Session Format: Virtual

CE: 3.0

Date/Time:  To be held April 29^th, 2022, in Seattle, WA – 2:00 pm PDT

Presenters:    

Dr. Dyan Ferraris

Bo Young Lee

Abstract:

This interactive seminar will exhibit how Uber approaches Inclusive Leadership training in the corporate setting, providing (1) a view into how Uber balances science and practice in training content, (2) illustrative stories of how leaders interact with content, (3) our learnings along the way. Participants will undergo an evidence-based Inclusive Leadership training and learn practical and tangible ways to bring the content to life. The seminar is intended to create a safe space for those looking to learn and dialog about advancing the application of Leadership Theory and Inclusion Models.

Session Length: 3 hours 

Learning Objectives:

• How does Uber make Inclusive Leadership training as impactful and actionable as possible?
• What is inclusive leadership and where am I in that journey?
• What evidence-based inclusive leadership models have translated effectively at Uber? Why? How can I bring this into my own workplace?

Detailed Description: This session on Inclusive Leadership will be delivered in an interactive lecture format. Participants will engage in a few ways to help deepen the learning: individual reflection, small group or paired discussion, and large group discussion. Presenter will use a story-telling approach to elucidate applied examples. Confidentiality and co-creating a safe space are important to this session and is expected of all participants. All materials and activities will be accessible remotely.

Presenter bios:

Dyan Ferraris, Ph.D. is a social-organizational scientist dedicated to social justice and anti-racism at work. She currently serves as the Head of People Science and Strategy on Uber’s Global Diversity & Inclusion team. Ferraris’ academic research focuses on understanding how gender and race-based stereotypes impact workplace outcomes such as hiring, performance, and promotion decisions. She brings a scientist-practitioner approach to her applied work, and has partnered with non-profits, Fortune 500 companies, and government institutions in alleviating bias, increasing objectivity, implementing data-based strategies, and bringing equity and fostering inclusion with leaders, teams, and organizations dedicated to positive change. Dyan's work has been featured in the Academy of Management, the Journal of Neurosurgery, and is currently SIOP’s 2021 workplace trends champion for social justice. Dyan received her Ph.D. from Columbia University, Teachers College and her B.A. from Mount Holyoke College. 

Bo Young Lee (pronouns: she/her/they) is Uber’s Chief Diversity & Inclusion Officer, and leads Uber’s Diversity, Equity and Inclusion efforts in the company, with stakeholders, and in the communities where Uber operates. Bo partners with senior leadership including its CEO, Dara Khosrowshahi, to build a work culture where radically diverse and inclusive teams drive innovation, accelerate growth, and increase connection to customers and earners. This begins with building a work culture and systems where all employees can excel and grow to their highest potential. Prior to joining Uber, Bo was the first Global Diversity and Inclusion Officer for the Risk and Insurance Services businesses for Marsh McLennan where we oversaw D&I strategy for 40,000 employees across 89 countries. Bo also launched and led Hewitt Associates’ Global Emerging Workforce Solutions consulting practice and held diversity leadership roles at Ernst & Young and National Grid. Bo also served as a Director of Advisory Services at Catalyst, the leading non-profit focused on the advancement of women in business. As a consultant and thought leader, Bo has enabled dozens of clients to achieve their diversity & inclusion goals. Past clients include Marriott International, Northern Trust, John Deere, Allstate, Booz & Co., Discover, Aon, Human Rights Campaign, McKesson, and many others. Bo has an MBA with distinction from New York University’s Stern School of Business and a BBA (magna cum laude) from the University of Michigan’s Ross School of Business. She is a frequently sought-after speaker and has been featured in The Wall Street Journal, Bloomberg BusinessWeek, Tech Crunch, The Huffington Post, MSNBC, and other media outlets and conferences.

Session Format: This seminar will have up to 18 in-person attendees and 50 virtual attendees. 

CE: 3.0

Date/Time:  To be held April 29^th, 2022, in Seattle, WA and Virtually – 8:00 am PDT 

Presenters:    

Dr. Scott Withrow
Dr. Rachel T. King
​Dr. Isaac Thompson

Abstract:

The modern world is increasingly driven by algorithms, machine learning, and data science. There are certainly many dangers and pitfalls that abound in using these tools and models that may cause issues in recruitment, selection, and promotion (and more). In this session we will cover some models and data science techniques so that you can not only avoid these common pitfalls but leverage these models to enhance validity and make quality decisions about data. We will be presenting two practical examples of machine learning–one using a supervised model and one using an unsupervised model.

