Fair Classifiers that Abstain without Harm

• URL: http://arxiv.org/abs/2310.06205v1
• Date: Mon, 9 Oct 2023 23:07:28 GMT
• Title: Fair Classifiers that Abstain without Harm
• Authors: Tongxin Yin, Jean-Fran\c{c}ois Ton, Ruocheng Guo, Yuanshun Yao, Mingyan Liu, Yang Liu
• Abstract summary: In critical applications, it is vital for classifiers to defer decision-making to humans. We propose a post-hoc method that makes existing classifiers selectively abstain from predicting certain samples. Our framework outperforms existing methods in terms of fairness disparity without sacrificing accuracy at similar abstention rates.
• Score: 24.90899074869189
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In critical applications, it is vital for classifiers to defer decision-making to humans. We propose a post-hoc method that makes existing classifiers selectively abstain from predicting certain samples. Our abstaining classifier is incentivized to maintain the original accuracy for each sub-population (i.e. no harm) while achieving a set of group fairness definitions to a user specified degree. To this end, we design an Integer Programming (IP) procedure that assigns abstention decisions for each training sample to satisfy a set of constraints. To generalize the abstaining decisions to test samples, we then train a surrogate model to learn the abstaining decisions based on the IP solutions in an end-to-end manner. We analyze the feasibility of the IP procedure to determine the possible abstention rate for different levels of unfairness tolerance and accuracy constraint for achieving no harm. To the best of our knowledge, this work is the first to identify the theoretical relationships between the constraint parameters and the required abstention rate. Our theoretical results are important since a high abstention rate is often infeasible in practice due to a lack of human resources. Our framework outperforms existing methods in terms of fairness disparity without sacrificing accuracy at similar abstention rates.

Related papers

• Mitigating LLM Hallucinations via Conformal Abstention [70.83870602967625]
  We develop a principled procedure for determining when a large language model should abstain from responding in a general domain. We leverage conformal prediction techniques to develop an abstention procedure that benefits from rigorous theoretical guarantees on the hallucination rate (error rate) Experimentally, our resulting conformal abstention method reliably bounds the hallucination rate on various closed-book, open-domain generative question answering datasets.
  arXiv  Detail & Related papers  (2024-04-04T11:32:03Z)
• Leveraging Uncertainty Estimates To Improve Classifier Performance [4.4951754159063295]
  Binary classification involves predicting the label of an instance based on whether the model score for the positive class exceeds a threshold chosen based on the application requirements. However, model scores are often not aligned with the true positivity rate. This is especially true when the training involves a differential sampling across classes or there is distributional drift between train and test settings.
  arXiv  Detail & Related papers  (2023-11-20T12:40:25Z)
• Online Decision Mediation [72.80902932543474]
  Consider learning a decision support assistant to serve as an intermediary between (oracle) expert behavior and (imperfect) human behavior. In clinical diagnosis, fully-autonomous machine behavior is often beyond ethical affordances.
  arXiv  Detail & Related papers  (2023-10-28T05:59:43Z)
• Evaluating the Fairness of Discriminative Foundation Models in Computer Vision [51.176061115977774]
  We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP) We then systematically evaluate existing methods for mitigating bias in these models with respect to our taxonomy. Specifically, we evaluate OpenAI's CLIP and OpenCLIP models for key applications, such as zero-shot classification, image retrieval and image captioning.
  arXiv  Detail & Related papers  (2023-10-18T10:32:39Z)
• Boosting Fair Classifier Generalization through Adaptive Priority Reweighing [59.801444556074394]
  A performance-promising fair algorithm with better generalizability is needed. This paper proposes a novel adaptive reweighing method to eliminate the impact of the distribution shifts between training and test data on model generalizability.
  arXiv  Detail & Related papers  (2023-09-15T13:04:55Z)
• Causal Fair Machine Learning via Rank-Preserving Interventional Distributions [0.5062312533373299]
  We define individuals as being normatively equal if they are equal in a fictitious, normatively desired (FiND) world. We propose rank-preserving interventional distributions to define a specific FiND world in which this holds. We show that our warping approach effectively identifies the most discriminated individuals and mitigates unfairness.
  arXiv  Detail & Related papers  (2023-07-24T13:46:50Z)
• Classification with abstention but without disparities [5.025654873456756]
  We build a general purpose classification algorithm, which is able to abstain from prediction, while avoiding disparate impact. We establish finite sample risk, fairness, and abstention guarantees for the proposed algorithm. Our method empirically shows that moderate abstention rates allow to bypass the risk-fairness trade-off.
  arXiv  Detail & Related papers  (2021-02-24T12:43:55Z)
• Stopping criterion for active learning based on deterministic generalization bounds [4.518012967046983]
  We propose a criterion for automatically stopping active learning. The proposed stopping criterion is based on the difference in the expected generalization errors and hypothesis testing. We demonstrate the effectiveness of the proposed method via experiments with both artificial and real datasets.
  arXiv  Detail & Related papers  (2020-05-15T08:15:47Z)
• Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning [61.32992639292889]
  Fine-tuning of pre-trained transformer models has become the standard approach for solving common NLP tasks. We introduce a new scoring method that casts a plausibility ranking task in a full-text format. We show that our method provides a much more stable training phase across random restarts.
  arXiv  Detail & Related papers  (2020-04-29T10:54:40Z)
• Progressive Identification of True Labels for Partial-Label Learning [112.94467491335611]
  Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label. Most existing methods elaborately designed as constrained optimizations that must be solved in specific manners, making their computational complexity a bottleneck for scaling up to big data. This paper proposes a novel framework of classifier with flexibility on the model and optimization algorithm.
  arXiv  Detail & Related papers  (2020-02-19T08:35:15Z)
• Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint [31.86959207229775]
  In this paper, we propose a framework for learning an individually fair classifier. We define the it probability of individual unfairness (PIU) and solve an optimization problem where PIU's upper bound, which can be estimated from data, is controlled to be close to zero. Experimental results show that our method can learn an individually fair classifier at a slight cost of accuracy.
  arXiv  Detail & Related papers  (2020-02-17T02:46:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.