Title：Spatial Dimensions of Algorithmic Transparency: A Summary
Related Authors: Corey Kewei Xu – HKUST（GZ）
Fields: SDGs,Policy
Keywords: Fairness, Accountability, and Transparency, Public policy, Spatial data science, Urban Planning

Background

Spatial data brings an important dimension to AI’s quest for algorithmic transparency. (抽象空间数据为人工智能寻求算法透明性带来了一个重要的维度)
For example, data driven computer-aidedpolicy-decisions（计算机辅助政策决策） use measures of segregation (e.g., dissimilarity in-dex) or income-inequality (e.g., Gini index), and these measures areaffected by space partitioning choice.（空间划分选择）
This may lead policymakersto underestimate（低估） the level of inequality or segregation within aregion.
The problem stems from（源于） the fact that many segregation based analyses use aggregated census data（汇总的人口普查数据） but do not report result sensitivity to choice of spatial partitioning (e.g., census block, tract).
Currently, issues such as reducing global inequality is part of theUN Sustainability Development Goals (SDGs)

Methodology

mathematical proofs
case studies（ with census data and census based synthetic micro-population data）（using ArcGIS Pro）
Computationally, this work was done in Python using the propri-etary library “ArcPy” from the company Esri; NumPy and pandaswere used for data processing.（避免数值溢出）

values of many measures (e.g., Gini index, dissimilarity index) diminish monotonically（单调递减） with increasing spatial-unit size （空间单元大小的增加）in a hierarchical space partitioning（分层空间分区） (e.g., block, block-group, tract),
however， the ranking based on spatially（存在于空间地的） aggregated measures remain sensitive to the scale of spatial partitions (e.g., block, block group).
This paper highlights the need for social scientists to report how rankings of inequality are affected by the choice of spatial partitions（空间分区选择）.

Our findings highlight the importance of collecting and using fine-scale data to inform policies that address various social equity issues.
We highlight the spatial dimension of algorithmic transparency with findings that reinforce the need to report the sensitivity of results to the choice of spatial partitioning.（空间划分的选择，应该进行敏感性分析）
Beyond MAUP, we show theoretically that some measures (e. G., gini index, index of dissimilarity) of inequality decrease monotonically with increasing scale of analysis.（划分空间越大，这些值越小）
We provide formal proofs on the upper and lower bound of the IOSR.（给出了形式证明）（收入五分位数份额比率（IQSR）：IQSR是最高20%的收入者获得的总收入与最低20%的收入者获得的收入之比。）
case study supports the theoretical results
- using a synthetic house- hold level dataset
- using a 2010 American Community Survey dataset
- on various income inequality measures (Gini Index, IOSR, Theil, and Atkinson)
have broad implications for equity related policy making
- Policymakers should be cautious about the unit of analysis when using existing equity indexes
- should conduct sensitivity analysis with different spatial partitioning choices

plan to analyze the sensitivity of other measures of income inequality and segregation to choice of spatial partitioning.
investigate computational methods to address the spatial dimensions of algorithmic transparency.(解决算法透明度的空间维度)
In addition, we plan to consider other aggregation problems such as boundary effects（边界效应）. Computationally, random partitioning techniquescan(随机分区算法) be improved to reduce complexity and produce more optimal zones.
Monte Carlo simulations can be used to estimate the effects of space partitioning for different zones.（蒙特卡罗模拟估计不同分区的空间划分效果）

Did not read the mathematical proofs very carefully.
I am interested in algorithmic transparency and measures of income inequality and segregation to choice of spatial partitioning.
In terms of research methodology, I need to learn the processing of geographic and spatial data（ArcPy、ArcGIS Pro）, as well as the systematic study of spatial spillover effects, boundary effects and other spatial measurement theories.
It is important to take notes and think while reading the literature.

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