Papers
Topics
Authors
Recent
Search
2000 character limit reached

Spatial Fairness: The Case for its Importance, Limitations of Existing Work, and Guidelines for Future Research

Published 20 Mar 2024 in cs.CY | (2403.14040v1)

Abstract: Despite location being increasingly used in decision-making systems employed in many sensitive domains such as mortgages and insurance, astonishingly little attention has been paid to unfairness that may seep in due to the correlation of location with characteristics considered protected under anti-discrimination law, such as race or national origin. This position paper argues for the urgent need to consider fairness with respect to location, termed \textit{spatial fairness}, by outlining the harms that continue to be perpetuated due to location's correlation with protected characteristics. This interdisciplinary work connects knowledge from fields such as public policy, economic development, and geography to highlight how fair-AI research currently falls short of correcting for spatial biases, and does not consider challenges unique to spatial data. Furthermore, we identify limitations of the handful of spatial fairness work proposed so far, and finally, detail guidelines for future research so subsequent work may avoid such issues and help correct spatial biases.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (118)
  1. State fair housing protections. temple university center for public health law research, policy surveillance program. URL https://lawatlas.org/datasets/state-fair-housing-protections-1498143743.
  2. URL https://nlihc.org/resource/14-1-advancing-tenant-protections-source-income-protections#:~:text=Source%2Dof%2Dincome%20laws%20gained,holders%20between%202019%20and%202022.
  3. Time-dependent reachability analysis: A data-driven approach. In 2019 20th IEEE International Conference on Mobile Data Management (MDM), pp.  138–143. IEEE, 2019.
  4. Anderson, S. G. Ensuring the stability of welfare-to-work exits: The importance of recipient knowledge about work incentives. Social Work, 47(2):162–170, 2002.
  5. Minority neighborhoods pay higher car insurance premiums than white areas with the same risk. ProPublica, April, 5:2017, 2017.
  6. The measure of distance in a social science policy context: Advantages and costs of using network distances in eight canadian metropolitan areas. Journal of Geographic Information and Decision Analysis, 7(2):105–131, 2003.
  7. Big data’s disparate impact. California law review, pp.  671–732, 2016.
  8. Supreme court affirms fha disparate impact claims, 2015.
  9. Becker, G. S. The economics of discrimination. University of Chicago press, 2010.
  10. A convex framework for fair regression. arXiv preprint arXiv:1706.02409, 2017.
  11. Brata, A. G. et al. Do geographic factors determine local economic development? Economics, Management, and Financial Markets, 4(3):170–189, 2009.
  12. Spatial data discretization methods for geocomputation. International journal of applied earth observation and geoinformation, 26:432–440, 2014.
  13. Fairness under unawareness: Assessing disparity when protected class is unobserved. In Proceedings of the conference on fairness, accountability, and transparency, pp.  339–348, 2019.
  14. The effects of exposure to better neighborhoods on children: New evidence from the moving to opportunity experiment. American Economic Review, 106(4):855–902, 2016.
  15. A fair classifier using kernel density estimation. Advances in neural information processing systems, 33:15088–15099, 2020.
  16. The measure and mismeasure of fairness: A critical review of fair machine learning. arXiv preprint arXiv:1808.00023, 2018.
  17. Flexibly fair representation learning by disentanglement. In International conference on machine learning, pp.  1436–1445. PMLR, 2019.
  18. Proxy non-discrimination in data-driven systems. arXiv preprint arXiv:1707.08120, 2017.
  19. Accounting for spatial autocorrelation in algorithm-driven hedonic models: A spatial cross-validation approach. The Journal of Real Estate Finance and Economics, pp.  1–39, 2022.
  20. Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference, pp.  214–226, 2012.
  21. Economic growth and economic development: Geographical dimensions, definition, and disparities. The new Oxford handbook of economic geography, 143:143–157, 2018.
  22. Certifying and removing disparate impact. In proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp.  259–268, 2015.
  23. The modifiable areal unit problem in multivariate statistical analysis. Environment and planning A, 23(7):1025–1044, 1991.
  24. Fulwood III, S. The costs of segregation and the benefits of the fair housing act. In The Fight for Fair Housing, pp.  40–56. Routledge, 2017.
