Hello, I’m Avijit!

I am a Ph.D. Candidate at the Khoury College of Computer Sciences at Northeastern University, Boston, advised by Dr. Christo Wilson, studying Fairness, Transparency and Ethics in Algorithms. I strive to find answers to questions about the societal impacts of modern data driven platforms and technology's role in addressing inequality.

Research Interests

  • Algorithmic Fairness
  • Ethical AI
  • Information Retrieval
  • Machine Learning
  • AI Explainability
  • Computational Social Science



  • Subverting Fair Image Search with Generative Adversarial Perturbations. -acm

    at the ACM Conference on Fairness, Accountability, and Transparency (FAccT) 2022, Seoul, South Korea

    Avijit Ghosh, Matthew Jagielski, Christo Wilson

  • ”FairCanary: Rapid Continuous Explainable Fairness ” - arxiv

    at the AAAI/ACM Conference on AI, Ethics, and Society (AIES) 2022, Oxford, United Kingdom

    Avijit Ghosh, Aalok Shanbhag, Christo Wilson

  • ”Algorithms that "Don't See Color": Comparing Biases in Lookalike and Special Ad Audiences ” - arxiv

    at the AAAI/ACM Conference on AI, Ethics, and Society (AIES) 2022, Oxford, United Kingdom
    also presented at the Workshop on Technology and Consumer Protection (ConPro) 2022, San Francisco, California, USA (Best paper runner up)

    Media articles: Propublica, Mother Jones

    Piotr Sapiezynski, Avijit Ghosh, Levi Kaplan, Alan Mislove and Aaron Rieke

  • When Fair Ranking Meets Uncertain Inference. -acm

    at the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) 2021, Virtual

    Avijit Ghosh, Ritam Dutt, Christo Wilson

  • Building and Auditing Fair Algorithms: A Case Study in Candidate Screening. -acm

    at the ACM Conference on Fairness, Accountability, and Transparency (FAccT) 2021, Toronto, Ontario, Canada / Virtual

    Christo Wilson, Avijit Ghosh, Shan Jiang, Alan Mislove, Lewis Baker, Janelle Szary, Kelly Trindel, Frida Polli

  • Characterizing Intersectional Group Fairness with Worst-Case Comparisons. -pmlr

    at the 2nd Affinity Group Workshop on Diversity in Artificial Intelligence: Diversity, Belonging, Equity, and Inclusion (AIDBEI) at AAAI 2021

    Avijit Ghosh, Lea Genuit, Mary Reagan

  • Analyzing Political Advertisers’ Use of Facebook’s Targeting Features. -ieee

    at the Workshop on Technology and Consumer Protection (ConPro) 2019, San Francisco, California, USA

    Avijit Ghosh, Giridhari Venkatadri, Alan Mislove

  • Public Sphere 2.0: Targeted Commenting in Online News Media. -springer

    at the European Conference on Information Retrieval (ECIR) 2019, Cologne, Germany (Best poster)

    Ankan Mullick, Sayan Ghosh*, Ritam Dutt*, Avijit Ghosh*,Abhijnan Chakrabarty

  • SAVITR: A System for Real-time Location Extraction from Microblogs during Emergencies. -acm

    at the WWW 2018 workshop on Exploitation of Social Media for Emergency Relief and Preparedness (SMERP) - 2018 Lyon, France

    Ritam Dutt, Kaustubh Hiware, Avijit Ghosh, Rameshwar Bhaskaran


  • ”Unified Shapley Framework to Explain Prediction Drift ” - arxiv

    Aalok Shanbhag*, Avijit Ghosh*, and Josh Rubin*


  • Connectedness of Markets with Heterogeneous Agents and the Information Cascades. -springer

    Applied Advanced Analytics (Springer Proceedings in Business and Economics), 2021
    Presented at the 6th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence (ICADABAI) 2019 Ahmedabad, India

    Avijit Ghosh, Aditya Chourasiya, Lakshay Bansal, Abhijeet Chandra

  • WebSelect: A Research Prototype for Optimizing Ad Exposures based on Network Structure.-arxiv

    at the Workshop on Information Technology and Systems (WITS) 2016, Dublin, Ireland.

