Learning Algorithms for Discrimination Free Innovation Funding Activities


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.

Additional Informationsunspecified
Commencement DateDecember 2019
Contributors Blohm, Prof. Dr. Ivo; Wincent, Prof. Ph.D Joakim & Malmström, Malin
Datestamp 09 Feb 2021 07:50
Institute/School ?? SoM Pr Inst ??
?? Referenten ??
ITEM - Institute of Technology Management with Transfer Center for Technology Management (TECTEM)
IWI - Institute of Information Management
?? SoM AP ??
Publications Antretter, Torben; Blohm, Ivo & Grichnik, Dietmar: Predicting Startup Survival from Digital Traces: Towards a Procedure for Early Stage Investors. 2018. - International Conference on Information Systems (ICIS). - San Francisco, CA, USA.
Antretter, Torben; Blohm, Ivo; Grichnik, Dietmar & Wincent, Joakim (2019) Predicting new venture survival: A Twitter-based machine learning approach to measuring online legitimacy. Journal of Business Venturing Insights, 11 (June). 1-8. ISSN 2352-6734
Blohm, Ivo; Antretter, Torben; Siren, Charlotta; Wincent, Joakim & Grichnik, Dietmar (2020) It’s a Peoples Game, Isn’t It?! A Comparison between the Investment Returns of Business Angels and Machine Learning Algorithms. Entrepreneurship Theory and Practice, ISSN 1042-2587
Antretter, Torben; Blohm, Ivo; Siren, Charlotta; Grichnik, Dietmar; Malmstrom, Malin & Wincent, Joakim (2020) Do Algorithms Make Better — and Fairer — Investments Than Angel Investors? Harvard Business Review, ISSN 0017-8012
Methods Machine Learning
Funders other
Partners Luleå University of Technology, Danske Bank
Principal Stiftelsen IMIT (Sweden)
Id 247960
Project Range HSG + Partners
Project Status ongoing
Project Type fundamental research project
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