  25. The dialectic of neighborhood social mix: Editors’ introduction to the special issue. Housing Studies, 30(2):175–191, 2015.
  26. California’s fair employment and housing act: A viable state remedy for employment discrimination. Hastings LJ, 34:1055, 1982.
  27. Glasmeier, A. K. Income inequality and growing disparity: Spatial patterns of inequality and the case of the usa. The new Oxford handbook of economic geography, pp.  63–77, 2018.
  28. The case for process fairness in learning: Feature selection for fair decision making. In NIPS symposium on machine learning and the law, volume 1, pp.  11. Barcelona, Spain, 2016.
  29. Modifiable areal units: Problem or perception in modeling of residential location choice? Transportation Research Record, 1898(1):138–147, 2004.
  30. Guttman, A. R-trees: A dynamic index structure for spatial searching. In Proceedings of the 1984 ACM SIGMOD international conference on Management of data, pp.  47–57, 1984.
  31. Haining, R. P. Spatial data analysis: theory and practice. Cambridge university press, 2003.
  32. A geometric solution to fair representations. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp.  279–285, 2020.
  33. A multiscalar approach for identifying clusters and segregation patterns that avoids the modifiable areal unit problem. Annals of the American Association of Geographers, 107(3):555–574, 2017.
  34. Hoffman, S. The importance of immutability in employment discrimination law. Wm. & Mary L. Rev., 52:1483, 2010.
  35. The increasing effect of neighborhood racial composition on housing values, 1980–2015. Social Problems, 68(4):1051–1071, 2021.
  36. Low-income students sacrifice for better education, Apr 2016. URL https://gunnoracle.com/7456/news/low-income-students-sacrifice-for-better-education/.
  37. Fair representation learning using interpolation enabled disentanglement. arXiv preprint arXiv:2108.00295, 2021.
  38. Generalized demographic parity for group fairness. In International Conference on Learning Representations, 2022.
  39. Discrimination aware decision tree learning. In 2010 IEEE international conference on data mining, pp.  869–874. IEEE, 2010.
  40. Rising spatial disparities and development. 2005.
  41. Kaplan, D. H. The spatial structure of urban ethnic economies. Urban geography, 19(6):489–501, 1998.
  42. Spatial autocorrelation incorporated machine learning model for geotechnical subsurface modeling. Applied Sciences, 13(7):4497, 2023.
  43. Kim, P. T. Race-aware algorithms: Fairness, nondiscrimination and affirmative action. Cal. L. Rev., 110:1539, 2022.
  44. Spatial disparities and development policy. World Bank Publications, 2009.
  45. Job accessibility, residential segregation and risk of long-term unemployment in the paris region. Urban Studies, 47(11):2279–2324, 2010.
  46. Counterfactual fairness. Advances in neural information processing systems, 30, 2017.
  47. Algorithmic fairness in mortgage lending: from absolute conditions to relational trade-offs. Minds and Machines, 31(1):165–191, 2021.
  48. Race and regionalism: The structure of local government and racial disparity. Urban Affairs Review, 47(3):349–384, 2011.
  49. Residential segregation, spatial mismatch and economic growth across us metropolitan areas. Urban Studies, 50(13):2642–2660, 2013.
  50. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926, 2017.
  51. Incorporating spatial autocorrelation in machine learning models using spatial lag and eigenvector spatial filtering features. ISPRS International Journal of Geo-Information, 11(4):242, 2022.
  52. MacCarthy, M. Standards of fairness for disparate impact assessment of big data algorithms. Cumb. L. Rev., 48:67, 2017.
  53. Detecting bias in the presence of spatial autocorrelation. In Algorithmic Fairness through the Lens of Causality and Robustness workshop, pp.  6–18. PMLR, 2022.
  54. Manley, D. Scale, aggregation, and the modifiable areal unit problem. In Handbook of regional science, pp.  1711–1725. Springer, 2021.
  55. Marshall, R. The economics of racial discrimination: A survey. Journal of Economic Literature, 12(3):849–871, 1974.
  56. Martin, D. Extending the automated zoning procedure to reconcile incompatible zoning systems. International Journal of Geographical Information Science, 17(2):181–196, 2003.
