Options
Learning Algorithms for Discrimination Free Innovation Funding Activities
Type
fundamental research project
Start Date
December 2019
End Date
November 2021
Status
ongoing
Description
The goal of the project is to evaluate the extent to which investment algorithms in new venture funding discriminate women entrepreneurs as well as the effectiveness of approaches to debiasing algorithms.
Leader contributor(s)
Partner(s)
Luleå University of Technology
Danske Bank
Funder(s)
Method(s)
Machine Learning
Range
HSG + Partners
Range (De)
HSG + Partner
Principal
Stiftelsen IMIT (Sweden)
Division(s)
Eprints ID
247960
4 results
Now showing
1 - 4 of 4
-
PublicationDo Algorithms Make Better — and Fairer — Investments Than Angel Investors?(Harvard Business Review, 2020-11-02)
;Malmstrom, MalinType: journal articleJournal: Harvard Business Review -
PublicationPredicting Startup Survival from Digital Traces: Towards a Procedure for Early Stage Investors( 2018-12-13)We investigate whether digital traces can be used to predict early stage startup survival. Based on common survival factors from the entrepreneurship literature, we mined the digital footprints of 542 entrepreneurs and their ventures. Using a context-specific text mining approach, we performed a bootstrapping simulation in which we predict 5-year survival for different survival rates that range from 50% to 10%. Our results indicate that we can predict 5-year survival with an accuracy of up to 91%. With this study, we will provide an evidence-based taxonomy of digital traces for predicting early stage startup survival, identify the most important digital traces for doing so and benchmark our predictive approach against the actual investments of 339 business angels.Type: conference paper
-
PublicationPredicting new venture survival: A Twitter-based machine learning approach to measuring online legitimacyResearch indicates that interactions on social media can reveal remarkably valid predictions about future events. In this study, we show that online legitimacy as a measure of social appreciation based on Twitter content can be used to accurately predict new venture survival. Specifically, we analyze more than 187,000 tweets from 253 new ventures' Twitter accounts using context-specific machine learning approaches. Our findings suggest that we can correctly discriminate failed ventures from surviving ventures in up to 76% of cases. With this study, we contribute to the ongoing discussion on the importance of building legitimacy online and provide an account of how to use machine learning methodologies in entrepreneurship research.Type: journal articleJournal: Journal of Business Venturing InsightsVolume: 11Issue: June
Scopus© Citations 49 -
PublicationType: journal articleJournal: Entrepreneurship Theory and Practice
Scopus© Citations 29