December 10-11, 2020

 Virtual on Zoom

Co-organized by

Stern School of Business, New York University
Heinz College, Carnegie Mellon University
Fox School of Business, Temple University

Over five billion people worldwide actively engage with AI, bots, machine-to-machine connected solutions, wearables, Internet-of-Things, 5G, AR/VR, Fintech, Mooc, and blockchain. This conference will explore how digital, social, and mobile technologies affect business models, customer behavior, public policy, and social changes at large. Exemplar topics include:

AI automation/ Robotics AI adoption and user behavior/AI chatbot voice-mining for promo and recommendations/ Bot trading and AI advisor in financial markets/AI for ad creatives and publishers/ AI applications in worker training, hiring, and supervising/ ML applications in fintech, pharma, and e-commerce/ Privacy and new technologies/ Data breach and security/ Blockchain applications/ Future of work and unemployment/ Big data IoT, 5G, AR, and VR applications/ Public policy and regulation of AI technologies/ AI algorithm bias, interpretable ML/ Machine learning for causal inference/ ML and deep learning for statistics methods/ Machine learning for empirical IO/ Deep reinforcement learning for microeconomics theory / Healthcare applications of ML/ Multi-armed bandits for online advertising and pricing/ Contextual MAB for personalized dynamic recommendations/ MOOC education online with ML and AI NLP with social media text data for targeting/ ML for images, voice, and video data B2B markets with ML and AI

Confirmed Keynote Speakers: Alessandro Acquisti (CMU), Jonah Berger (Wharton), Anindya Ghose (NYU), John Hauser (MIT), Kartik Hosanagar (Wharton), Dokyun Lee (CMU), Harikesh Nair (Stanford), Beibei Li (CMU), Sridhar Narayan (Stanford), Xueming Luo (Temple), Puneet Manchanda (UMich), PK Kannan (UMaryland), Bin Gu (BostonU),  Roland Rust (UMaryland), Ravi Bapna (UMN), Param Singh (CMU), Catherine Tucker (MIT), Olivier Toubia (Columbia), Shunyuan Zhang (Harvard)

Submission logistics: Submit either a 3-page abstract, full paper, or 10 PPT slides to AIML2020Conf@gmail.com; and cc the conference co-chairs: AGhose@stern.nyu.edu; BeibeiLi@andrew.cmu.edu; Xueming.Luo@temple.edu;

Submission deadline: October 26, 2020. Acceptance notification date: November 2, 2020 (Acceptance of submission requires one co-author to register and present at the conference)

This annual conference was hosted at Chicago Booth in 2014, NYU Stern in 2015 and 2017, Stanford GSB in 2016, CMU Tepper in 2018, and Temple in 2019. It has attracted a vibrant group of professors, industry people, and PhD students (each year max 150 people) working on cutting-edge ML AI models and data in inter-disciplinary fields. This conference serves as an intellectual bridge between computer science, economics, statistics, marketing, management, finance, strategy, IS, healthcare, education, public policy, and others.

Conference Schedule

Full Conference Schedule *Click here

*all time in Eastern Standard Time (EST)

Thursday, December 10, 2020

Welcome by Conference Chairs (Xueming Luo, Beibei Li, and Anindya Ghose)

Session Chair: Yang Wang

 

Presenter  Title 
Catherine Tucker (MIT)

 

Does Black-Box Profiling Lead To Disadvantages For Less Privileged and Unhealthy People

 

PK Kannan (Maryland)

 

An AI-based Adaptive Personalization System for Online Learning
Ravi Bapna (Minnesota)
Ravi Bapna (Minnesota) Social Learning in Prosumption: Evidence from a Randomized Field Experiment
10 min Break 
Olivier Toubia (Columbia) A Poisson Factorization Topic Model for the Study of Creative Documents
Anindya Ghose (NYU) Empowering Patients Using Smart Mobile Health Platforms

 

All plenary sessions will be livestreamed on Facebook: https://www.facebook.com/AIMLConference/live/

Break

Track A

Zoom Link: Click Here

 (AI-Human Hybrids, Sales Performance)

1

Human vs. AI: Will AI Allow Humans to Do Tasks that are Best Suited to Them?