  57. Fairness-aware learning for continuous attributes and treatments. In International Conference on Machine Learning, pp.  4382–4391. PMLR, 2019.
  58. Riding the stagecoach to hell: A qualitative analysis of racial discrimination in mortgage lending. City & Community, 15(2):118–136, 2016.
  59. Where families with children use housing vouchers. Center on Budget and Policy Priorities, 3, 2019.
  60. McMillen, D. P. Issues in spatial data analysis. Journal of Regional Science, 50(1):119–141, 2010.
  61. A survey on bias and fairness in machine learning. ACM computing surveys (CSUR), 54(6):1–35, 2021.
  62. Algorithmic impact assessments and accountability: The co-construction of impacts. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pp.  735–746, 2021.
  63. Built environment correlates of active school transportation: neighborhood and the modifiable areal unit problem. Journal of transport geography, 20(1):51–61, 2012.
  64. Race, neighborhood economic status, income inequality and mortality. PloS one, 11(5):e0154535, 2016.
  65. Opening doorways to health care for children: 10 steps to ensure eligible but uninsured children get health insurance. 2006.
  66. Evaluating data stability in aggregation structures across spatial scales: revisiting the modifiable areal unit problem. Cartography and Geographic Information Science, 44(1):35–50, 2017.
  67. Beyond individual neighborhoods: a geography of opportunity perspective for understanding racial/ethnic health disparities. Health & place, 16(6):1113–1123, 2010.
  68. The sociology of discrimination: Racial discrimination in employment, housing, credit, and consumer markets. Annu. Rev. Sociol, 34:181–209, 2008.
  69. Disparate impact of artificial intelligence bias in ridehailing economy’s price discrimination algorithms. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, pp.  822–833, 2021.
  70. America’s formerly redlined neighborhoods have changed, and so must solutions to rectify them. 2019.
  71. A review on fairness in machine learning. ACM Computing Surveys (CSUR), 55(3):1–44, 2022.
  72. Primus, R. The future of disparate impact. Michigan Law Review, pp.  1341–1387, 2010.
  73. Algorithmic impact assessments: A practical framework for public agency. AI Now, 2018.
  74. Linking users across domains with location data: Theory and validation. In Proceedings of the 25th international conference on world wide web, pp.  707–719, 2016.
  75. Fr-train: A mutual information-based approach to fair and robust training. In International Conference on Machine Learning, pp.  8147–8157. PMLR, 2020.
  76. A multidisciplinary survey on discrimination analysis. The Knowledge Engineering Review, 29(5):582–638, 2014.
  77. Mortgage lending in chicago and los angeles: A paired testing study of the pre-application process. Journal of Urban Economics, 63(3):902–919, 2008.
  78. Rothstein, R. The racial achievement gap, segregated schools, and segregated neighborhoods: A constitutional insult. Race and social problems, 7:21–30, 2015.
  79. Rothstein, R. The color of law: A forgotten history of how our government segregated America. Liveright Publishing, 2017.
  80. Foreign-born population concentration and neighbourhood growth and development within us metropolitan areas. Urban Studies, 55(4):826–843, 2018.
  81. Auditing for spatial fairness. arXiv preprint arXiv:2302.12333, 2023.
  82. Immigration and the neighborhood. American Economic Journal: Economic Policy, 3(2):169–188, 2011.
  83. Salcedo, A. State senator wants to bar zip code from car insurance equation, Apr 2017. URL https://chicago.suntimes.com/2017/4/17/18407951/state-senator-wants-to-bar-zip-code-from-car-insurance-equation.
  84. The price of fair pca: One extra dimension. Advances in neural information processing systems, 31, 2018.
  85. Sanchez, T. W. An inherent bias. The Brookings Institution Series on Transportation Reform, 2006.
  86. How do you measure distance in spatial models? an example using open-space valuation. Environment and Planning B: Planning and Design, 37(5):874–894, 2010.
  87. Using the areal unit segregation measure to identify racially “packed” and “cracked” legislative districts. Electoral Studies, 80:102526, 2022.
  88. How do fairness definitions fare? examining public attitudes towards algorithmic definitions of fairness. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pp.  99–106, 2019.
