Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy

• URL: http://arxiv.org/abs/2011.07194v2
• Date: Fri, 16 Apr 2021 01:42:13 GMT
• Title: Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy
• Authors: Amanda Coston, Neel Guha, Derek Ouyang, Lisa Lu, Alexandra Chouldechova, and Daniel E. Ho
• Abstract summary: We show how linking administrative data can enable auditing mobility data for bias. We show that older and non-white voters are less likely to be captured by mobility data. We show that allocating public health resources based on such mobility data could disproportionately harm high-risk elderly and minority groups.
• Score: 61.60099467888073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anonymized smartphone-based mobility data has been widely adopted in devising and evaluating COVID-19 response strategies such as the targeting of public health resources. Yet little attention has been paid to measurement validity and demographic bias, due in part to the lack of documentation about which users are represented as well as the challenge of obtaining ground truth data on unique visits and demographics. We illustrate how linking large-scale administrative data can enable auditing mobility data for bias in the absence of demographic information and ground truth labels. More precisely, we show that linking voter roll data -- containing individual-level voter turnout for specific voting locations along with race and age -- can facilitate the construction of rigorous bias and reliability tests. These tests illuminate a sampling bias that is particularly noteworthy in the pandemic context: older and non-white voters are less likely to be captured by mobility data. We show that allocating public health resources based on such mobility data could disproportionately harm high-risk elderly and minority groups.

Related papers

• MisinfoEval: Generative AI in the Era of "Alternative Facts" [50.069577397751175]
  We introduce a framework for generating and evaluating large language model (LLM) based misinformation interventions. We present (1) an experiment with a simulated social media environment to measure effectiveness of misinformation interventions, and (2) a second experiment with personalized explanations tailored to the demographics and beliefs of users. Our findings confirm that LLM-based interventions are highly effective at correcting user behavior.
  arXiv  Detail & Related papers  (2024-10-13T18:16:50Z)
• Leveraging Prototypical Representations for Mitigating Social Bias without Demographic Information [50.29934517930506]
  DAFair is a novel approach to address social bias in language models. We leverage prototypical demographic texts and incorporate a regularization term during the fine-tuning process to mitigate bias.
  arXiv  Detail & Related papers  (2024-03-14T15:58:36Z)
• The Impact of Differential Feature Under-reporting on Algorithmic Fairness [86.275300739926]
  We present an analytically tractable model of differential feature under-reporting. We then use to characterize the impact of this kind of data bias on algorithmic fairness. Our results show that, in real world data settings, under-reporting typically leads to increasing disparities.
  arXiv  Detail & Related papers  (2024-01-16T19:16:22Z)
• PopSim: An Individual-level Population Simulator for Equitable Allocation of City Resources [12.152728063703005]
  We introduce PopSim, a system for generating semi-synthetic individual-level population data with demographic information. We use PopSim to generate multiple benchmark datasets for the city of Chicago and conduct extensive statistical evaluations.
  arXiv  Detail & Related papers  (2023-04-25T23:43:21Z)
• Fast Few shot Self-attentive Semi-supervised Political Inclination Prediction [12.472629584751509]
  It is increasingly common now for policymakers/journalists to create online polls on social media to understand the political leanings of people in specific locations. We introduce a self-attentive semi-supervised framework for political inclination detection to further that objective. We found that the model is highly efficient even in resource-constrained settings.
  arXiv  Detail & Related papers  (2022-09-21T12:07:16Z)
• 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)
• Data Representativeness in Accessibility Datasets: A Meta-Analysis [7.6597163467929805]
  We review datasets sourced by people with disabilities and older adults. We find that accessibility datasets represent diverse ages, but have gender and race representation gaps. We hope our effort expands the space of possibility for greater inclusion of marginalized communities in AI-infused systems.
  arXiv  Detail & Related papers  (2022-07-16T23:32:19Z)
• Biases in human mobility data impact epidemic modeling [0.0]
  We identify two types of bias caused by unequal access to, and unequal usage of mobile phones. We find evidence for data generation bias in all examined datasets in that high-wealth individuals are overrepresented. To mitigate the skew, we present a framework to debias data and show how simple techniques can be used to increase representativeness.
  arXiv  Detail & Related papers  (2021-12-23T13:20:54Z)
• Balancing Biases and Preserving Privacy on Balanced Faces in the Wild [50.915684171879036]
  There are demographic biases present in current facial recognition (FR) models. We introduce our Balanced Faces in the Wild dataset to measure these biases across different ethnic and gender subgroups. We find that relying on a single score threshold to differentiate between genuine and imposters sample pairs leads to suboptimal results. We propose a novel domain adaptation learning scheme that uses facial features extracted from state-of-the-art neural networks.
  arXiv  Detail & Related papers  (2021-03-16T15:05:49Z)
• The Disparate Impact of Uncertainty: Affirmative Action vs. Affirmative Information [0.0]
  We show that groups with higher average outcomes are typically assigned higher false positive rates. We explain why the intuitive remedy to omit demographic variables from datasets does not correct it. Instead of data omission, this paper examines how data enrichment can broaden access to opportunity.
  arXiv  Detail & Related papers  (2021-02-19T16:40:47Z)

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.