Gender Discriminatory Taxes, Fairness Perception, and Labor Supply

In this paper, we examine the gender specific impact of discriminatory taxation on fairness perception and individual labor supply decisions. Using the controlled environment of an experimental laboratory, we manipulate both distributional as well as procedural justice of taxation between subjects. We violate distributional fairness through the random application of tax rates, while procedural justice is broken by levying discriminatory tax rates based on taxpayer gender. For both inequality in outcome as well as discrimination, we find strong differences in reactions between male and female participants. Male participants perceived gender discriminatory taxation as unfair in and of itself. Female participants perceived random taxation as well as gender discriminatory taxation to be unfair, as long as they ended up with the higher tax rate. The perceived fairness strongly drove (did not affect) male (female) participants’ labor supply. Taken both subgroups together, while mere outcome inequality did not influence labor supply decisions significantly, we find evidence of a negative effect of gender-based discrimination on labor supply

Equity and economic theory: reflections on methodology and scope

This paper provides an introduction to the recent literature on ordinal distributive justice. Its objetive is to explain the process of the mathematical analysis of fairness and to consider its potential for solving real allocative problems by means of several illustrative examples

Preference-Informed Fairness

We study notions of fairness in decision-making systems when individuals have diverse preferences over the possible outcomes of the decisions. Our starting point is the seminal work of Dwork et al. which introduced a notion of individual fairness (IF): given a task-specific similarity metric, every pair of individuals who are similarly qualified according to the metric should receive similar outcomes. We show that when individuals have diverse preferences over outcomes, requiring IF may unintentionally lead to less-preferred outcomes for the very individuals that IF aims to protect. A natural alternative to IF is the classic notion of fair division, envy-freeness (EF): no individual should prefer another individual's outcome over their own. Although EF allows for solutions where all individuals receive a highly-preferred outcome, EF may also be overly-restrictive. For instance, if many individuals agree on the best outcome, then if any individual receives this outcome, they all must receive it, regardless of each individual's underlying qualifications for the outcome. We introduce and study a new notion of preference-informed individual fairness (PIIF) that is a relaxation of both individual fairness and envy-freeness. At a high-level, PIIF requires that outcomes satisfy IF-style constraints, but allows for deviations provided they are in line with individuals' preferences. We show that PIIF can permit outcomes that are more favorable to individuals than any IF solution, while providing considerably more flexibility to the decision-maker than EF. In addition, we show how to efficiently optimize any convex objective over the outcomes subject to PIIF for a rich class of individual preferences. Finally, we demonstrate the broad applicability of the PIIF framework by extending our definitions and algorithms to the multiple-task targeted advertising setting introduced by Dwork and Ilvento

Peer feedback content and sender’s competence level in academic writing revision tasks: Are they critical for feedback perceptions and efficiency?

Peer feedback content is a core component of peer assessment, but the impact of various contents of feedback is hardly studied. Participants in the study were 89 graduate students who were assigned to four experimental and a control group. Experimental groups received a scenario with Concise General (CGF) or Elaborated Specific (ESF) feedback by a high or low competent peer. ESF by a high competent peer was perceived as more adequate, but led to more negative affect. Students in CGF groups outperformed ESF groups during treatment. Groups with a low competent peer outperformed groups with a high competent peer during the posttest. Feedback perceptions and performance were uncorrelated

Avoiding Discrimination through Causal Reasoning

Recent work on fairness in machine learning has focused on various statistical discrimination criteria and how they trade off. Most of these criteria are observational: They depend only on the joint distribution of predictor, protected attribute, features, and outcome. While convenient to work with, observational criteria have severe inherent limitations that prevent them from resolving matters of fairness conclusively. Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning. This viewpoint shifts attention from "What is the right fairness criterion?" to "What do we want to assume about the causal data generating process?" Through the lens of causality, we make several contributions. First, we crisply articulate why and when observational criteria fail, thus formalizing what was before a matter of opinion. Second, our approach exposes previously ignored subtleties and why they are fundamental to the problem. Finally, we put forward natural causal non-discrimination criteria and develop algorithms that satisfy them.Comment: Advances in Neural Information Processing Systems 30, 2017 http://papers.nips.cc/paper/6668-avoiding-discrimination-through-causal-reasonin

Open Door Policies: Measuring Impact Using Attitude Surveys

This study examines employee perceptions of an Open Door Complaint System from both those who have filed claims and those who have not. Our sample includes over 4000 employees working in a Fortune 100 company. We examine these perceptions through an organization wide employee attitude survey. Analyzing situation specific perceptions, we examine their relationship with overall fairness, satisfaction and intent to remain with the organization. Results suggest that a positive Open Door incident raises both distributive and procedural justice perceptions. In turn, fairness perceptions influence satisfaction levels. Finally, results indicate that satisfaction has a strong effect on the intent to remain with the organization. Implications are discussed for both complaint systems and employee opinion surveys

50 Years of Test (Un)fairness: Lessons for Machine Learning

Quantitative definitions of what is unfair and what is fair have been introduced in multiple disciplines for well over 50 years, including in education, hiring, and machine learning. We trace how the notion of fairness has been defined within the testing communities of education and hiring over the past half century, exploring the cultural and social context in which different fairness definitions have emerged. In some cases, earlier definitions of fairness are similar or identical to definitions of fairness in current machine learning research, and foreshadow current formal work. In other cases, insights into what fairness means and how to measure it have largely gone overlooked. We compare past and current notions of fairness along several dimensions, including the fairness criteria, the focus of the criteria (e.g., a test, a model, or its use), the relationship of fairness to individuals, groups, and subgroups, and the mathematical method for measuring fairness (e.g., classification, regression). This work points the way towards future research and measurement of (un)fairness that builds from our modern understanding of fairness while incorporating insights from the past.Comment: FAT* '19: Conference on Fairness, Accountability, and Transparency (FAT* '19), January 29--31, 2019, Atlanta, GA, US