50: Machine Learning in Innovation Research: From Measurement to Causal Inference (10067)

Friday, August 9, 2024

08:00 – 09:30 CT (GMT-5/UTC-5)

Location: Sheraton: Sheraton Ballroom I

Session Number: 50
PDW (workshop)
Orientation: Theme,Research
Shareable link: https://cdmcd.co/3RbK9W

Organizer(s)

MM

Mariano Mastrogiorgio

IE Business School, Spain
MA

Mario Daniele Amore

HEC Paris, France

Panelist(s)

SA

Sam Arts

KU Leuven, Belgium
NC

Natalie Carlson

The Wharton School, U. of Pennsylvania, United States
AM

Arianna Marchetti

London Business School, United Kingdom
MM

Milan Miric

U. of Southern California -Marshall School of Business, United States

Moderator(s)

LL

LI Liu

U. Carlos III de Madrid (UC3M), Spain

Machine learning has gained increasing attention owing to the latest advancements in natural language processing, especially those based on transformers, such as OpenAI’s ChatGPT and Google’s BERT. Transformers are large language models built upon deep learning neural networks, harnessing some key underlying components, notably attention modules and contextual word embeddings. This PDW features several presentations by leading scholars who will share their knowledge and research insights into the latest developments in machine learning of text data, and address some of the key challenges for measurement and causal inference. The PDW is scheduled to run for one hour and thirty minutes, consisting of two segments: Part 1, featuring a general introduction and guest speakers (lasting 70 minutes), and Part 2, dedicated to a Q&A session and open debate (lasting 20 minutes). The goal of the PDW is to raise awareness on how scholars can leverage machine learning in innovation research.

The session is open to anyone interested. Registration and additional fees are NOT required.