On November 4th, the University of Minnesota’s Data Science Initiative, in partnership with MinneAnalytics, hosted the Women in AI & Data Science (WiADS) Conference at the McNamara Alumni Center. With over 1,000 registrants, this sold-out event celebrated the contributions of women, non-binary, and gender-diverse leaders in data science. Attendees from nearly 300 units across 230 business and academic institutions highlighted the growing commitment to increasing gender representation in the field.
The conference showcased a lineup of speakers from both academia and industry, including leaders from the University of Minnesota, Target, Optum, CHS, U.S. Bank, and more. Throughout the discussions, several key themes emerged that offered insights into both the current landscape of AI and data science and the vision for a more inclusive future. Presenters covered a wide range of topics, from the importance of ethical AI practices to the impact of AI on sectors like healthcare, finance, and retail. They shared challenges, success stories, and strategies aimed at making sure data science continues to drive positive change across industries. Here are some highlights from our favorite sessions:
Dr. Cathy O'Neil, renowned data scientist and author of Weapons of Math Destruction and The Shame Machine, delivered a powerful keynote on the ethical challenges of algorithmic influence. She emphasized how algorithms, used in critical areas like hiring, healthcare, and education, often lack transparency and fairness. Citing examples like biased healthcare algorithms and facial recognition technology, Dr. O'Neil discussed the broader ethical implications and the importance of algorithmic audits. She outlined three types of audits to ensure that algorithms are transparent, fair, and responsible in their decision-making processes: adversarial audits for legal cases, invitational audits requested by companies, and third-party audits by regulators. Dr. O'Neil’s talk reinforced the need for responsible audits to make sure data systems serve the public good, setting a high standard for ethical data practices.
Callie Flynn, Lead Data Scientist at General Mills, explored the growing applications of deep learning, particularly in how embeddings—commonly used in natural language processing (NLP) and computer vision—can be applied to tabular data. Flynn explained that embeddings help represent data in a compressed form while preserving its meaning, using the example of “LOL” standing for “laugh out loud.”
Flynn demonstrated how General Mills uses embeddings to gain deeper insights into market trends and consumer behavior. By applying deep learning to categorize stores and products more accurately, General Mills can fine-tune marketing strategies and optimize product assortments. This approach enables a more precise understanding of consumer purchasing patterns, ultimately improving product positioning. Attendees left with a solid understanding of how embeddings can be applied creatively beyond traditional text and image applications.
Wissal Jawad, Data Scientist at Element Fleet Management, discussed how generative AI can assist data scientists by automating their daily tasks. She showcased a system built using the LangChain framework on top of a large language model (LLM) to create a powerful Data Science Assistant. This assistant automates a wide range of tasks by converting natural language requests into executable code, including:
The system offers significant value to businesses and research environments by automating these tasks. It streamlines common operations in data analysis, data science, and database querying, allowing high-level users to self-serve and retrieve answers directly without needing to submit requests. This capability not only saves time but also democratizes access to data science tools, opening up the field to a broader audience and fostering greater collaboration.
Samantha Schumacher, Senior Director of Data Science & Analytics at Target, spoke about Target’s use of ML and AI to tackle challenges such as demand forecasting, inventory planning, and personalizing the digital shopping experience. One major problem they've addressed is out-of-stock items, where traditional systems often miss inaccuracies. Schumacher stated that Target developed an AI-driven solution, the Inventory Ledger, which tracks and adjusts inventory based on inferred data patterns, correcting out-of-stock errors automatically. This system uses ML models tailored to product categories, processing millions of data points in real-time to detect and fix unknown out-of-stock issues without manual intervention. The integration of these ML models into core retail applications has led to substantial improvements in inventory accuracy and sales, demonstrating the value of AI in large-scale retail operations.
This year’s WiADS Conference was not just an opportunity to learn, but also a powerful reminder of why representation matters in data science. By amplifying voices from diverse backgrounds, WiADS underscored the value of an inclusive AI and data science community—one that is better for everyone.
Krissy Tripp, Senior Director of Decision Science at Concord, shared her insights on the importance of diversity in the workplace and how Concord fosters an inclusive culture within its teams.
"I believe a core tenant of the Decision Science team's success is in our inclusivity and diversity of thought. We're purposeful in hiring team members from a variety of psychographic, demographic, academic, and professional backgrounds because that's what it takes to solve the innovative analytics and AI problems our clients bring to us."
Krissy also highlighted Concord’s commitment to excellence, regardless of labels.
“I have the privilege of working with some of the best talent in the analytics. Many of them are women, but you'll never hear any of us saying that we want to be the best women in Analytics. We're here to be the best in Analytics, no qualifiers necessary.”
At Concord, we believe that diverse perspectives lead to the best ideas. We’re proud to lead by example and demonstrate how diversity strengthens the field of data science.
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