On Meritocracy in Optimal Set Selection

We consider the problem of selecting a set of individuals from a candidate population in order to maximise utility. When the utility function is defined over sets, this raises the question of how to define meritocracy. We define and analyse an appropriate notion of meritocracy derived from the utility function. We introduce the notion of expected marginal contributions of individuals and analyse its links to the underlying optimisation problem, our notion of meritocracy, and other notions of fairness such as the Shapley value. We also experimentally analyse the effect of different policy structures on the utility and meritocracy in a simulated college admission setting including constraints on statistical parity

Equity of Attention: Amortizing Individual Fairness in Rankings

Rankings of people and items are at the heart of selection-making, match-making, and recommender systems, ranging from employment sites to sharing economy platforms. As ranking positions influence the amount of attention the ranked subjects receive, biases in rankings can lead to unfair distribution of opportunities and resources, such as jobs or income. This paper proposes new measures and mechanisms to quantify and mitigate unfairness from a bias inherent to all rankings, namely, the position bias, which leads to disproportionately less attention being paid to low-ranked subjects. Our approach differs from recent fair ranking approaches in two important ways. First, existing works measure unfairness at the level of subject groups while our measures capture unfairness at the level of individual subjects, and as such subsume group unfairness. Second, as no single ranking can achieve individual attention fairness, we propose a novel mechanism that achieves amortized fairness, where attention accumulated across a series of rankings is proportional to accumulated relevance. We formulate the challenge of achieving amortized individual fairness subject to constraints on ranking quality as an online optimization problem and show that it can be solved as an integer linear program. Our experimental evaluation reveals that unfair attention distribution in rankings can be substantial, and demonstrates that our method can improve individual fairness while retaining high ranking quality.Comment: Accepted to SIGIR 201

Meritocratic matching can dissolve the efficiency-equality tradeoff: the case of voluntary contributions

One of the fundamental tradeoffs underlying society is that between efficiency and equality. The challenge for institutional design is to strike the right balance between these two goals. Game-theoretic models of public-goods provision under ‘meritocratic matching’ succinctly capture this tradeoff: under zero meritocracy (society is randomly formed), theory predicts maximal inefficiency but perfect equality; higher levels of meritocracy (society matches contributors with contributors) are predicted to improve efficiency but come at the cost of growing inequality. We conduct an experiment to test this tradeoff behaviorally and make the astonishing finding that, notwithstanding theoretical predictions, higher levels of meritocracy increase both efficiency and equality, that is, meritocratic matching dissolves the tradeoff. Fairness considerations can explain the departures from theoretical predictions including the behavioral phenomena that lead to dissolution of the efficiency-equality tradeoff

Operationalizing Individual Fairness with Pairwise Fair Representations

We revisit the notion of individual fairness proposed by Dwork et al. A central challenge in operationalizing their approach is the difficulty in eliciting a human specification of a similarity metric. In this paper, we propose an operationalization of individual fairness that does not rely on a human specification of a distance metric. Instead, we propose novel approaches to elicit and leverage side-information on equally deserving individuals to counter subordination between social groups. We model this knowledge as a fairness graph, and learn a unified Pairwise Fair Representation (PFR) of the data that captures both data-driven similarity between individuals and the pairwise side-information in fairness graph. We elicit fairness judgments from a variety of sources, including human judgments for two real-world datasets on recidivism prediction (COMPAS) and violent neighborhood prediction (Crime & Communities). Our experiments show that the PFR model for operationalizing individual fairness is practically viable.Comment: To be published in the proceedings of the VLDB Endowment, Vol. 13, Issue.

