Jennifer Shin

Understanding complex AI systems requires more than isolated insights. In our latest insight, Shichang (Ray) Zhang, Tessa H., Usha Bhalla, and Himabindu Lakkaraju propose a unified framework that integrates feature, data, and component attribution methods. This approach aims to bridge the fragmented landscape of AI interpretability, offering a holistic view into model behavior. By recognizing shared techniques across these attribution methods, the framework enhances our ability to diagnose, regulate, and trust AI systems. This unified perspective is crucial for advancing AI transparency and accountability. Explore the full insight here: 🔗 https://lnkd.in/evzaKPsW #AIInterpretability #TrustworthyAI #HarvardBusinessSchool #AITransparency

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Exciting News from Scatterplot: The MAA Journal of Data Science! We’re thrilled to highlight a new kind of storytelling in academic publishing—video conversation! 🎥 In this special multimedia editorial piece, Babson College’s Rick Cleary sits down with renowned mathematician Gil Strang for a deep and engaging conversation on: “The Four Fundamental Subspaces.” This marks an important step in expanding how we communicate data science insights—through dynamic, two-way dialogue. Check out the full video and experience this new format in action! http://spr.ly/6046f6Rqg Mathematical Association of America #DataScience #MathInAction #GilStrang #MAA #HigherEd #STEM #AcademicPublishing #Scatterplot

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Great session at #SXSW26 from my colleagues Monique (Nikki) Bhargava, Andreas Splittgerber, joined by Vanessa McKay and Zakah Alexander Ahmadi from Tripadvisor on what it takes to build responsible #AI. Key takeaway: responsible AI doesn’t slow #innovation. It powers #trust. https://lnkd.in/gzum8EFh

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The third blog post in our AI and metadata workflows series is out -- this one is written by my colleague Brian Lavoie and focusses on workflows for institutional repositories! Read and find out -- can AI help to manage institutional repository metadata? From improving deposit workflows to cleaning up legacy data, explore how AI might support IRs while keeping human expertise front and center. Our final blog post will be out next week! Stay tuned. https://lnkd.in/gMaQjTyy

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From UI frameworks to data dashboards to cutting-edge NLP, at New Math Data, we’ve been busy exploring the cutting-edge technology that moves teams and organizations forward. ✅ Angular 20 is here, and Sriprasad's article touts it as the most developer-friendly release yet. With standalone components, zoneless change detection, and a smarter build pipeline, this version doesn’t just modernize, but it simplifies and accelerates. This article has an overview of the new features with code examples to boot! 📊 QuickSight QuickStart: Chris K. shows how to go from a blank AWS account to a fully deployed BI environment in one command. No click-heavy setup. Just dashboards, data, and fast insights. 🧠 RAG Meets Computational Linguistics: Here, I discuss computational linguistics concepts and how Retrieval-augmented generation becomes far more powerful when paired with knowledge of and clever implementation of the fundamentals. This isn’t just theory. It’s how you build smarter RAG systems. We're always aiming to ship ideas that matter and push us forward, as well as systems that scale. Dive into the details or reach out if you're solving similar challenges! https://lnkd.in/gTzHibvk https://lnkd.in/gawTbraM #Angular #AWSQuickSight #NLP #RAG #DataEngineering #SoftwareDevelopment #MachineLearning #AI #CloudComputing #NewMathData

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📢 New Blog Post: NeurIPS Datasets & Benchmarks Track - From Art to Science in AI Evaluations we just published a new blog post that provides an update on the 2025 Datasets and Benchmarks Track, reflecting on how the new hosting and metadata requirements affected submissions and the review process. We present submission statistics, survey findings from 851 authors and 155 reviewers, and identify areas requiring continued development. This differs from our earlier post on the review process by focusing on empirical outcomes rather than procedural changes. with Konstantina Palla , Francesco Locatello , Joaquin Vanschoren , Megan Risdal https://lnkd.in/eHxQTvnh