Description:  

We will continue to see the growth of decisions being made by machine learning models across all facets of modern life. While these models grow in popularity so too do the reports of bad (and sometimes illegal) decisions being made by or with these models. In our highly regulated industry, it is essential that anyone applying or considering using machine learning understands how the models work, when it’s appropriate to use them, and what the benefits and drawbacks are. This session will cover some common machine learning topics and present two practical examples of machine learning by IO psychologists.

Session Length: 3 hours

Learning Objectives: 

• What are the basic machine learning models relevant to an I-O Psychologist? 
• How can data science and machine learning help us understand data? 
• What are some practical applications of machine learning in the IO world? 
  • Bayesian Linear Regression with optimization formula for enhancing validity and avoiding adverse impact.
  • Novel NLP methods to classify and make sense of unstructured text without the need of human labels (unsupervised deep learning).

Detailed Description: 

The goal of this session is for participants to come away with a general understanding of what machine learning is and what it can be used for. For example, what is the difference between a supervised and an unsupervised model? What kind of specific tools fall into those buckets and what does that mean to a practitioner thinking about using these models? We will then cover some basic data quality measures. For example, using bootstrapping in small samples, using different cross-validation strategies to give better statistics to clients (ranges of plausible validity), common data issues, and best practices for data handling. Finally, we will wrap up the session with two actual applications of machine learning in the IO world. These applications will be discussed in the seminar with data and code (R and Python) being disseminated for those that want to dip a toe into machine learning.

Presenter biographies:

Dr. Scott Withrow is the senior psychometrician at Infor Talent Science. His primary duties are to lead a team of people in developing new assessments and assessment strategy, calibrating the output from the assessments to best fit client organizations following job analysis, and conducting post-hoc studies to validate the continuing effectiveness of the tools. He uses a wide variety of statistical tools from Item Response Theory to Machine Learning to accomplish these goals.

Dr. Rachel T. King is a Senior Data Scientist at Modern Hire. Her work at Modern Hire involves building machine learning models to improve prediction in pre-hire assessments in addition to working with clients to implement these models. Her work often uses data science techniques such as natural language processing and machine learning. Additionally, she has worked extensively with the programming languages R and python. She holds a PhD in Industrial-Organizational Psychology from Bowling Green State University.

Dr. Isaac Thompson is a Principal Data Scientist at Modern Hire where he develops AI solutions applied to assessments and interviews, helping power selection and assessment for over 50% of the Fortune 500 companies. Broadly, his work focuses on the development of novel AI applications and linking it within traditional psychometrics with expertise in deep learning, machine learning, R, Python, Bayesian statistics, and measurement.

Session Format: This seminar will have up to 18 in-person attendees and 50 virtual attendees. 

CE: 3.0

Date/Time:  To be held April 29^th, 2022, in Seattle, WA – 8:00 am PDT 

Presenters:   

Dr. Scott Withrow
Dr. Rachel T. King
​Dr. Isaac Thompson

Abstract:

The modern world is increasingly driven by algorithms, machine learning, and data science. There are certainly many dangers and pitfalls that abound in using these tools and models that may cause issues in recruitment, selection, and promotion (and more). In this session we will cover some models and data science techniques so that you can not only avoid these common pitfalls but leverage these models to enhance validity and make quality decisions about data. We will be presenting two practical examples of machine learning–one using a supervised model and one using an unsupervised model.

Description:  

We will continue to see the growth of decisions being made by machine learning models across all facets of modern life. While these models grow in popularity so too do the reports of bad (and sometimes illegal) decisions being made by or with these models. In our highly regulated industry, it is essential that anyone applying or considering using machine learning understands how the models work, when it’s appropriate to use them, and what the benefits and drawbacks are. This session will cover some common machine learning topics and present two practical examples of machine learning by IO psychologists.

Session Length: 3 hours

Learning Objectives: 

• What are the basic machine learning models relevant to an I-O Psychologist?
• How can data science and machine learning help us understand data?
• What are some practical applications of machine learning in the IO world?
  • Bayesian Linear Regression with optimization formula for enhancing validity and avoiding adverse impact.
  • Novel NLP methods to classify and make sense of unstructured text without the need of human labels (unsupervised deep learning).

