Data quality dimensions for fair AI
- URL: http://arxiv.org/abs/2305.06967v2
- Date: Wed, 04 Dec 2024 16:54:03 GMT
- Title: Data quality dimensions for fair AI
- Authors: Camilla Quaresmini, Giuseppe Primiero,
- Abstract summary: We consider the problem of bias in AI systems from the point of view of data quality dimensions.
We highlight the limited model construction of bias mitigation tools based on accuracy strategy.
We propose to reconsider the fairness of the classification task in terms of completeness, consistency, timeliness and reliability.
- Score: 0.0
- License:
- Abstract: Artificial Intelligence (AI) systems are not intrinsically neutral and biases trickle in any type of technological tool. In particular when dealing with people, the impact of AI algorithms' technical errors originating with mislabeled data is undeniable. As they feed wrong and discriminatory classifications, these systems are not systematically guarded against bias. In this article we consider the problem of bias in AI systems from the point of view of data quality dimensions. We highlight the limited model construction of bias mitigation tools based on accuracy strategy, illustrating potential improvements of a specific tool in gender classification errors occurring in two typically difficult contexts: the classification of non-binary individuals, for which the label set becomes incomplete with respect to the dataset; and the classification of transgender individuals, for which the dataset becomes inconsistent with respect to the label set. Using formal methods for reasoning about the behavior of the classification system in presence of a changing world, we propose to reconsider the fairness of the classification task in terms of completeness, consistency, timeliness and reliability, and offer some theoretical results.
Related papers
- Human-Centric Multimodal Machine Learning: Recent Advances and Testbed
on AI-based Recruitment [66.91538273487379]
There is a certain consensus about the need to develop AI applications with a Human-Centric approach.
Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes.
We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
arXiv Detail & Related papers (2023-02-13T16:44:44Z) - Locating disparities in machine learning [24.519488484614953]
We propose a data-driven framework called Automatic Location of Disparities (ALD)
ALD aims at locating disparities in machine learning algorithms.
We demonstrate the effectiveness of ALD based on both synthetic and real-world datasets.
arXiv Detail & Related papers (2022-08-13T16:39:51Z) - D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling
Algorithmic Bias [57.87117733071416]
We propose D-BIAS, a visual interactive tool that embodies human-in-the-loop AI approach for auditing and mitigating social biases.
A user can detect the presence of bias against a group by identifying unfair causal relationships in the causal network.
For each interaction, say weakening/deleting a biased causal edge, the system uses a novel method to simulate a new (debiased) dataset.
arXiv Detail & Related papers (2022-08-10T03:41:48Z) - Understanding Unfairness in Fraud Detection through Model and Data Bias
Interactions [4.159343412286401]
We argue that algorithmic unfairness stems from interactions between models and biases in the data.
We study a set of hypotheses regarding the fairness-accuracy trade-offs that fairness-blind ML algorithms exhibit under different data bias settings.
arXiv Detail & Related papers (2022-07-13T15:18:30Z) - Social Norm Bias: Residual Harms of Fairness-Aware Algorithms [21.50551404445654]
Social Norm Bias (SNoB) is a subtle but consequential type of discrimination that may be exhibited by automated decision-making systems.
We quantify SNoB by measuring how an algorithm's predictions are associated with conformity to gender norms.
We show that post-processing interventions do not mitigate this type of bias at all.
arXiv Detail & Related papers (2021-08-25T05:54:56Z) - Improving Fairness of AI Systems with Lossless De-biasing [15.039284892391565]
Mitigating bias in AI systems to increase overall fairness has emerged as an important challenge.
We present an information-lossless de-biasing technique that targets the scarcity of data in the disadvantaged group.
arXiv Detail & Related papers (2021-05-10T17:38:38Z) - Representative & Fair Synthetic Data [68.8204255655161]
We present a framework to incorporate fairness constraints into the self-supervised learning process.
We generate a representative as well as fair version of the UCI Adult census data set.
We consider representative & fair synthetic data a promising future building block to teach algorithms not on historic worlds, but rather on the worlds that we strive to live in.
arXiv Detail & Related papers (2021-04-07T09:19:46Z) - Towards Learning an Unbiased Classifier from Biased Data via Conditional
Adversarial Debiasing [17.113618920885187]
We present a novel adversarial debiasing method, which addresses a feature that is spuriously connected to the labels of training images.
We argue by a mathematical proof that our approach is superior to existing techniques for the abovementioned bias.
Our experiments show that our approach performs better than state-of-the-art techniques on a well-known benchmark dataset with real-world images of cats and dogs.
arXiv Detail & Related papers (2021-03-10T16:50:42Z) - Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive
Person Re-Identification [64.37745443119942]
This paper jointly enforces visual and temporal consistency in the combination of a local one-hot classification and a global multi-class classification.
Experimental results on three large-scale ReID datasets demonstrate the superiority of proposed method in both unsupervised and unsupervised domain adaptive ReID tasks.
arXiv Detail & Related papers (2020-07-21T14:31:27Z) - Bias in Multimodal AI: Testbed for Fair Automatic Recruitment [73.85525896663371]
We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
We train automatic recruitment algorithms using a set of multimodal synthetic profiles consciously scored with gender and racial biases.
Our methodology and results show how to generate fairer AI-based tools in general, and in particular fairer automated recruitment systems.
arXiv Detail & Related papers (2020-04-15T15:58:05Z) - Leveraging Semi-Supervised Learning for Fairness using Neural Networks [49.604038072384995]
There has been a growing concern about the fairness of decision-making systems based on machine learning.
In this paper, we propose a semi-supervised algorithm using neural networks benefiting from unlabeled data.
The proposed model, called SSFair, exploits the information in the unlabeled data to mitigate the bias in the training data.
arXiv Detail & Related papers (2019-12-31T09:11:26Z)
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.