Interviews * Session Previews

The greatest tool is the hardest to use. Machine learning is the world’s most important general-purpose technology – but it’s notoriously difficult to launch. Outside Big Tech and a handful of other leading companies, machine learning initiatives routinely fail to deploy, never realizing value. What’s missing? A specialized business practice suitable for wide adoption.

In this keynote, Machine Learning Week Founder Eric Siegel presents a six-step practice called bizML for ushering machine learning projects from conception to deployment. This disciplined approach, which is the topic of Siegel’s new book, The AI Playbook (http://www.bizml.com), serves both sides: It empowers business professionals and it establishes a sorely needed strategic framework for data professionals.

Maximize occupancy and minimize waste with advanced demand forecasting. This session presents a Python-based approach, incorporating broader economic variables and local events into predictive models for room supply, staff scheduling, and F&B services. We’ll showcase how machine learning goes beyond traditional demand indicators, providing hoteliers with the tools to anticipate fluctuations and make data-informed decisions for inventory and staffing, crucial to thriving in today’s competitive landscape.

The nemesis of most appointment-based businesses is no-shows; nowhere is this more true than in the Healthcare industry. Healthcare providers are expensive and you have to pay them even if patients don’t show up for their appointments. Join us as we explain how we began our journey into using Predictive Analytics to impact our no-show rate, the struggles we encountered along the way, and how we ended up saving $330K of lost revenue in just the first year. Embracing this model, we are now acting on predictions with lower confidence scores to save up to $3.2M in year two.

The nemesis of most appointment-based businesses is no-shows; nowhere is this more true than in the Healthcare industry. Healthcare providers are expensive and you have to pay them even if patients don’t show up for their appointments. Join us as we explain how we began our journey into using Predictive Analytics to impact our no-show rate, the struggles we encountered along the way, and how we ended up saving $330K of lost revenue in just the first year. Embracing this model, we are now acting on predictions with lower confidence scores to save up to $3.2M in year two.

Explore the intersection of logic and AI in “Decoding Paradoxes.” Learn how fallacies like the Streetlight Effect and Survivorship Bias influence AI decision-making and algorithmic biases. Discover how these paradoxes affect AI development and deployment. Join us to deepen your understanding of AI’s future. Topics include:

-Cognitive fallacies that often lead to biased models and flawed decision-making
-Strategies for training models to recognize and mitigate the influence of fallacies
-The challenges in interpreting machine learning models affected by fallacies and methods to enhance model transparency
-The ethical dimensions of cognitive fallacies in ML
Real-world case studies and examples where cognitive fallacies have played a significant role.

With the explosion of Data and AI capabilities, “Citizen Data Scientists” have popped up all across our organizations. The first reaction may be to try to squelch their activities in an effort to control the risk of inaccurate data storytelling and misinformation. But a more effective strategy is to embrace these data practitioners and enable them with tools and best practices to further accelerate the data and AI journey. Hear how Paychex has launched the Data and AI Community of Practice, leveraging proven techniques to engage the community and drive organizational value.

As a UPS Manager of 43 years, Jack Levis witnessed tremendous change. UPS became an Airline, a Global Logistics Player, and integral to the E-Commerce explosion. Of all the changes Jack witnessed, Data and Technology was arguably the largest and most impactful. As a Senior Director responsible for the Digitization and Advanced Analytics of Operations, Jack oversaw much of that technology transformation. The impact was tremendous with many hundreds of millions of dollars in cost savings while simultaneously providing additional services to customers.

In this talk, Jack will discuss lessons learned in successfully managing the breakthrough innovation. While the tools, algorithms, and mathematics are important, he will also discuss the need for psychology in terms of culture, leadership, and change management. Using examples from his journey, Jack will describe Ten Steps to Innovation through Math and Psychology.

To stay competitive as technology evolves and user interactions become more direct, it is essential to find data-driven ways to continuously improve customers’ experiences. In this talk, we’ll showcase an example of how we helped a company in the hospitality industry employ machine learning models to generate real-time predictions for how long their customers would have to wait for an order. We’ll highlight the components of the solution, which utilizes gradient boosting quantile regression, as well as key data inputs, feature engineering and selection techniques, and model monitoring considerations.

One of the biggest challenges facing AI today is the fact that, despite volumes of guidance detailing how AI should be safe, ethical, and responsible, many organizations don’t have established standards and practices to enforce responsible AI. Responsible AI requires firm model development governance standards, including algorithms allowed, processes followed, testing completed, and auditability to ensure accountability to meeting responsible AI standards. In this keynote address, FICO CAO Scott Zoldi will discuss three pillars of Responsible AI: explainable AI, ethical AI, and auditable AI in the context of life-altering decisions derived from AI models. Establishing a corporate model development standard and enforcing adherence through auditable AI not only allows meeting regulatory rules and guidance, but further establishes customer trust and safe usage of AI.

The terms “Bias” and “Unfair” have become almost synonymous, but that is not always the case. This session opens the discussion on the ethics of fair vs. unfair discrimination which is the basis for pricing insurance. We have a principle obligation to understand the loss per exposure, which includes understanding when data is or is not representative of that exposure. We’ll walk through a couple of cases to spark that conversation.