Search Results for author: Abhijnan Chakraborty

Found 16 papers, 7 papers with code

Antitrust, Amazon, and Algorithmic Auditing

no code implementations27 Mar 2024 Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, Jens Frankenreiter, Stefan Bechtold, Krishna P. Gummadi

In digital markets, antitrust law and special regulations aim to ensure that markets remain competitive despite the dominating role that digital platforms play today in everyone's life.

Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification

no code implementations28 Feb 2024 Garima Chhikara, Anurag Sharma, Kripabandhu Ghosh, Abhijnan Chakraborty

Employing Large Language Models (LLM) in various downstream applications such as classification is crucial, especially for smaller companies lacking the expertise and resources required for fine-tuning a model.

Fairness In-Context Learning

Towards Fairness in Online Service with k Servers and its Application on Fair Food Delivery

1 code implementation18 Dec 2023 Daman Deep Singh, Amit Kumar, Abhijnan Chakraborty

In this paper, we introduce a realistic generalization of k-SERVER without such assumptions - the k-FOOD problem, where requests with source-destination locations and an associated pickup time window arrive in an online fashion, and each has to be served by exactly one of the available k servers.


Gigs with Guarantees: Achieving Fair Wage for Food Delivery Workers

1 code implementation7 May 2022 Ashish Nair, Rahul Yadav, Anjali Gupta, Abhijnan Chakraborty, Sayan Ranu, Amitabha Bagchi

With the increasing popularity of food delivery platforms, it has become pertinent to look into the working conditions of the 'gig' workers in these platforms, especially providing them fair wages, reasonable working hours, and transparency on work availability.

Scheduling Virtual Conferences Fairly: Achieving Equitable Participant and Speaker Satisfaction

1 code implementation26 Apr 2022 Gourab K. Patro, Prithwish Jana, Abhijnan Chakraborty, Krishna P. Gummadi, Niloy Ganguly

As the efficiency and fairness objectives can be in conflict with each other, we propose a joint optimization framework that allows conference organizers to design schedules that balance (i. e., allow trade-offs) among efficiency, participant fairness and speaker fairness objectives.

Fairness Scheduling

FaiRIR: Mitigating Exposure Bias from Related Item Recommendations in Two-Sided Platforms

1 code implementation1 Apr 2022 Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, Krishna P. Gummadi

To this end, our experiments on multiple real-world RIR datasets reveal that the existing RIR algorithms often result in very skewed exposure distribution of items, and the quality of items is not a plausible explanation for such skew in exposure.

Alexa, in you, I trust! Fairness and Interpretability Issues in E-commerce Search through Smart Speakers

no code implementations8 Feb 2022 Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, Krishna P. Gummadi

While investigating for the fairness of the default action, we observe that over a set of as many as 1000 queries, in nearly 68% cases, there exist one or more products which are more relevant (as per Amazon's own desktop search results) than the product chosen by Alexa.


Towards Fair Recommendation in Two-Sided Platforms

1 code implementation26 Dec 2021 Arpita Biswas, Gourab K Patro, Niloy Ganguly, Krishna P. Gummadi, Abhijnan Chakraborty

Many online platforms today (such as Amazon, Netflix, Spotify, LinkedIn, and AirBnB) can be thought of as two-sided markets with producers and customers of goods and services.

Fairness Vocal Bursts Valence Prediction

When the Umpire is also a Player: Bias in Private Label Product Recommendations on E-commerce Marketplaces

no code implementations30 Jan 2021 Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, Krishna P. Gummadi

Along a number of our proposed bias measures, we find that the sponsored recommendations are significantly more biased toward Amazon private label products compared to organic recommendations.


Analyzing 'Near Me' Services: Potential for Exposure Bias in Location-based Retrieval

no code implementations14 Nov 2020 Ashmi Banerjee, Gourab K Patro, Linus W. Dietz, Abhijnan Chakraborty

Online platforms like Google and Yelp allow location-based search in the form of nearby feature to query for hotels or restaurants in the vicinity.

Attribute Recommendation Systems +1

On Fair Virtual Conference Scheduling: Achieving Equitable Participant and Speaker Satisfaction

no code implementations24 Oct 2020 Gourab K Patro, Abhijnan Chakraborty, Niloy Ganguly, Krishna P. Gummadi

We show that the welfare and fairness objectives can be in conflict with each other, and there is a need to maintain a balance between these objective while caring for them simultaneously.

Fairness Scheduling

FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms

2 code implementations25 Feb 2020 Gourab K Patro, Arpita Biswas, Niloy Ganguly, Krishna P. Gummadi, Abhijnan Chakraborty

We investigate the problem of fair recommendation in the context of two-sided online platforms, comprising customers on one side and producers on the other.

Fairness Vocal Bursts Valence Prediction

On the Fairness of Time-Critical Influence Maximization in Social Networks

no code implementations16 May 2019 Junaid Ali, Mahmoudreza Babaei, Abhijnan Chakraborty, Baharan Mirzasoleiman, Krishna P. Gummadi, Adish Singla

As we show in this paper, the time-criticality of the information could further exacerbate the disparity of influence across groups.

Social and Information Networks Computers and Society

Public Sphere 2.0: Targeted Commenting in Online News Media

no code implementations21 Feb 2019 Ankan Mullick, Sayan Ghosh, Ritam Dutt, Avijit Ghosh, Abhijnan Chakraborty

Because the readers lack the time to go over an entire article, most of the comments are relevant to only particular sections of an article.

Summarizing User-generated Textual Content: Motivation and Methods for Fairness in Algorithmic Summaries

1 code implementation22 Oct 2018 Abhisek Dash, Anurag Shandilya, Arindam Biswas, Kripabandhu Ghosh, Saptarshi Ghosh, Abhijnan Chakraborty

Specifically, considering that an extractive summarization algorithm selects a subset of the textual units (e. g. microblogs) in the original data for inclusion in the summary, we investigate whether this selection is fair or not.

Extractive Summarization Fairness +1

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