1 code implementation • 22 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.
no code implementations • 21 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.
no code implementations • 16 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
2 code implementations • 25 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.
no code implementations • 24 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.
no code implementations • 14 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.
no code implementations • 29 Jan 2021 • Anurag Shandilya, Abhisek Dash, Abhijnan Chakraborty, Kripabandhu Ghosh, Saptarshi Ghosh
Moreover, standard ROUGE evaluation metrics are unable to quantify the perceived (un)fairness of the summaries.
no code implementations • 30 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.
1 code implementation • 26 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.
no code implementations • 8 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.
1 code implementation • 1 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.
1 code implementation • 26 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.
1 code implementation • 7 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.
1 code implementation • 18 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.
no code implementations • 28 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.
no code implementations • 27 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.