Donghyuk Shin (Arizona State University), Ga Young Ko (University of Virginia),

Seigyoung Auh (Arizona State University),Yeonjung Lee (Arizona State University),Sang-Pil Han (Arizona State University)

2

The Janus Face of Artificial Intelligence (AI) Supervision:

Deployment and Disclosure Effects

Siliang Tong (Temple University), Nan Jia, (USC),

Zheng Fang

3

Manager Heuristics Vs. Machine Learning:

Automation for Prediction of Customer Value in Retailing

Emelie Fröberg (Stockholm School of Economics), Sara Rosengren (Stockholm School of Economics)

4

Algorithm vs. Human Curation Communication:

Depth or Breadth Selling and AI-Human Hybrid with Dynamic Field Experiments

Han Chen (Temple University), Yang Wang (Temple University),

Hanbing Xue (University of Science and Technology of China), Yongjun Li (University of Science and Technology of China)

Track B

Zoom Link: Click Here

(Online Reviews and Social Media)

1

The Market for Fake Reviews

Davide Proserpio (USC), Sherry He (UCLA),

Brett Hollenbeck (UCLA)

2

Chasing Stars: Firms’ Strategic Responses to

Online Consumer Ratings

Megan Hunter (Boston College)

3

The Bandwagon Effect: Can A Trophy Tame Internet Troll?

Xinlong Li (NTU), Xinying Hao (University of Arizona)

4

Estimating the impact of Airbnb on the local economy:

Evidence from the restaurant industry

Yongseok Kim (University of Texas at San Antonio), Davide Proserpio (USC)

Suman Basuroy (University of Texas at San Antonio)

Track C

Zoom Link: Click Here

 (Machine Learning and Consumers)

1

How does labor mobility affect business adoption of a GPT?

The case of machine learning

Ruyu Chen (Cornell University), Natarajan Balasubramanian (Syracuse University),

Chris Forman (Cornell University)

2

Complementing Human Effort in Online Reviews:

A Machine Learning Approach to Automatic Content Generation

Praveen K. Kopalle (Dartmouth College), Keith E. Carlson (Dartmouth College), Daniel Rockmore (Dartmouth College),Allen Riddell (Indiana University),

Prasad Vana (Dartmouth College)

3

Machine Learning Inference for Heterogeneous Effects

of Product Attributes

Lingling Zhang (University of Maryland), Fan Feng (Nanjing University),

Vithala R. Rao (Cornell University)

4

Using Interpretable Machine Learning to Understand

Consumer Credit Scores

Seung-Wook Kim (University of Iova), Thomas S. Gruca (University of Iowa),

Hyeong-Tak Lee (University of Iowa)

Track D

Zoom Link: Click Here

 (Deep Learning, Brand and Product)

1

Learning to Rank an Assortment of Products

Shreyas Sekar (University of Toronto), Kris J. Ferreira (Harvard Business School),

Sunanda Parthasarathy (CVS Health),

Shreyas Sekar (University of Toronto)

2

Mining Iconic Marketing Assets:

A Unified Multi-Modal Deep Learning Framework

Jochen Hartmann (University of Hamburg), Amos Schikowsky, University of Hamburg

Mark Heitmann, University of Hamburg

Michael Haenlein, ESCP Business School

3

Short-Lived Item Recommendation

Meizi Zhou (University of Minnesota), Yichen Song (University of Minnessota)

Zhuoxin Li (Boston College)

Chunmian Ge (South China University of Technology)

4

How Does Competition Affect Exploration vs. Exploitation?

A Tale of Two Recommendation Algorithms

Z. Eddie Ning (Cheung Kong Graduate School of Business), Henry Cao (Cheung Kong Graduate School of Business), Liye Ma (University of Maryland), Baohong Sun(Cheung Kong Graduate School of Business)

Break

click here (https://temple.zoom.us/j/99660952830?pwd=cTROMHVYWVpiRlh0TFl0dHFXcUxhdz09)

Session Chair: Marco Qin

 

Presenter  Title
Harikesh Nair (Stanford) Online Inference for Advertising Auctions
DK Lee (CMU) Patents, Generative Algorithms, and Innovation Frontiers
Sridhar Narayanan (Stanford) Behavioral Targeting, Machine Learning and Causal Effects using a Regression Discontinuity Design
10 min Break 
John Hauser (MIT)
Identifying Profitable and Feasible Design Gaps for New Products
Xueming Luo (Temple) AI for Customer Services and Employee Management

