Vishal Rituraj
Vishal Rituraj
PhD Candidate in Strategic Management
PhD Candidate in Strategic Management
University of Washington
University of Washington
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My research examines the political strategies of firms, with a focus on how market forces shape corporate political activity. One stream investigates when and why firms adopt partisan versus bipartisan contribution strategies as a means of managing political uncertainty. A second asks why some organizations develop systematic ideological skews in their political behavior over time. My dissertation develops these questions further, examining how strategic diversification shapes the scope and composition of firms' political engagement.
Before joining the University of Washington, I completed my M.Sc. in Management (Strategy) at Queen’s University, Canada, where my research examined how historical events imprint communities with enduring cultural toolkits that shape how they collectively interpret and solve new challenges.
EDUCATIONAL BACKGROUND
EDUCATIONAL BACKGROUND
Bachelor of Engineering
Bachelor of Engineering
MSc Strategy
MSc Strategy
PhD Strategy (Expected '28)
PhD Strategy (Expected '28)
ONGOING RESEARCH PROJECTS
Ideological skew in corporate america
Strategic diversification to Political diversification
PUBLISHED WORKS
Venugopal, V., & Rituraj, V. (2022). Beyond Siri and Alexa: Gender and AI policy. In Gender, Diversity and Innovation (pp. 125-147). Edward Elgar Publishing.
Artificial Intelligence (AI) is one of the biggest tools in innovation in the current age. However, there is increased recognition of its susceptibility to biases owing to the use of biased datasets. This chapter focuses on gender bias in AI and aims to uncover the "premises" on which the national AI policies of 19 major countries around the world are based. It explores crucial questions pertaining to the intended output of AI-enabled technologies, whether the AI policies explicitly address gender, and if so, in which contexts within AI do these discussions occur. We argue that the AI policies should catch up to and stay ahead of the technology in adopting regulations that are gendered and help minimize, if not eliminate, the bias and discrimination based on gender. Based on the premises observed in policies analysed, we recommend three approaches to reimagining and reformulating an inclusive AI policy.
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