    Avijit Ghosh, Agam Gupta, Divya Sharma, Uttam Sarkar

  • Molecule2Vec: Vector Space Representation of Organic Molecules for prediction of properties using Deep Neural networks.-slides

    at the European Congress of Chemical Sciences (EUCHEMS) 2018, Liverpool, UK

    Avijit Ghosh, Debasis Sarkar

  • ”Supervised extraction of catchphrases from legal documents.” - pdf

    Avijit Ghosh*, Prerit Gupta*, Ritam Dutt, Kaustubh Hiware, Arpan Mandal, Kripabandhu Ghosh and Saptarshi Ghosh

  • Using Global Vectors in Social Interaction Network for Song Recommendation. -pdf

    Avijit Ghosh*, Sayan Ghosh*

* Equal contribution


Sep 2021 - Dec 2021

Research Intern

• Working with the META (Machine Ethics, Transparency and Accountability) team at Twitter, investigating the relationship between demography agnostic and demography dependent fairness metrics at scale.

Oct 2020 - Jan 2021

Research Intern

• Explain distributional shifts in Machine Learning model outputs by unifying Shapley based methods.
• Using optimal transport theory, proposed a threshold independent fairness metric that allows for real time explanations.
• Worked with the product team and civil rights lawyers in the deployment of Fiddler’s Machine Learning model fairness dashboard. Introduced and incorporated intersectional fairness metrics in the product.

Sep 2019 - Present

Graduate Research Assistant

• Collaborating with PyMetrics, a talent matching software, to audit their end to end pipeline to discover and fix racial and gender biases in their recommendation algorithm
• Analysing Fair ranking systems that have been published in CS literature and showing how they break down in the presence of noisy protected attribute data, to show the theoretical bounds of fairness in such cases.
• Investigated Facebook’s Special Audiences system for opportunity advertisements and showed that the audience creation algorithm was still biased against women, old people and minorites. Covered in the media by Propublica and Mother Jones.
• Analysed the ad reach and spend information obtained from Facebook’s ad transparency feature and the personal targeting dataset from Propublica’s Facebook ad dataset and showed that advertisers with higher budgets use more privacy sensitive targeting techniques like PII or Lookalike audiences. Findings published and presented at IEEE ConPro 2019.

Supervisor: Prof. Alan Mislove and Prof. Christo Wilson
May 2019 - July 2019

Visiting Researcher

• Study of how news companies promote different items on social media, investigating possible patterns of differential use.

Supervisor: Dr. Oana Goga
May 2017 - July 2017

Summer Research Intern

• Worked with the smart communications team and the machine learning team at Conduent Labs (formerly Xerox Research Center India)
• Implemented XTrack, a Smart Vehicle Tracking and Battery usage minimizing Algorithm.
• Uber Surge Price Prediction using Spatio-Temporal techniques like the Neural Hawkes and Recurrent Marked Temporal Point Process. Was given the Best Internship Project award for this project.

Supervisors: Narendra Annamaneni, Poorvi Agrawal
May - Aug 2016

Google Summer of Code Student

• Replaced the HTML XForms system used in the Android app with native generated forms using the Forms REST Api and added offline form saving.
• Configured Travis CI to automatically build and push the apk to play store.

Supervisors: Rafal Korytkowski and Robert O’Connor
2014 - 2019

Undergraduate Researcher

• Automated Extraction of Catchwords from Legal Documents using a novel NER based tagger to help categorize lengthy legal texts.
• Automatically position user comments against relevant news article paragraphs. Presented at ECIR 2019.
• Savitr - A realtime location extraction system for disaster management using twitter. Presented at WWW-SMERP 2018.

Supervisors: Prof. Niloy Ganguly and Prof. Saptarshi Ghosh - CNERG Lab


2019 - Present

Northeastern University, Boston

Ph.D. in Computer Science
2014 - 2019

Indian Institute of Technology, Kharagpur

• B.Tech. in Chemical Engineering

• M.Tech. in Financial Engineering

• Minor in Computer Science