no code implementations • 4 Dec 2023 • Gyandev Gupta, Bashir Rastegarpanah, Amalendu Iyer, Joshua Rubin, Krishnaram Kenthapadi
Then we study the effectiveness of our approach when applied to text embeddings generated by both LLMs and classical embedding algorithms.
1 code implementation • 31 May 2023 • Vijay Keswani, L. Elisa Celis, Krishnaram Kenthapadi, Matthew Lease
Instead, we find ourselves in a "closed" decision-making loop in which the same fallible human decisions we rely on in practice must also be used to guide task allocation.
no code implementations • 6 Jul 2022 • Ana Lucic, Sheeraz Ahmad, Amanda Furtado Brinhosa, Vera Liao, Himani Agrawal, Umang Bhatt, Krishnaram Kenthapadi, Alice Xiang, Maarten de Rijke, Nicholas Drabowski
In this paper, we report on ongoing work regarding (i) the development of an AI system for flagging and explaining low-quality medical images in real-time, (ii) an interview study to understand the explanation needs of stakeholders using the AI system at OurCompany, and, (iii) a longitudinal user study design to examine the effect of including explanations on the workflow of the technicians in our clinics.
no code implementations • 25 Jun 2022 • David Munechika, Zijie J. Wang, Jack Reidy, Josh Rubin, Krishna Gade, Krishnaram Kenthapadi, Duen Horng Chau
Recent research has developed algorithms for effectively identifying intersectional bias in the form of interpretable, underperforming subsets (or slices) of the data.
no code implementations • 6 Jun 2022 • Murtuza N Shergadwala, Himabindu Lakkaraju, Krishnaram Kenthapadi
Predictive models are increasingly used to make various consequential decisions in high-stakes domains such as healthcare, finance, and policy.
1 code implementation • 9 Apr 2022 • Michael Lohaus, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, Chris Russell
Based on this observation, we investigate an alternative fairness approach: we add a second classification head to the network to explicitly predict the protected attribute (such as race or gender) alongside the original task.
no code implementations • 21 Mar 2022 • Deborah Sulem, Michele Donini, Muhammad Bilal Zafar, Francois-Xavier Aubet, Jan Gasthaus, Tim Januschowski, Sanjiv Das, Krishnaram Kenthapadi, Cedric Archambeau
In this work we propose a model-agnostic algorithm that generates counterfactual ensemble explanations for time series anomaly detection models.
1 code implementation • ICLR 2022 • Fan Wu, Linyi Li, Chejian Xu, huan zhang, Bhavya Kailkhura, Krishnaram Kenthapadi, Ding Zhao, Bo Li
We leverage COPA to certify three RL environments trained with different algorithms and conclude: (1) The proposed robust aggregation protocols such as temporal aggregation can significantly improve the certifications; (2) Our certification for both per-state action stability and cumulative reward bound are efficient and tight; (3) The certification for different training algorithms and environments are different, implying their intrinsic robustness properties.
1 code implementation • 17 Feb 2022 • Kate Donahue, Alexandra Chouldechova, Krishnaram Kenthapadi
In many settings, however, the final prediction or decision of a system is under the control of a human, who uses an algorithm's output along with their own personal expertise in order to produce a combined prediction.
1 code implementation • 9 Feb 2022 • Vijay Keswani, Matthew Lease, Krishnaram Kenthapadi
Our key insight is that by exploiting weak prior information, we can match experts to input examples to ensure fairness and accuracy of the resulting deferral framework, even when imperfect and biased experts are used in place of ground truth labels.
no code implementations • 23 Dec 2021 • Muhammad Bilal Zafar, Philipp Schmidt, Michele Donini, Cédric Archambeau, Felix Biessmann, Sanjiv Ranjan Das, Krishnaram Kenthapadi
The large size and complex decision mechanisms of state-of-the-art text classifiers make it difficult for humans to understand their predictions, leading to a potential lack of trust by the users.
no code implementations • 26 Nov 2021 • David Nigenda, Zohar Karnin, Muhammad Bilal Zafar, Raghu Ramesha, Alan Tan, Michele Donini, Krishnaram Kenthapadi
With the increasing adoption of machine learning (ML) models and systems in high-stakes settings across different industries, guaranteeing a model's performance after deployment has become crucial.
no code implementations • 29 Sep 2021 • Chulin Xie, Yunhui Long, Pin-Yu Chen, Krishnaram Kenthapadi, Bo Li
Federated learning (FL) provides an efficient training paradigm to jointly train a global model leveraging data from distributed users.
no code implementations • 29 Sep 2021 • Jing Liu, Chulin Xie, Krishnaram Kenthapadi, Oluwasanmi O Koyejo, Bo Li
Vertical Federated Learning (VFL) is a distributed learning paradigm that allows multiple agents to jointly train a global model when each agent holds a different subset of features for the same sample(s).
no code implementations • 29 Sep 2021 • Xiaosen Wang, Bhavya Kailkhura, Krishnaram Kenthapadi, Bo Li
Finally, to demonstrate the generality of I-PGD-AT, we integrate it into PGD adversarial training and show that it can even further improve the robustness.
