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Research Interests

Human Computer Interaction, labor, social computing, sustainability


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I am a doctoral candidate in the Information Science department at Cornell University. ​ My research falls in the intersection of Human Computer Interaction (HCI),  Computer Science (CS), and  Development Sociology.  I  study and design technologies that minimize inequities in digital labor, especially for underserved communities. I take a mixed-methods approach with an aim to translate my research insights into impactful technological solutions. You can read my work in leading conferences, including CHI, CSCW, ICTD, & COMPASS.  All of my research has been generously funded by Engaged, Einaudi, and Mozilla grants. I also actively mentor under-resourced students and provide research assistance for leading non-profits. Please feel free to send me an email at rv288 at cornell [dot] edu.


  • July'23: New paper at COMPASS'23 around post-pandemic teacher support. My first as a mentor!

  • Apr'23: New postdoc position at NYU Tandon School from Jan. New chapter!

  • Feb'23: Gave invited talk at the department of Informatics, New Jersey Institute of Technology (NJIT).

  • Jan'23: New publication @CHI'23 around Responsible AI challenges encountered by practitioners in big tech.

  • Oct'22: Received the prestigious DLI fellowship.

  • Apr'22:  Will be working as student research intern at Google in responsible AI team starting this summer.

  • Mar'22: Recieved honorable mention for our CHI'22 paper around women crowd workers.

  • Feb'22: Another CHI'22 paper around motivations & challenges of first-time women crowd workers is in.



“It is currently hodgepodge”: Examining AI/ML Practitioners’ Challenges during Co-production of Responsible AI Values

Recently AI/ML research community has indicated an urgent need to establish Responsible AI (RAI) values and practices as part of the AI/ML lifecycle. Several organizations and communities are responding to this call by sharing RAI guidelines. However, there are gaps in awareness, deliberation, and execution of such practices for multi-disciplinary ML practitioners. This work contributes to the discussion by unpacking challenges faced by practitioners as they align their RAI values. We interviewed 23 individuals, across 10 organizations, tasked to ship AI/ML based products while upholding RAI norms and found that both top-down and bottom-up institutional structures burden selective roles to uphold RAI values, a challenge that is further exacerbated when executing conflicted values. We share multiple value levers used as strategies by the practitioners to resolve their challenges. We end our paper with recommendations for inclusive and equitable RAI value-practices, creating supportive organizational-structures and opportunities to further aid practitioners.

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