Quotas for Men: Reframing Gender Quotas as a Means of Improving Representation for All

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Wealthy Americans and redistribution:The role of fairness preferences

We examine the attitudes of the wealthy towards government redistribution using a large and diverse sample of individuals from the top 5% of the income and wealth distribution in the U.S., as well as the remaining 95%. Three results stand out: (1) wealthy Americans have distinct fairness preferences, with a greater willingness to accept inequalities relative to the general public, (2) individuals who self-report having experienced upward social mobility and became first-generation wealthy are particularly accepting of inequality, while those born into wealth have fairness preferences similar to the general population; (3) the disparity in fairness preferences between the rich and the general public is predictive of greater opposition to redistribution among the wealthy, resulting in more conservative voting behavior. These findings provide new insights into the reasons behind the wealthy's opposition to government redistribution.</p

Group Meritocratic Fairness in Linear Contextual Bandits

We study the linear contextual bandit problem where an agent has to select one candidate from a pool and each candidate belongs to a sensitive group. In this setting, candidates⧠rewards may not be directly comparable between groups, for example when the agent is an employer hiring candidates from different ethnic groups and some groups have a lower reward due to discriminatory bias and/or social injustice. We propose a notion of fairness that states that the agent* policy is fair when it selects a candidate with highest relative rank, which measures how good the reward is when compared to candidates from the same group. This is a very strong notion of fairness, since the relative rank is not directly observed by the agent and depends on the underlying reward model and on the distribution of rewards. Thus we study the problem of learning a policy which approximates a fair policy under the condition that the contexts are independent between groups and the distribution of rewards of each group is absolutely continuous. In particular, we design a greedy policy which at each round constructs a ridge regression estimate from the observed context-reward pairs, and then computes an estimate of the relative rank of each candidate using the empirical cumulative distribution function. We prove that, despite its simplicity and the lack of an initial exploration phase, the greedy policy achieves, up to log factors and with high probability, a fair pseudo-regret of order √dT after T rounds, where d is the dimension of the context vectors. The policy also satisfies demographic parity at each round when averaged over all possible information available before the selection. Finally, we use simulated settings and experiments on the US census data to show that our policy achieves sub-linear fair pseudo-regret also in practice

Meritocracy: A widespread ideology due to school socialization?

The following research focuses on the perception of meritocracy and the support for an education-based meritocracy among individuals. The impact of education at both micro- (individual) and macro- (country) level has been closely investigated through this study, as education is supposed to influence the support for dominant ideologies since Bourdieu and Passeron (1970). However, these researchers propose no empirical evidence for their theory. Moreover, the influence of education is not straightforward, as education may have contradictory effects on the justification of social inequalities (Baer and Lambert 1982), and the impact of education may be different at the individual level or at the country level. Comparative data from ISSP Social Inequality III (1999) survey were examined. Multilevel analysis has been conducted on these data. It has been proved that, at the individual level, education is effective in strengthening the support for education-based meritocracy but it has a more uncertain impact on the perception of social positions as deserved. At the macro level, some national patterns also have an impact on perceived and preferred meritocracy. Perceived meritocracy proves to be correlated with the expansion of the educational system, while the support for education-based meritocracy is correlated with the average returns to education in a country. Beyond educational characteristics, our results show that other economic and social variables can affect representations, such as gender and age at th

Meritocracy-Based Stickiness Measure of Social Mobility

I measure the stickiness of social mobility in terms of meritocratic assumptions through the first-known Meritocracy-Based Stickiness Measure of Mobility (MBSMoM) using mobility transition matrices and assumptions based on Full Meritocracy (FM) and Lack of Meritocracy (LM). I develop the Simple Stickiness Measure of Mobility (SSMoM) and the Weighted Stickiness Measure of Mobility (WSMoM). In addition, I create the MBSMoM which is calculated from mobility transition matrices of intragenerational, intergenerational, and multigenerational correlations using various measures of status including education, occupation, class, consumption, income, and wealth. Utilizing mobility transition matrices employed by plethora of studies, MBSMoMs are calculated as a percentage between SSMoMs or WSMoMs under assumptions of FM and LM. The MBSMoM is a standalone measure and is interpreted as the percentage between outcomes under FM and LM assumptions. I calculate MBSMoM values for 92 mobility matrices from 22 previous studies of mobility and report individual and group results