1 code implementation • 7 Sep 2021 • Michaela Hardt, Xiaoguang Chen, Xiaoyi Cheng, Michele Donini, Jason Gelman, Satish Gollaprolu, John He, Pedro Larroy, Xinyu Liu, Nick McCarthy, Ashish Rathi, Scott Rees, Ankit Siva, ErhYuan Tsai, Keerthan Vasist, Pinar Yilmaz, Muhammad Bilal Zafar, Sanjiv Das, Kevin Haas, Tyler Hill, Krishnaram Kenthapadi
We present Amazon SageMaker Clarify, an explainability feature for Amazon SageMaker that launched in December 2020, providing insights into data and ML models by identifying biases and explaining predictions.
no code implementations • 9 Jul 2021 • Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, Saeed Sharifi-Malvajerdi
The goal of the proxy is to allow a general "downstream" learner -- with minimal assumptions on their prediction task -- to be able to use the proxy to train a model that is fair with respect to the true sensitive features.
no code implementations • Findings (ACL) 2021 • Muhammad Bilal Zafar, Michele Donini, Dylan Slack, Cédric Archambeau, Sanjiv Das, Krishnaram Kenthapadi
With the ever-increasing complexity of neural language models, practitioners have turned to methods for understanding the predictions of these models.
1 code implementation • 14 May 2021 • Zeinab S. Jalali, Krishnaram Kenthapadi, Sucheta Soundarajan
The interaction patterns of employees in social and professional networks play an important role in the success of employees and organizations as a whole.
1 code implementation • 7 May 2021 • Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, Chris Russell
We initiate the study of fairness for ordinal regression.
1 code implementation • 11 Mar 2021 • Sergul Aydore, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Ankit Siva
We propose, implement, and evaluate a new algorithm for releasing answers to very large numbers of statistical queries like $k$-way marginals, subject to differential privacy.
1 code implementation • 25 Feb 2021 • Vijay Keswani, Matthew Lease, Krishnaram Kenthapadi
Machine learning models are often implemented in cohort with humans in the pipeline, with the model having an option to defer to a domain expert in cases where it has low confidence in its inference.
no code implementations • 11 Feb 2021 • Dylan Slack, Nathalie Rauschmayr, Krishnaram Kenthapadi
Each region contains a specific type of model bug; for instance, a misclassification region for an MNIST classifier contains a style of skinny 6 that the model mistakes as a 1.
no code implementations • 1 Jan 2021 • Dylan Z Slack, Nathalie Rauschmayr, Krishnaram Kenthapadi
As a route to better discover and fix model bugs, we propose failure scenarios: regions on the data manifold that are incorrectly classified by a model.
no code implementations • 15 Dec 2020 • Valerio Perrone, Huibin Shen, Aida Zolic, Iaroslav Shcherbatyi, Amr Ahmed, Tanya Bansal, Michele Donini, Fela Winkelmolen, Rodolphe Jenatton, Jean Baptiste Faddoul, Barbara Pogorzelska, Miroslav Miladinovic, Krishnaram Kenthapadi, Matthias Seeger, Cédric Archambeau
To democratize access to machine learning systems, it is essential to automate the tuning.
1 code implementation • 5 Nov 2020 • Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth
We consider a recently introduced framework in which fairness is measured by worst-case outcomes across groups, rather than by the more standard differences between group outcomes.
no code implementations • 14 Aug 2020 • Sriram Vasudevan, Krishnaram Kenthapadi
Many internet applications are powered by machine learned models, which are usually trained on labeled datasets obtained through either implicit / explicit user feedback signals or human judgments.
1 code implementation • 30 Jul 2020 • G. Roshan Lal, Sahin Cem Geyik, Krishnaram Kenthapadi
For this purpose, we construct a stylized model for generating training data with potentially biased features as well as potentially biased labels and quantify the extent of bias that is learned by the model when the user responds in a biased manner as in many real-world scenarios.
no code implementations • 9 Jun 2020 • Valerio Perrone, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, Cédric Archambeau
Moreover, our method can be used in synergy with such specialized fairness techniques to tune their hyperparameters.
no code implementations • 30 Apr 2019 • Sahin Cem Geyik, Stuart Ambler, Krishnaram Kenthapadi
We finally present the online A/B testing results from applying our framework towards representative ranking in LinkedIn Talent Search, and discuss the lessons learned in practice.
no code implementations • NAACL 2019 • Alexey Romanov, Maria De-Arteaga, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, Anna Rumshisky, Adam Tauman Kalai
In the context of mitigating bias in occupation classification, we propose a method for discouraging correlation between the predicted probability of an individual's true occupation and a word embedding of their name.
4 code implementations • 27 Jan 2019 • Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, Adam Tauman Kalai
We present a large-scale study of gender bias in occupation classification, a task where the use of machine learning may lead to negative outcomes on peoples' lives.
no code implementations • 20 Sep 2018 • Krishnaram Kenthapadi, Thanh T. L. Tran
Preserving privacy of users is a key requirement of web-scale analytics and reporting applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR.
no code implementations • 18 Sep 2018 • Sahin Cem Geyik, Qi Guo, Bo Hu, Cagri Ozcaglar, Ketan Thakkar, Xianren Wu, Krishnaram Kenthapadi
LinkedIn Talent Solutions business contributes to around 65% of LinkedIn's annual revenue, and provides tools for job providers to reach out to potential candidates and for job seekers to find suitable career opportunities.
no code implementations • 17 Sep 2018 • Rohan Ramanath, Hakan Inan, Gungor Polatkan, Bo Hu, Qi Guo, Cagri Ozcaglar, Xianren Wu, Krishnaram Kenthapadi, Sahin Cem Geyik
In this paper, we present the results of our application of deep and representation learning models on LinkedIn Recruiter.
no code implementations • 29 Mar 2017 • Krishnaram Kenthapadi, Stuart Ambler, Liang Zhang, Deepak Agarwal
The recently launched LinkedIn Salary product has been designed with the goal of providing compensation insights to the world's professionals and thereby helping them optimize their earning potential.