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Algorithmic Pluralism: A Structural Approach To Equal Opportunity

Shomik Jain, Vinith Suriyakumar, Kathleen Creel, Ashia Wilson

TL;DR

The paper addresses how algorithmic decision-making can constrain access to opportunity through structural bottlenecks that reproduce inequality. It extends Joseph Fishkin's bottleneck theory to algorithms, introducing algorithmic pluralism as a policy and design goal aimed at reducing bottleneck severity and increasing opportunities for diverse individuals. The authors argue that promoting a pluralism of opportunity—many paths to valued outcomes via varied criteria, institutions, and decision-makers—has practical implications for algorithmic hiring, regulation, and system design. They illustrate these ideas with a case study on hiring and discuss regulatory and design interventions to reduce severe bottlenecks and mitigate monocultures, highlighting the potential for meaningful social impact in high-stakes domains.

Abstract

We present a structural approach toward achieving equal opportunity in systems of algorithmic decision-making called algorithmic pluralism. Algorithmic pluralism describes a state of affairs in which no set of algorithms severely limits access to opportunity, allowing individuals the freedom to pursue a diverse range of life paths. To argue for algorithmic pluralism, we adopt Joseph Fishkin's theory of bottlenecks, which focuses on the structure of decision-points that determine how opportunities are allocated. The theory contends that each decision-point or bottleneck limits access to opportunities with some degree of severity and legitimacy. We extend Fishkin's structural viewpoint and use it to reframe existing systemic concerns about equal opportunity in algorithmic decision-making, such as patterned inequality and algorithmic monoculture. In proposing algorithmic pluralism, we argue for the urgent priority of alleviating severe bottlenecks in algorithmic decision-making. We contend that there must be a pluralism of opportunity available to many different individuals in order to promote equal opportunity in a systemic way. We further show how this framework has several implications for system design and regulation through current debates about equal opportunity in algorithmic hiring.

Algorithmic Pluralism: A Structural Approach To Equal Opportunity

TL;DR

The paper addresses how algorithmic decision-making can constrain access to opportunity through structural bottlenecks that reproduce inequality. It extends Joseph Fishkin's bottleneck theory to algorithms, introducing algorithmic pluralism as a policy and design goal aimed at reducing bottleneck severity and increasing opportunities for diverse individuals. The authors argue that promoting a pluralism of opportunity—many paths to valued outcomes via varied criteria, institutions, and decision-makers—has practical implications for algorithmic hiring, regulation, and system design. They illustrate these ideas with a case study on hiring and discuss regulatory and design interventions to reduce severe bottlenecks and mitigate monocultures, highlighting the potential for meaningful social impact in high-stakes domains.

Abstract

We present a structural approach toward achieving equal opportunity in systems of algorithmic decision-making called algorithmic pluralism. Algorithmic pluralism describes a state of affairs in which no set of algorithms severely limits access to opportunity, allowing individuals the freedom to pursue a diverse range of life paths. To argue for algorithmic pluralism, we adopt Joseph Fishkin's theory of bottlenecks, which focuses on the structure of decision-points that determine how opportunities are allocated. The theory contends that each decision-point or bottleneck limits access to opportunities with some degree of severity and legitimacy. We extend Fishkin's structural viewpoint and use it to reframe existing systemic concerns about equal opportunity in algorithmic decision-making, such as patterned inequality and algorithmic monoculture. In proposing algorithmic pluralism, we argue for the urgent priority of alleviating severe bottlenecks in algorithmic decision-making. We contend that there must be a pluralism of opportunity available to many different individuals in order to promote equal opportunity in a systemic way. We further show how this framework has several implications for system design and regulation through current debates about equal opportunity in algorithmic hiring.
Paper Structure (26 sections, 1 figure)

This paper contains 26 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Equal Opportunity and Its Limitations
  3. The problem of a starting gate
  4. The problem of isolating merit
  5. The problem of metrics and what is measured
  6. The problem of zero-sum thinking
  7. Bottleneck Theory: A Structural View of Equal Opportunity
  8. Opportunity Pluralism
  9. Bottlenecks
  10. Bottleneck Severity
  11. The concern about patterned inequality is a concern about severe bottlenecks
  12. The concern about algorithmic monoculture is a concern about severe bottlenecks
  13. Bottleneck Legitimacy
  14. The concern about compounding injustice is a concern about illegitimate bottlenecks.
  15. The concern about relational harms is a concern about illegitimate bottlenecks.
  16. ...and 11 more sections

Figures (1)

  • Figure 1: (Fig. 6 in fishkin2014bottlenecks) Fishkin's framework for classifying bottlenecks. He proposes mapping the structural impact of bottlenecks along the axes of severity and legitimacy.