Break

Track A

Zoom Link: Click Here

(AI, Deep Learning, Reinforcement Learning)

1

Dynamic Marketing Policies: Constructing Markov States

Yuting Zhu (MIT), Duncan Simester (MIT),

Jonathan Parker (MIT),

Antoinette Schoar (MIT)

2

AI Agents for Sequential Promotions: Combining

Deep Reinforcement Learning and Dynamic Field Experimentation

Wen Wang (Carnegie Mellon University), Beibei Li (Carnegie Mellon University), Xiaoyi Wang (Zhejiang University)

3

Dynamic Algorithm-Human Hybrid Curation: Nudging Customer on Media Platforms

Han Chen (Temple University), Yang Wang (Temple University), Hanbing Xue (University of Science and Technology of China), Yongjun Li (University of Science and Technology of China)

4

Multi-Agent Deep Reinforcement Learning for

High-Density Air Traffic Management

Arun Balasubramaniyan (Insight. Inc), Nina Lopatina (In-Q-Tel)

Track B

Zoom Link: Click Here

(Advertising and Machine Learning)

1

 Engaging Customers Through Engaging Emails

Prasad Vana (Dartmouth College), Scott Neslin (Dartmouth College),

Pradeep Pachigolla (Dartmouth College)

2

How Does the Adoption of Ad Blockers Affect

News Consumption?

Shunyao Yan (Goethe University Frankfurt), Klaus M. Miller (Goethe University Frankfurt),

Bernd Skiera (Goethe University Frankfurt)

3

Deep Transfer Learning & Beyond: Future Directions for Transformer Language Models in Information Systems Research

Ross Gruetzemacher (The University of Memphis), David Paradice (Auburn University)

4

Building Bots That Can Bargain Against Beings

Sumon Chaudhuri (ESSEC) and Arnaud de Bruyn (ESSEC)

Track C

Zoom Link: Click Here

(AI, employees and Behaviors)

1

AI and Frontline Employee Innovativeness

Ozzie Ozkok (University of Melbourne)

2

The Effect and Drivers of Trustworthy Face in Online Job Market Platform

Junbum Kwon (UNSW), Donghyuk Shin (Arizona State University),

Gene Moo Lee, University of British Columbia),

Jake An (University of Sydney),

Sam Hwang, University of British Columbia)  

3

Partner or Servant?

A qualitative analysis of Smart Speakers social roles

Luigi Monsurro’ (La Sapienza University of Rome), Ilaria Querci (La Sapienza University of Rome)

4

Bad News? Send a Robot. Good News? Send a Human:

AI Agents Alter Consumer Responses

Aaron M. Garvey (University of Kentucky),

Tae Woo Kim (University of Technology Sydney),

Adam Duhachek (University of Illinois Chicago)

Track D

Zoom Link: Click Here

(Covid-19 and Business Analytics)

1

 How has COVID-19 Impacted Customer Equity

Daniel McCarthy (Emory University), Elliot Shin Oblander (Columbia University)

2

CRM and AI in the Time of Crisis: The Case of Paycheck Protection Program in the COVID-19 Pandemic

Michelle Lu (McG

Ill University), Navid Mojir (Harvard)

3

Individualism During Crises:

Big Data Analytics of Collective Actions amid COVID-19

Jingjing Li (University of Virginia), Natasha Zhang Foutz (University of Virginia), Bo Bian (University of British Columbia), Ting Xu (University of Virginia)

4

Crowdedness as the Missing Link between Shelter-In-Place and the Spread of COVID-19

Van Ngo (Rice University), Yang Wang (Temple University), Han Chen (Temple University)

Click Here 

“Challenges and Opportunities of Publishing AIML Topics in Top Journals” (Olivier Toubia at Columbia, Anindya Ghose at NYU, Param Singh at CMU, Xueming Luo at Temple).

Each panelist will first talk about one project or research themes on AIML topics at very high level for 5 min, then open up to conference attendees for Q&A on challenges and opportunities of publishing impactful AIML research at top journals such as Management Science, Marketing Science, Journal of Marketing Research, Information Systems Research, and others.    