Detailed Description:

The goal of this session is for participants to come away with a general understanding of what machine learning is and what it can be used for. For example, what is the difference between a supervised and an unsupervised model? What kind of specific tools fall into those buckets and what does that mean to a practitioner thinking about using these models? We will then cover some basic data quality measures. For example, using bootstrapping in small samples, using different cross-validation strategies to give better statistics to clients (ranges of plausible validity), common data issues, and best practices for data handling. Finally, we will wrap up the session with two actual applications of machine learning in the IO world. These applications will be discussed in the seminar with data and code (R and Python) being disseminated for those that want to dip a toe into machine learning.

Presenter biographies:

Dr. Scott Withrow is the senior psychometrician at Infor Talent Science. His primary duties are to lead a team of people in developing new assessments and assessment strategy, calibrating the output from the assessments to best fit client organizations following job analysis, and conducting post-hoc studies to validate the continuing effectiveness of the tools. He uses a wide variety of statistical tools from Item Response Theory to Machine Learning to accomplish these goals.

Dr. Rachel T. King is a Senior Data Scientist at Modern Hire. Her work at Modern Hire involves building machine learning models to improve prediction in pre-hire assessments in addition to working with clients to implement these models. Her work often uses data science techniques such as natural language processing and machine learning. Additionally, she has worked extensively with the programming languages R and python. She holds a PhD in Industrial-Organizational Psychology from Bowling Green State University.

Dr. Isaac Thompson is a Principal Data Scientist at Modern Hire where he develops AI solutions applied to assessments and interviews, helping power selection and assessment for over 50% of the Fortune 500 companies. Broadly, his work focuses on the development of novel AI applications and linking it within traditional psychometrics with expertise in deep learning, machine learning, R, Python, Bayesian statistics, and measurement.

Session Type: Seminar

Session Format: In-person

CE: 3.0

Date/Time:  To be held April 29^th, 2022, in Seattle, WA – 8:00 am PDT

Presenters:    

Nora P. Reilly

Kasey V. Warren       

Abstract:

Remote work is on the rise and presents both challenges and opportunities at each of the organization, team, and individual levels.  Though there is no one-size-fits-all template for determining the suitability of remote work for a given organization, questions that assess required resources, policies, and strategic people practices will be addressed within the context of the culture of an organization.  The seminar will emphasize the development and growth of virtual teams using an interactive format.  Examples will be presented, and best practices will be suggested.

Session Length: 3 hours

Learning Objectives:

1. Identify critical questions to ask for the development of remote work
2. Learn how to conduct a Remote Work Needs Assessment at each of the organizational, group and individual levels
3. Recognize challenges and opportunities for developing and sustaining an organization’s culture through remote work
4. Better understand proactive practices for remote work

Detailed description:

This is a 3-hour interactive seminar in which participants will share opinions through digital media and respond to elements in case scenarios – so please do not stow your electronic devices or expect your seats to remain in an upright and locked position.  The goal of the session is to present a roadmap for navigating alternate routes to successful remote work.

Presenter bios:

Nora P. Reilly is professor emerita of industrial-organizational psychology at Radford University where she has conducted research, consulted, and taught for a very long time. Her research continues to focus on quality of work-life and the ethical issues associated with people practices in organizations.  Her LLC, People at Work Solutions, engages in hybrid assessments and OD interventions.  Reilly holds a Ph.D. in social psychology from Dartmouth College and continued her training in I-O at Colorado State University.

Kasey V. Warren is a consultant at EY who works on talent development with a focus on mentoring in a virtual environment.  Her passion is to teach others how to traverse the digital world of work to develop themselves and their organizations.  She works at home in Ithaca with virtual team members across the Americas. Warren holds an M.S. in industrial-organizational psychology from Radford University.

Date/Time:  Postponed to 2023 SIOP Annual Conference in Boston

Presenters:    

Dr. Jennifer K. Dimoff

Dr. Leslie B. Hammer

Presenter bios:

