Data and Displacement: Assessing the Practical and Ethical Implications of Targeting Humanitarian Protection

数据和流离失所：评估以人道主义保护为目标的实际和道德影响

• 批准号: AH/T007516/1
• 负责人: Vicki Squire
• 金额: $ 60.59万
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=AH%2FT007516%2F1
• 关键词: Data; Displacement; Assessing; Practical; Ethical

Data-driven practices of targeted humanitarian protection are in urgent need of assessment, since these raise a range of practical and ethical questions that directly impact at-risk populations. While the targeting of protection needs through the production of data is common practice, the proliferation of large-scale quantitative, biometric and visual data within the humanitarian field is unprecedented. Established in 2014, there are presently 10,055 datasets for 253 locations from 1219 sources on the Humanitarian Data Exchange (HDX). This project produces a timely and robust analysis of data-driven protection targeting, focusing on two contexts that are characterised by conflict and high numbers of internally displaced persons (IDPs): northern Nigeria and South Sudan. It does so by assessing the implications of the production and use of large-scale data in targeting humanitarian protection, particularly on those most directly affected by such developments: IDPs themselves.The increasing production and use of large-scale data is not unique to the field of humanitarianism. However, the stakes are particularly high when it comes to data-driven practices of targeting protection for those most at risk. Humanitarian interventions are designed to protect the most vulnerable groups, hence any misuse or miscalculation in the use of data can have a drastic effect on at-risk populations. Careful assessment of the practical and ethical implications of data-driven targeting of protection is thus foundational. It is often assumed that humanitarians can be trusted more than commercial organisations or governments in collating and using large-scale data, due to their mandate of 'do no harm'. Yet it is vital to examine the potential risks, exclusions and biases or vulnerabilities implicit in the production and use of such data. While data can enable quicker, efficient and improved evidence-based responses, critical questions surrounding processes of data collection and what counts as evidence, the ethics of data collection and its use, and the accountability and protection of the data produced in contexts of vulnerability are increasingly necessary.Our research addresses these questions based on the research team's combined interdisciplinary academic and operational expertise. The project brings together academic researchers from the UK and Nigeria with practitioners from the International Organisation for Migration - the United Nations Migration Agency, which is responsible for the Displacement Tracking Matrix (DTM). DTM is the system deployed to track and monitor displacement and population mobility, and forms one of the largest data collectors on IDPs globally. Our project examines the work of DTM in the context of wider datasets used for the targeting of humanitarian protection in northern Nigeria and South Sudan. It focuses on practical and ethical challenges that arise in the collection and use of such data, and undertakes in-depth research with local stakeholders and internally displaced communities. The project asks: How effective is the data-based targeting of humanitarian protection in practice? Who benefits from such practices - and who is excluded? And how can the data-driven targeting be developed to reduce the chances of vulnerable groups falling through the cracks of humanitarian protection?The project contributes: an interdisciplinary perspective on the practical and ethical implications of data-driven humanitarian targeting in sub-Saharan Africa; an operationally-driven analysis of the efficacy of data-driven humanitarian protection in contexts of conflict and displacement; a qualitative assessment of the impacts of the production and use of large-scale data for at-risk populations; and methodological insights regarding the utility of mixed methods approaches for the analysis of large-scale data.

迫切需要对数据驱动的定向人道主义保护做法进行评估，因为这些做法提出了一系列直接影响风险人口的实际和道德问题。虽然通过编制数据来确定保护需求是常见做法，但人道主义领域内大规模的定量、生物识别和视觉数据的扩散是前所未有的。人道主义数据交换（HDX）成立于2014年，目前有来自1219个来源的253个地点的10，055个数据集。该项目对数据驱动的保护目标进行了及时和有力的分析，重点关注以冲突和大量境内流离失所者为特征的两种情况：北方尼日利亚和南苏丹。它评估了大规模数据的制作和使用对人道主义保护的影响，特别是对那些最直接受这种事态发展影响的人：国内流离失所者本身的影响。然而，当涉及到针对风险最大的人的数据驱动的保护实践时，风险尤其高。人道主义干预旨在保护最脆弱的群体，因此，在使用数据方面的任何滥用或错误计算都可能对风险人群产生巨大影响。因此，认真评估以数据为导向的保护目标的实际和道德影响至关重要。人们通常认为，人道主义者在整理和使用大规模数据方面比商业组织或政府更值得信任，因为他们的使命是“不伤害”。然而，至关重要的是要审查在编制和使用此类数据时隐含的潜在风险、排斥、偏见或脆弱性。虽然数据可以使更快，更有效和更好的循证反应，围绕数据收集过程的关键问题，什么算作证据，数据收集和使用的道德，以及在脆弱性背景下产生的数据的问责制和保护越来越有必要。我们的研究基于研究团队的跨学科学术和业务专业知识来解决这些问题。该项目汇集了来自英国和尼日利亚的学术研究人员与国际移民组织-联合国移民局的从业人员，该机构负责流离失所跟踪矩阵（DTM）。DTM是用于跟踪和监测流离失所和人口流动的系统，是全球最大的境内流离失所者数据收集者之一。我们的项目在更广泛的数据集的背景下，用于在北方尼日利亚和南苏丹的人道主义保护的目标检查DTM的工作。它侧重于收集和使用此类数据过程中出现的实际和道德挑战，并与当地利益攸关方和境内流离失所者社区开展深入研究。该项目提出的问题是：基于数据的人道主义保护目标确定在实践中有多有效？谁从这些做法中受益-谁被排除在外？如何发展数据驱动的目标定位，以减少弱势群体错过人道主义保护的机会？该项目有助于：对撒哈拉以南非洲地区数据驱动的人道主义目标的实际和道德影响的跨学科视角;对冲突和流离失所背景下数据驱动的人道主义保护的有效性进行业务驱动的分析;对制作和使用大规模数据对风险人群的影响进行定性评估;和方法论的见解，关于混合方法的效用方法分析大规模数据。

项目成果

Coloniality and frictions: Data-driven humanitarianism in North-Eastern Nigeria and South Sudan

殖民与摩擦：尼日利亚东北部和南苏丹数据驱动的人道主义

• DOI: 10.1177/20539517231163171
• 发表时间: 2023
• 期刊: Big Data & Society
• 影响因子: 8.5
• 作者: Squire V

Data and Displacement: Collaborative Research at a Time of Uncertainty

数据与位移：不确定时期的合作研究

• 发表时间: 2021
• 作者: Abimbola, O.

Data-based humanitarianism in Nigeria and South Sudan

尼日利亚和南苏丹的基于数据的人道主义

• 发表时间: 2022
• 作者: Fayehun, F

Situational Brief - Humanitarian Crises and Internally Displaced Persons (IDPs) in Nigeria

情况简介 - 尼日利亚的人道主义危机和境内流离失所者 (IDP)

• 发表时间: 2021
• 作者: Fayehun, F.

Data Gathering and Utilization: Humanitarian Targeting and Ethical Issues in Northeastern Nigeria

数据收集和利用：尼日利亚东北部的人道主义目标和道德问题

• 发表时间: 2023
• 期刊: Journal of International Humanitarian Action
• 作者: Akanle O.