  89. Missed opportunities in fair ai. In Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), pp.  961–964. SIAM, 2023a.
  90. Unveiling and mitigating bias in ride-hailing pricing for equitable policy making. arXiv preprint arXiv:2301.03489, 2023b.
  91. Spatial bias of federal housing law and policy: Concentrated poverty in urban america. U. Pa. L. Rev., 143:1285, 1994.
  92. Housing market constraints and spatial stratification by income and race. Housing Policy Debate, 6(1):141–167, 1995.
  93. Fairness and abstraction in sociotechnical systems. In Proceedings of the conference on fairness, accountability, and transparency, pp.  59–68, 2019.
  94. Combining spatial autocorrelation with machine learning increases prediction accuracy of soil heavy metals. Catena, 174:425–435, 2019.
  95. Models and mechanisms for spatial data fairness. arXiv preprint arXiv:2204.01880, 2022.
  96. Sharkey, P. Spatial segmentation and the black middle class. American Journal of Sociology, 119(4):903–954, 2014.
  97. Privileged places: Race, uneven development and the geography of opportunity in urban america. Urban Studies, 42(1):47–68, 2005.
  98. Stigma and other determinants of participation in tanf and medicaid. Journal of Policy Analysis and Management, 23(3):509–530, 2004.
  99. Multi-criteria dimensionality reduction with applications to fairness. Advances in neural information processing systems, 32, 2019.
  100. Wealth gaps rise to record highs between Whites, Blacks and Hispanics, volume 37. Pew Research Center Washington, DC, 2011.
  101. Tuson, M. New geographical and statistical methods to address the modifiable areal unit problem, with applications in health. 2022.
  102. Incorporating geography into a new generalized theoretical and statistical framework addressing the modifiable areal unit problem. International journal of health geographics, 18:1–15, 2019.
  103. Actionable recourse in linear classification. In Proceedings of the conference on fairness, accountability, and transparency, pp.  10–19, 2019.
  104. Fairness definitions explained. In Proceedings of the international workshop on software fairness, pp.  1–7, 2018.
  105. Why fairness cannot be automated: Bridging the gap between eu non-discrimination law and ai. Computer Law & Security Review, 41:105567, 2021.
  106. Willborn, S. L. The disparate impact model of discrimination: Theory and limits. Am. UL Rev., 34:799, 1984.
  107. Wong, D. W. The modifiable areal unit problem (maup). In WorldMinds: geographical perspectives on 100 problems: commemorating the 100th anniversary of the association of American geographers 1904–2004, pp.  571–575. Springer, 2004.
  108. Woods, L. L. The federal home loan bank board, redlining, and the national proliferation of racial lending discrimination, 1921–1950. Journal of Urban History, 38(6):1036–1059, 2012.
  109. Need for and barriers to accessing public benefits among low-income families with children. Children and Youth Services Review, 32(1):58–66, 2010.
  110. Xiang, A. Reconciling legal and technical approaches to algorithmic bias. Tenn. L. Rev., 88:649, 2020.
  111. On the legal compatibility of fairness definitions. arXiv preprint arXiv:1912.00761, 2019.
  112. Fairness by “where”: A statistically-robust and model-agnostic bi-level learning framework. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pp.  12208–12216, 2022.
  113. The modifiable areal unit problem in traffic safety: Basic issue, potential solutions and future research. Journal of traffic and transportation engineering (English edition), 5(1):73–82, 2018.
  114. From parity to preference-based notions of fairness in classification. Advances in neural information processing systems, 30, 2017.
  115. Zedlewski, S. R. How much do welfare recipients know about time limits? 2003.
  116. Zedlewski, S. R. et al. Left behind or staying away? eligible parents who remain off tanf. 2002.
  117. Unequal economic development in china: spatial disparities and regional policy reconsideration, 1985-1995. Regional studies, 34(6):549–561, 2000.
  118. You are where you go: Inferring demographic attributes from location check-ins. In Proceedings of the eighth ACM international conference on web search and data mining, pp.  295–304, 2015.
Citations (1)
View on Semantic Scholar

Summary

No one has generated a summary of this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Sign Up to Summarize

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up