Document Type

Article

Publication Date

4-28-2023

Abstract

Lawmakers, technologists, and thought leaders are facing a once-in-a-generation opportunity to build equity into the digital infrastructure that will power our lives; we argue for a two-pronged approach to seize that opportunity. Artificial Intelligence (AI) is poised to radically transform our world, but we are already seeing evidence that theoretical concerns about potential bias are now being borne out in the market. To change this trajectory and ensure that development teams are focused explicitly on creating equitable AI, we argue that we need to shift the flow of investment dollars. Venture Capital (VC) firms have an outsized impact in determining which innovations will scale, we argue that influencing how these firms allocate the capital in their funds can ensure that issues of equity are top of mind for development teams. To shift the flow of investment dollars, we propose a two-pronged approach that will address two core drivers of the flow of investment: intellectual property (IP) and diversity. Our current IP system incentivizes a lack of transparency in the AI space frustrating attempts by third parties to assess whether AI- powered products and services are inequitable. And the current demographic makeup of VC firms and companies within the AI investment environment are out of sync with the general population, which can have negative downstream effects in terms of bias in AI. To change the existing dynamic, we argue for 1. creating a fifth category of IP for data and AI that would exchange ownership for compliance with a human rights framework and 2. establishing a tax incentive for VC firms graded favorably on our commitment index. Our approach is designed to create an equitable ecosystem of sorts, one that both necessitates and encourages equitable AI from conception to implementation.

Keywords

Artificial Intelligence, intellectual property, IP, AI, equity, Venture Capital, human rights, Machine Learning, data bias, diversity & inclusion, two-pronged approach, discrimination, demographic representation, tax adjustment

Publication Title

Michigan Technology Law Review

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