I do interdisciplinary research that explores how practitioners can govern, anticipate, measure, and mitigate risks posed by AI systems. My work draws from theories and methods in AI/ML, HCI, design, and policy research to study the societal impacts of AI. These days, I am especially interested in methods that center the lived experiences of people who might be negatively impacted by AI, and interventions that give these stakeholders a say in how AI is developed and governed.
Previously, I graduated from Harvard in 2021 with a BA and MS in Computer Science, where I worked with Finale Doshi-Velez. I've also interned at Microsoft Research UK on the Teachable AI Experiences Team.
Email: narij at andrew dot cmu dot edu
Selected Research
For a complete list, see my Google Scholar.
Public Procurement for Responsible AI? Understanding US Cities' Practices, Challenges, and Needs
, Elise Silva, Harrison Leon, Motahhare Eslami, Beth Schwanke, Ravit Dotan, Hoda Heidari
arXiv preprint, under revision
October 2024
pdf
abstract
cite
response to US OMB RFI
The Fall of an Algorithm: Characterizing the Dynamics Toward Abandonment
, Sanika Moharana, Christina N. Harrington, Nazanin Andalibi, Hoda Heidari*, Motahhare Eslami*
ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2024
(* = Equal Contribution)
June 2024
pdf
abstract
database
podcast episode
cite
Where Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms
, Ángel Alexander Cabrera, Gregory Plumb, Ameet Talwalkar
AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2023
ICML Workshop on Spurious Correlations, Invariance, and Stability, 2023
November 2023
pdf
abstract
cite
conference talk
data & code
Provocation: Who benefits from "inclusion" in Generative AI?
*, Samantha Dalal*, Siobhan Mackenzie Hall*
The NeurIPS 2024 Workshop on Evaluating Evaluations
(* = Equal Contribution, shared first authorship)
October 2024
pdf
abstract
cite