajayi, Herbert Ho   ... View more

anyone knows  the one below? ERROR: Windows error code: 21 in hc_disk_normal_read for E:\sastemp\_TD16080_LAPTOP-LF4NQTV2_\sastmp-000000005.sas7butl Work with pc/sas, ever session to restart sas, get this and need reboot sas. ... View more

I open SAS.   I submit a macro (just submit it, so nothing happens except it puts the macro into memory).  A line in the macro points to a formats library.   I call the macro, so the guts of the macro runs.   When the macro runs it creates a dataset.  After running the macro I do a PROC FREQ on that dataset.  All the formats are applied correctly.   Then I call the macro again.  I do not submit the macro itself again, rather I just call (run) it.  I change nothing and I do nothing else.  All I do is call the macro a second time.   Then I do that same PROC FREQ mentioned above and this time some...not all, but some...of the formats are not applied.   So the 1st time I run the PROC FREQ I see all words (the formatted values) instead of their underlying numbers.   And the 2nd time I run the PROC FREQ I still see mostly words (the formatted values), but for some of them I see their underlying numbers instead.   And every subsequent time I run it I see the mostly words but some underlying numbers instead.   IOW, it works properly after I open a new SAS session and run it for the first time.   And it doesn't work properly every subsequent time I run it.   Any idea? ... View more

The key to AI trustworthiness? Mitigating bias. This webinar explores SAS techniques designed to mitigate bias in machine learning models.     Watch the Webinar   You will learn about: Common sources of bias in machine learning models. SAS procedures and metrics used to assess fairness. Practical approaches for monitoring and reducing model bias. The questions from the Q&A segment held at the end of the webinar are listed below and the slides from the webinar are attached.   1. Which fairness metrics should I use, and how do I choose among them? There really isn’t one fairness metric that works for every situation. It depends on what you’re trying to protect against. For example, demographic parity looks at whether groups receive positive outcomes at similar rates, while equal opportunity focuses on whether qualified individuals have a similar chance of getting a positive outcome. I usually recommend starting with the business use case, identifying who could be impacted, and then choosing the metric that reflects that specific risk.   2. How can fairness assessments support Responsible AI and AI Governance initiatives? Fairness assessments turn Responsible AI principles into something we can actually measure and govern. Instead of simply saying a model should be fair, we can evaluate outcomes across groups, document what we find, establish thresholds, and monitor whether that changes over time. That gives governance teams evidence they can use for reviews, approvals, and ongoing monitoring.   3. At what stage of the AI lifecycle is bias mitigation most effective? Ideally, throughout the lifecycle. It’s much easier to address bias when you’re looking for it early, starting with how the problem is defined and what data is being used. But it shouldn’t stop there. We should assess fairness during model development, before deployment, and continue monitoring after deployment because populations, data, and outcomes can change over time.   Recommended Resources SAS Trustworthy AI Examples Github Procedures for assessing and mitigating bias in SAS Monitoring Model Fairness in SAS Model Manager A Model Card for Every Model: New Model Card Available SAS Developers - Trustworthy AI Beyond balancing data: How conditional generation makes synthetic data more useful Please see additional resources in the attached slide deck.   Want more tips? Be sure to subscribe to the Ask the Expert board to receive follow-up Q&A, slides and recordings from other SAS Ask the Expert webinars. ... View more

Team Name SASquatch Track Telecom & Media Use Case Analyze where growing electricity demand from AI data centers may outpace the grid’s ability to add new power capacity.  Technology SAS Viya (various applications) Region NA Team lead Riley McGowan Team members aubrykhuff, u64589018, (2 pending) Social media handles linkedin.com/in/Aubry-K-Huff , Siddharth Birajdar - Globe Life | LinkedIn , linkedin.com/in/DheerushaTiwari , linkedin.com/in/RileyMcGowan  Is your team interested in participating in an interview? Y Optional: Expand on your technology expertise Our team plans to use SAS Viya to combine public electricity-demand and grid-interconnection data, analyze demand and supply trends across U.S. power regions, and identify where capacity constraints may be developing. Pitch Video   Jury Video Team Photo   ... View more