Agenda

As companies continue to increase their investment in Machine Learning (ML) and Artificial Intelligence (AI), the focus is increasingly on how to make machine learning pervasive, creating business value by embedding it throughout a company's operations. But what does it take to get out of Proof of Concept purgatory and go from pilots to operational deployments? Do we need more technology, new roles, a different approach or what?

In this session, James will kick off the operational track with a discussion of what it takes to get value at scale from ML. He'll talk about critical issues and how to address them. He'll outline a proven approach that will let non-digital natives and even the most risk-averse companies operationalize and get value from their machine learning investments. Whatever your motivation for using machine learning and no matter how mature (or not) your machine learning team is, you'll learn how to succeed.

In this talk Dr. Anzelc will provide an overview of her own career path from particle physics PhD to insurance analytics executive to people analytics leader, providing a view of how she approached her job search and developed her career path from individual contributor to team leader. Practical advice on what to do, resources available, and other non-academic career paths of physicists will also be shared.

Due diligence prior to model deployment involves identifying bias risks, features associated with group differences, and potential missing information that could mitigate disparities. Stereotyping, or ascribing common traits to all individuals in a group, is a particular risk for machine learning models, which generate predictions based on feature similarities. Here, I explore stereotyping in regression or classification models. I demonstrate that common fairness metrics are unable to distinguish stereotyping from decisions based on arguably reasonable factors. Therefore, Paychex has developed a “due diligence” script to guide data scientists in assessing disparities. This process uses model outputs and metrics to identify areas of risk, then Shapley explainers with custom references identify features driving differences. Additional reasoning is suggested to investigate the underlying causes of inequalities, consider the effects of missing information that can mitigate risk, and the effects of sensitive features. This structured report format is suitable for stakeholders or oversight committees.

A recurring theme among analytics and data science leaders is the concern of not being able to keep up with all of the rapid change taking place – both individually and as a team. In years past, it was possible to stand up an organization largely made up of analytics generalists who would handle initiatives end to end. In today’s world, it is necessary to utilize a range of specialists focused on either specific methods or specific points in the lifecycle of an analytics and data science initiative. This talk will discuss the trends driving the need to evolve your organization’s talent model, new roles you need to consider implementing, and how they all fit together.

Ethics in AI is no longer optional, and profit and human values are not mutually exclusive. As the scale, scope and speed of AI adoption increase, we are seeing more examples of algorithmic discrimination, automated racism, ageism, deepfakes, large-scale manipulation ("nudging"), and other harms. Much of this is unintentional, but were you to deploy such an app, it could severely damage your organization's reputation and top or bottomline. Because no matter what business you are in, we are all in the business of trust. Join this session to lean how to adopt and scale AI ethically and responsibly.

Learn how Barron’s Group is leveraging automation and Machine Learning to produce direct-to-reader content and power dynamic editorial tools. This talk will focus on both the technical infrastructure necessary to automate content, and the innumerable product and editorial decisions made throughout the design, development, and deployment phases.

Over the past year, Barron’s Group has used Wordsmith to manage the text generation component of its automated story systems. Auto-generated stories are templated to summarize relevant stock-market events, and may be triggered within our own system to publish under any conditions specified. Because templates can be designed for any relevant use-case, this system is easily extended to different story topics, languages, and business segments. Barron’s currently produces stories on the state of the market at company close, and plans to expand to publish japanese-language translations, automated flash headlines, and morning stock summaries.

Barron’s Group is also in the process of building a Machine Learning-powered system for editorial assists. This tool will rely on anomaly detection performed with Amazon Sagemaker, and will alert editors when noteworthy events occur. In the short-term, this tool is intended as a time-saver for editors who would otherwise have to manually sift through price and volume data for dozens of stocks. In the long term, this kind of technology could be combined with Wordsmith’s templating functionality to produce entire headlines or articles to send off to editors, for review or publication. Here, we will focus primarily on our current process for automated article publication with Wordsmith, along with our work to this point on the Sagemaker editorial assist tool. We will review the results of our work, and discuss future steps that can be taken to expand upon these systems.

About 80% of machine learning projects fail – not because the data scientists failed, but because of the many known and unknown unknowns especially of data, but also of business and users. Consequently, the key to success is to “fail early to succeed sooner” i.e., to identify the critical break points and to focus on the use case with high chances of success. The identification of the right use cases is the purpose of a data strategy and data thinking is the process of thinking through the right solution. This interactive session introduces a method for data thinking, called Data Strategy Design. The free Data Strategy Designkit consists of 15 visual tools to collaboratively and interdisciplinary develop data strategies and data products. It is already used by hundreds of companies worldwide and by participating in this deep dive session you will also learn how to apply the method and tools to your projects. For the joint exercise we will use Miro, an online whiteboard platform, and we will work together on an exemplary machine learning project.

Modeling the risk of customer churn in large B2B organizations can be tricky. In this session, we will outline our approach to handling that complexity by developing an interpretable model and an interactive front-end tool to highlight risk factors to a team of non-technical stakeholders. We highlight the importance of transparent modeling (as opposed to black-box modeling) in B2B retention, where understanding the risk factors is as important to our customer experience stakeholders as the model itself.

Most business leaders claim to be "results driven," an orientation reflected in their embrace of a "pay for performance" philosophy. Many performance measures commonly used are “noisy” or contaminated by situational factors far removed from employees’ control. Hence, they fail to capture the actual value that employees deliver, with unintended, negative consequences for employer and employees.

Using case examples, the presenter will show how advanced analytics can address this problem by adjusting performance measures to remove the effects of random or situational factors. By distinguishing employee “value” from “performance.” organizations can deliver more effective incentives at less cost to shareholders.

Many analytics teams wait too long to start working on deployment. The most common reason they give is that they want to make sure that the model is “good enough” before they start to plan deployment. The truth is that doing so virtually guarantees project failure, or at a minimum, massive delays. Projects don’t fail because of a failure to find patterns in the data. Projects fail because models that fail to align perfectly with organizational priorities will never be embraced by the end-users of the deployed solution.