Friday, December 11, 2020

click here (https://temple.zoom.us/j/99660952830?pwd=cTROMHVYWVpiRlh0TFl0dHFXcUxhdz09)

Session Chair: Ed Rosenthal

 

Presenter Title
Puneet Manchanda (Michigan) The Race for Data: Who Gained from Re-permission E-mails in the Enforcement of GDPR
Alessandro Acquisti (CMU) The Impact of the GDPR on Content Providers
Bin Gu (Boston) Information Technology, Artificial Intelligence, and Firm Labor Structure
10 min Break 
Shunyuan Zhang (Harvard) Unmasking with face masks: Analyzing customers’ risk-perception and purchase decisions with in-store shopping video
Beibei Li (CMU) Trading Privacy for the Greater Social Good: How Did America React During COVID-19?

Coffee Break

Track A

Zoom Link: Click Here

AI, Machine Learning and Advertising

1

Close Enough? A Large-Scale Exploration of Non-Experimental Approaches to Advertising Measurement

Brett R. Gordon (Northwestern University), Robert Moakler (Facebook)

Florian Zettelmeyer (Northwestern University)

2

What Can Pharmacy Do to Improve Patient’s Welfare?  

Modeling Health Risk based on Patients’ Medicine Purchase Record with AGCN

Wen Wang (Carnegie Mellon University), Beibei Li (Carnegie Mellon University),

Haizhong Wang (Sun Yat-sen Univerisity)

3

Field Experiments on the Profit Implications of Automation of

Data-Driven Decision-Making Tools in Retail

Tarun Kushwaha (George Mason University), Saravanan Kesavan, (University of North Carolina at Chapel Hill)

4

High Competence and Low Bias?  Effects of Artificial Intelligence Coaches on Employee 

Performance 

Nan Jia (USC), Zheng Fang (Sichuan University) Marco Qin (Temple University)

Track B

Zoom Link: Click Here

 (Pricing, Retailing and E-commerce)

1

Smoke and Mirrors: Impact of E-Cigarette Taxes on Underage Social Media Posting

Piyush Anand (Cornell University), Vrinda Kadiyali (Cornell University)

2

Estimating Heterogeneous Price Elasticities in P2P Markets:

A Semiparametric Approach to Orthogonal Random Forest

Ziwei Cong (HKUST), Jia Liu (HKUST)

3

Price Personalization in Freemium Settings:

A Field Experimental Study

Julian Runge (Humboldt University Berlin), Michaela Draganska (Drexel University),Daniel Klapper (Humboldt University Berlin)

4

Pricing Frictions on Airbnb

Yufeng Huang (Rochester University)

Track C

Zoom Link: Click Here

 (AI Bot and Voice AI)

1

The Daily Me versus The Daily Others:

How Do Curation Algorithms (Not)Diversify User Interests?

Jia Liu (HKUST), Ziwei Cong (HKUST)

2

The Financial Value of Launching

Conversational Commerce Applications

Kaushik Jayaram (University of Georgia), Sundar Bharadwaj (University of Georgia)

3

When Do AI Voicebots Work and Backfire?

Siliang Tong (Temple University),

Zheng Fang (Sichuan University), Zhe Qu (Fudan University)

4

The Effect of Voice AI on Consumer Shopping Behavior:

Evidence from Alibaba

Chenshuo Sun (NYU), Zijun Shi (HKUST), Xiao Liu (NYU), Anindya Ghose (NYU), Xueying Li (Alibaba), Feiyu Xiong (Alibaba Inc.)

Track D

Zoom Link: Click Here

(AI and employees)

1

 Improved Retention Analysis in Freemium Games by Jointly Modeling Players’ Motivation, Progression and Churn

Gourab Mukherjee (USC), Bikram Karmakar (Florida), Peng Liu (Santa Clara University), Hai Che (UC Riverside), Shantanu Dutta (USC)

2

Do You Mind if I Ask You a Personal Question? How

AI Agents Alter Consumer Self-Disclosure

Tae Woo Kim (University of Technology Sydney), Li Jiang (George Washington University),

Adam Duhachek (University of Illinois Chicago), Hyejin Lee (Indiana University), Aaron Garvey (University of Kentucky)

3

Documenting the death of the third-party cookie:

future directions for programmatic advertising

Phoebe Fletcher (Massey University)