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At Open Repositories 2025, Jenny Li delivered an engaging presentation on the innovative use of AI in metadata creation with the TurboCurator tool. She also made a compelling case for the future role of AI in data stewardship. #ResearchDataEcosystem

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Ohio State University's AI Fluency initiative (https://lnkd.in/etB8EyMB) sets an ambitious goal: every undergraduate will graduate with practical generative AI skills by 2029. Let's unpack this: ✅ Strong Points: ▪️Bold, clearly articulated institutional vision. ▪️Multi-layered integration (Gen Ed courses, discipline-specific modules, innovation opportunities). ▪️Dedicated resources and infrastructure (faculty support via Drake Institute). ▪️Commitment to hands-on innovation (hackathons, prototyping workshops). ⚠️ Areas of Concern: ▪️Faculty workload: Realistic expectations for widespread course redesign and adoption? ▪️Standardization & Assessment: What does "AI fluency" mean in practice across diverse fields? ▪️Curriculum Overload: Risk of student pushback if mandatory modules aren't streamlined or clearly valuable. ▪️Equity and Faculty Incentives: What's the motivation for faculty, especially those skeptical or hesitant about AI integration? What if they're not on board with using AI in the classroom, or it doesn't make sense for their course? ▪️Soft Skills and Ethical Frameworks: How thoroughly are ethics, interpretability, and responsible AI practices embedded? OSU's vision is exciting, but effective execution will be key. I'm curious: What does successful "AI fluency" look like in your discipline? And how should institutions balance ambition with realistic implementation? Let's talk!

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In her President’s Corner column for "Amstat News," ASA President Jeri Mulrow reflects on the rapid rise of large language models, generative AI, and the evolving landscape of statistical practice.  She emphasizes that statisticians are uniquely positioned at the intersection of extraordinary opportunity and profound responsibility and invites readers to collective action, noting that the decisions we make today will shape the profession for decades to come. Read “Stewards of Uncertainty: Our Role in the Age of AI.” She wants to hear from you and work toward a future in which data and statistical thinking remain an essential part of decision-making and discovery. https://lnkd.in/ehM7NibB

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🚀 “Simulated Patron Interviews: How I Trained ChatGPT to Speak Like Fulton Library Users in Atlanta https://lnkd.in/e8j7Te_e By integrating Simulated Patron Interviews into research design, we can systematically explore behavioral modeling, perception testing, and linguistic sentiment analysis without requiring immediate human focus groups. This approach introduces a pre-field validation layer — allowing researchers to evaluate conceptual soundness, refine questions, and anticipate respondent diversity using LLM-based personas. What makes this simulation academically relevant is its methodological potential. Dr. Patrick Fennell, this was precisely the component we discussed during my last guest lecture at Kennesaw. It could become a valuable pedagogical and methodological case study for your upcoming semester. Cc: Dr. Luis Torres, Dr. Asad Ahmad Khan, MSIT, Ana A. Galindo, MSMR, Dr. Jagdish Sheth #KennesawStateUniversity #GeorgiaGwinnettCollege #FultonCountyLibrary #UniversidadPanamericana #AIResearch #AIinEducation #HumanAICollaboration #LibraryInnovation #DigitalTransformation

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📢 AI in scholarly publishing is shifting from tools to operations—and it matters. In our latest blog, KGL's Hong Zhou reviews the literature to unpack why the real transformation isn’t about adding more AI features, but about redesigning workflows and embedding AI as an operating layer that executes work, manages exceptions, and enforces policy across systems. For publishers and content providers, this shift has big implications: ✨ Redesign beats automation ✨ Trust becomes infrastructure ✨ ROI needs to be tied to outcomes ✨ Computational economics will shape scale ✨ Discovery is moving into AI answer engines 👉 Read the full article: https://lnkd.in/eREuDnNA

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An interesting approach to the multi-turn chart editing task. LLM-supported editing of complex artifacts will be one of the major applications of LLMs, embedded within broader consumer and business workflows. As a result, evaluation methods for these types of interactive, multi-step editing tasks will become increasingly important and widely adopted. https://lnkd.in/gThNvenR

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