Open Access. Powered by Scholars. Published by Universities.®
Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Publication
- Publication Type
Articles 1 - 3 of 3
Full-Text Articles in Social and Behavioral Sciences
The Economic Opportunity Mapping (Eom) Tool, Craig W. Carpenter, Anders Van Sandt, Rebekka Dudensing, Scott Loveridge, Linda S. Niehm
The Economic Opportunity Mapping (Eom) Tool, Craig W. Carpenter, Anders Van Sandt, Rebekka Dudensing, Scott Loveridge, Linda S. Niehm
The Journal of Extension
Extension professionals increasingly understand data as integral to economic development planning and related efforts. However, regional economic data is often inaccurate, expensive, and unengaging for stakeholders. The Economic Opportunity Mapping Tool provides industry-specific free online interactive maps to engage stakeholders in the process of economic development planning, while also helping connect the determinants of business location with real local data on industry establishments.
The Role Of Data For Ai Startup Growth, James Bessen, Stephen Michael Impink, Lydia Reichensperger, Robert Seamans
The Role Of Data For Ai Startup Growth, James Bessen, Stephen Michael Impink, Lydia Reichensperger, Robert Seamans
Faculty Scholarship
Artificial intelligence (“AI”)-enabled products are expected to drive economic growth. Training data are important for firms developing AI-enabled products; without training data, firms cannot develop or refine their algorithms. This is particularly the case for AI startups developing new algorithms and products. However, there is no consensus in the literature on which aspects of training data are most important. Using unique survey data of AI startups, we find that startups with access to proprietary training data are more likely to acquire venture capital funding.
Ethical Ai Development: Evidence From Ai Startups, James Bessen, Stephen Michael Impink, Lydia Reichensperger, Robert Seamans
Ethical Ai Development: Evidence From Ai Startups, James Bessen, Stephen Michael Impink, Lydia Reichensperger, Robert Seamans
Faculty Scholarship
Artificial Intelligence startups use training data as direct inputs in product development. These firms must balance numerous trade-offs between ethical issues and data access without substantive guidance from regulators or existing judicial precedence. We survey these startups to determine what actions they have taken to address these ethical issues and the consequences of those actions. We find that 58% of these startups have established a set of AI principles. Startups with data-sharing relationships with high-technology firms; that were impacted by privacy regulations; or with prior (non-seed) funding from institutional investors are more likely to establish ethical AI principles. Lastly, startups …