Break

Click Here

Session Chair: Marco Qin

Presenter  Title
Kartik Hosanagar (Wharton) Trust and Adoption of AI
Nan Jia (USC) AI Supervision: A Battle of Quality and Trust
Param Singh (CMU) “Un”fair Machine Learning Algorithms
10 min Break 
Roland Rust (UMD)/Ming-Hui Huang (NTU) The Managerial Side of AI
Jonah Berger (Wharton) Quantifying the Shape of Narratives

Break

Track A

Zoom Link: Click Here

 (AI, Multi-Armed Bandit, Recommendation Algorithms)

1

Multi-armed Bandits with Cost Subsidy

Vashist Avadhanula, (Facebook), Deeksha Sinha (MIT), Karthik Abinav Sankararaman (Facebook),

Abbas Kazerouni (Facebook)

2

Ads with CMAB: Creative Design and Dynamic Targeting

Han Chen (Temple University), Haizhong Wang (Sun Yat-sen University)

3

A Multi-Armed Bandit Approach for

House Ads Recommendations

Marcel Goic (University of Chile), Nicol ́as Aramayo, I1B Labs

Mario Schiappacasse, I1B Labs

Track B 

Zoom Link:  Click Here 

 (AI, Privacy, and Society)

1

Towards a Coalescence of AR/VR and AI in Marketing 

Qeis Kamran (ISM International School of Management), Gerrit Schmidt (ISM International School of Management)

2

A marketing ecosystems perspective on

the impact of artificial intelligence on value cocreation 

Maria Petrescu (International University of Monaco), John T. Gironda, (University of North Carolina Wilmington)

3

Service Failures in Co-created, AI-powered Service Encounters: Exploring Customer Attribution of Responsibility

Daniela Castillo (Brunel University), Ana Isabel Canhoto (Brunel University),

Emanuel Said (University of Malta)

4

Towards an Evolving Logic of Artificial General Marketing Intelligence (AGMI)

Qeis Kamran (ISM International School of Management), Marcus Becker (ISM), Ard Reshani

Track C 

Zoom Link: Click Here

(Deep Learning, Algorithms, Predictions)

1

Learning from Driving Behaviors:

A Deep Learning Approach for Predicting Retail Visits

Unnati Narang (University of Illinois at Urbana-Champaign), Fernando Luco (Texas A&M University)

2

When Algorithms Err: Differential Impact of Early vs. Late Errors on Users’ Reliance on Algorithms

Mochen Yang (University of Minnesota), Antino Kim (Indiana University)

Jingjing Zhang (Indiana University)

3

Counterfactual Demand Predictions: Deep Learning with Microeconomic Structure

Mingyu (Max) Joo (UC Riverside), Dong Soo Kim (Ohio State University),

Chui Kim (Baruch College),

Hai Che (UC Riverside)

4

AI-Human Supervisory Configurations

Narayan Ramasubbu (University of Pittsburgh),

Zheng Fang (Sichuan University), He Peng (Fudan University)

Track D

Zoom Link: Click Here

(AI, Deep Learning, Customer Services)

1

First Law of Motion: Influencer Video Advertising on TikTok

Jeremy Yang (MIT), Juanjuan Zhang, MIT

Yuhan Zhang, Tsinghua University

2

Video Influencers: Unboxing the Mystique

Prashant Rajaram (University of Michigan), Puneet Manchanda (University of Michigan)

3

When Does Sentimental Artificial Intelligence Work and Backfire in Customer Services? A Field Experiment

Yuqian Chang (Rutgers University), Zheng Fang (Sichuan University),

Jaakko Aspara (Kanken School of Economics)

4

The Artifact of User Confirmation in Chatbot Design: Problems and Solutions

Yang Wang (Temple), Yuran Wang (Zhejiang),

Xiaoyi Wang (Zhejiang University)

Click Here

“Best Practices of Working with Industry Companies on AIML Topics” (John Hauser at MIT, Harikesh Nair at Stanford, Vashist Avadhanula at Facebook, Brett Gordon at Northwestern, moderated by Xueming Luo)

Each panelist will first share the experience of working with tech industry leaders such as Facebook, JD, and others on AIML applications at very high level for 5 min, then open up to conference attendees for Q&A on best practices and pitfalls of industry-academic collaboration on AIML topics.