Dr. Jennifer K. Dimoff is an Assistant Professor at the Telfer School of Management at the University of Ottawa. Dimoff earned a Ph.D. in Industrial/Organizational Psychology from Saint Mary’s University (Canada) and holds a B.Sc. from Queen’s University (Canada). Dr. Dimoff joined the faculty at Telfer in 2019, after spending three years at Portland State University, where she maintains status as an Affiliate Faculty member. She is also an Affiliate Faculty member at Oregon Health & Science University. Her primary area of research focuses on the intersections between leadership, occupational health and safety, and employee training and development. As the youngest recipient of the Society of Industrial/Organizational Psychology (SIOP) Scientist-Practitioner Recognition Award, her research reflects her firm endorsement of the scientist-practitioner model. She has worked with local, national, and international organizations to develop, deliver, and evaluate evidence-based solutions to real workplace problems. Most notably, Dr. Dimoff co-developed the Mental Health Awareness Training (MHAT)—one of the first scientifically evaluated mental health training programs for workplace leaders to demonstrate ROI.  Dr. Dimoff is also a co-leader of the Canadian Foundation of Innovation-funded Triple-I Lab at the University of Ottawa – a state-of-the-art research center, dedicated to analyzing social interactions in work settings.  In addition to numerous journal publications and chapters, she has co-edited a book, Leading to Occupational Health and Safety: How Leadership Behaviours Impact Organizational Safety and Well-Being.

Dr. Leslie B. Hammer is a Professor in the Oregon Institute of Occupational Health Sciences at Oregon Health & Science University, Co-Director of the Oregon Healthy Workforce Center, one of six centers of excellence in Total Worker Health^® funded by NIOSH. She is also a Professor of Psychology at Portland State University and Associate Director of the Portland State University Occupational Health Psychology graduate training program. She is a leading expert on leadership support training programs, work and family, and occupational stress, more generally, and serves regularly as a consultant on occupational stress and well-being workplace issues. She has been funded by the NIH, DoD, and NIOSH to conduct scientifically rigorous randomized controlled trails evaluating the effectiveness of leadership trainings on both the leaders and their employees’ health, safety, and well-being. She has extensive experience in designing, implementing, and evaluating worksite interventions and supervisor training in such industries as healthcare, high tech, military, and the service sector.

Session Format: In-person

CE: 3.0

Date/Time: To be held April 29^th, 2022, in Seattle, WA – 11:00 am PDT

Presenters:    

Dr. Myia S. Williams

Dr. Kahlil King

Abstract:

The syndemic of COVID-19 and social injustice that permeated organizations presented new and unique challenges for BIPOC employees. One such challenge is remote working, which provided involuntary access into the personal lives of BIPOC employees. In this seminar we will be taking a deep dive challenges, barriers and benefits of the remote working experiences of BIPOC employees all of which may have contributed to the Great Resignation/Reckoning. We will also be providing strategies for not only BIPOC employees, but organizations, practitioners, and academics alike.
 

Session Length: 3 hours

Learning Objectives:

• Identify strengths and challenges of remote working for BIPOC employees
• Analyze through an intersectionality lens how the dimensions of one’s identity shape their views, values, perceptions and experiences in the workplace
• Identify evidence-based inclusion and access strategies for BIPOC employees during remote working and beyond

Detailed Description: The session will include a variety of interactive learning modalities. Background information will be provided in a lecture format. The lectures will ensure to create a psychologically safe and sacred space that is inviting for individuals to share their experiences of remote working. Participants will engage with interactive reflective exercises to understand the importance of inclusive environments the provide access to racial/ethnic minority employees.  Role modeling will be provided in video examples and participants will practice what they have learned in psychologically safe role-playing exercises. All materials and activities will be accessible in person or remotely and participants will follow along in a customized workbook that includes copies of slides and exercises, as well as a toolkit with supplemental materials, which they can keep for later reference. 

Presenter bios:

Dr. Myia S Williams is A Research Industrial/Organizational Psychologist at the Institute of Health Systems Science at Northwell Health. Williams earned her PhD in Applied Organizational Psychology from Hofstra University in 2017. She also earned a BBA from Monroe College – St. Lucia and MA in I/O Psychology at Brooklyn College. Dr. William’s work is focused on health disparities, occupational health, intersectionality, diversity, inclusion, discrimination, equity, workplace ostracism, acculturation and diversification of the physician workforce. More specifically, her work is focused on the workplace experiences of racial/ethnic minority including immigrant employees. Dr Williams uses multiple methodologies including mixed methods to design evidence based culturally tailored interventions to increase access for racial/ethnic minority (including immigrant) employees and patients. Dr Williams is also an Assistant Professor at the Donald and Barbara School of Medicine at Hofstra University and Research Instructor at the Feinstein Institute of Medical Research at Northwell Health. She is also from the beautiful Caribbean Island of St. Lucia.

