Search Results for author:
Found 16 papers, 7 papers with code
Antitrust, Amazon, and Algorithmic Auditing
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
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.
Towards Fairness in Online Service with k Servers and its Application on Fair Food Delivery
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
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
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.
FaiRIR: Mitigating Exposure Bias from Related Item Recommendations in Two-Sided Platforms
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
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
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.
When the Umpire is also a Player: Bias in Private Label Product Recommendations on E-commerce Marketplaces
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.
Fairness for Whom? Understanding the Reader's Perception of Fairness in Text Summarization
Moreover, standard ROUGE evaluation metrics are unable to quantify the perceived (un)fairness of the summaries.
Analyzing 'Near Me' Services: Potential for Exposure Bias in Location-based Retrieval
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.
On Fair Virtual Conference Scheduling: Achieving Equitable Participant and Speaker Satisfaction
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.
FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms
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.
On the Fairness of Time-Critical Influence Maximization in Social Networks
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
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
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.