Dr. Kahlil King is a Visiting Assistant Professor at Northern Kentucky University. Dr. King earned her PhD in Applied Organizational Psychology from Hofstra University in 2020. She also earned a Masters Degree in Economics from Brooklyn College and has a Bachelors in Business Management from Hampton University. King's work is on family-work conflict and the effects of home stressors on work outcomes. She focuses on the concept of psychological detachment from home at work and its implications for mood at work, wellbeing, burnout, organizational citizenship behaviors, and counterproductive behaviors. Dr. King also studies the bidirectional relationship between eating disorders and the workplace, specifically focusing on women of color. 

Session Format: In-person

CE: 3.0

Date/Time:  To be held April 29^th, 2022, in Seattle, WA – 2:00 pm PDT

Presenters:    

Daniel Schmerling

Nick Koenig

Abstract:

Despite the increasing adoption of AI in HR practices, managers/leaders often find it difficult to build an effective machine learning practice in their traditional HR and I-O psychology organizations and teams. In this session, participants will learn what they need to know to build and manage a machine learning practice. We will cover the subfields of machine learning, different machine learning models/approaches (e.g., NLP), what one must consider when building machine learning solutions, and how to attract and retain the right talent for your machine learning team and how to structure it.

Session Length: 3 hours

Learning Objectives:

1. What is machine learning?
2. What are the different areas of machine learning?
3. What do you need to consider when developing and implementing machine learning solutions?
4. How do you recruit, manage, and retain your machine learning team?

Description of session: The seminar will be broken up into multiple sections.  Each section will focus on a different objective related to learning about machine learning, how to use machine learning, and how to build a team that can perform machine learning.  The information will be delivered via lecture using a slide deck as well as multimedia presentations when applicable.  At different points in the seminar, we will have break out groups to discuss problems/cases related to the topic area and present to the rest of the room on their responses/solutions.  All materials will be made available for the participants and the speakers will provide their contact information for any additional conversations or consultation that attendees would like to have.

Presenter Bios:

Daniel Schmerling, Ph.D. is the Head of HR Data Science at Prudential where he is responsible for leading all artificial intelligence initiatives and analyses in the HR space.  Some of Schmerling’s work at Prudential includes the development and implementation of a skills-based talent marketplace using natural language processing to automatically process employee resumes to identify relevant employee skills and match those skills to appropriate job openings and/or stretch assignment opportunities within the organization.  Dr. Schmerling’s team at Prudential has also developed career mapping models, employee attrition models, and performance management and employee survey NLP models.  Before working at Prudential, Dr. Schmerling was a Senior Machine Learning Engineer at Wonderlic, where he led the development of the first of its kind automated job analysis system which could validly and automatically identify the KSAs required for a job based solely on a job title and job description.  Before that, Dr. Schmerling also served as a Manager on the Talent Assessment team within People Analytics at Capital One.  In this role he owned the selection systems and processes for a number of LOBs designing, validating, and implementing selection tools and applications.  He started his career with FMP Consulting as a Human Capital Consultant in Alexandria, VA where among many different projects, he served as project manager developing a performance management system for use with the players on the University of Miami Men’s Basketball Team.  Finally, Dr. Schmerling was a member of the winning team from the inaugural SIOP machine learning competition and has presented on machine learning and artificial intelligence at numerous Industrial and Organizational Psychology as well as Artificial Intelligence conferences.  Dr. Schmerling received his B.A. in Psychology from the University of Maryland, College Park and his Ph.D. in Industrial and Organizational Psychology from the University of Central Florida.

Nick Koenig, Ph.D. is a Principal Data Scientist at Modern Hire where he has built, validated, and productionized machine learning and deep learning models using both structured and unstructured data with a focus on leveraging natural language processing (NLP) to take a competency-based approach to scoring asynchronous video interviews. Before working as a data scientist at Modern Hire, Koenig spent over 5 years working at Walmart where he worked on the Global Selection and Assessment Team designing, validating, and implementing interviews and assessments that touched over 5 million candidates a year. While at Walmart he also worked as a Senior Research Scientist within Walmart Labs where he leveraged both micro and macro data to better predict and understand consumer demand. Nick was also the primary member of the winning team from the inaugural SIOP machine learning competition and has co-hosted the SIOP machine learning competition for the past three years to spread the use and understanding of machine learning in the field of I/O. Nick received his B.A. from the University of Missouri – Columbia and his Ph.D. in Industrial and Organizational Psychology from the University of Central Florida.