The willingness of those in power to act fairly depends on how easily others can collectively push back against unfair treatment
Children’s Sense of Fairness as Equal Respect
One influential view holds that children’s sense of fairness emerges at age 8 and is rooted in the development of an aversion to unequal resource distributions. Here, we suggest two amendments to this view. First, we argue and present evidence that children’s sense of fairness emerges already at age 3 in (and only in) the context of collaborative activities. This is because, in our theoretical view, collaboration creates a sense of equal respect among partners. Second, we argue and present evidence that children’s judgments about what is fair are essentially judgments about the social meaning of the distributive act; for example, children accept unequal distributions if the procedure gave everyone an equal chance (so-called distributive justice). Children thus respond to unequal (and other) distributions not based on material concerns, but rather based on interpersonal concerns: they want equal respect.
FAIR
FAIR is the national progressive media watchdog group, challenging corporate media bias, spin and misinformation.
The myth of meritocracy: who really gets what they deserve?
The long read: Sorting people by ‘merit’ will do nothing to fix inequality
Meritocracy: the great delusion that ingrains inequality | Jo Littler
From Theresa May to Donald Trump, our leaders sell it as a utopian system of fairness – but ‘merit’ has been manipulated to privilege the wealthy
Another now: dispatches from an alternative present
What would a fair and equal society actually look like?…
FAIRdata.ai — FAIR Data Assessment
Assess your research data's FAIRness. Automated pipeline using F-UJI + Claude AI. Free to use.
Scientific institutions have a long history of anticipatory obedience
Societies should learn from this and speak up to support inclusion
Certifying and Removing Disparate Impact
What does it mean for an algorithm to be biased? In U.S. law, unintentional bias is encoded via disparate impact, which occurs when a selection process has widely different outcomes for different groups, even as it appears to be neutral. This legal determination hinges on a definition of a protected class (ethnicity, gender) and an explicit description of the process.When computers are involved, determining disparate impact (and hence bias) is harder. It might not be possible to disclose the process. In addition, even if the process is open, it might be hard to elucidate in a legal setting how the algorithm makes its decisions. Instead of requiring access to the process, we propose making inferences based on the data it uses.We present four contributions. First, we link disparate impact to a measure of classification accuracy that while known, has received relatively little attention. Second, we propose a test for disparate impact based on how well the protected class can be predicted from the other attributes. Third, we describe methods by which data might be made unbiased. Finally, we present empirical evidence supporting the effectiveness of our test for disparate impact and our approach for both masking bias and preserving relevant information in the data. Interestingly, our approach resembles some actual selection practices that have recently received legal scrutiny.
Les mesures de discrimination positive bénéficient-elles aux populations qu’elles ciblent ?
Les mesures de discrimination positive sont apparues il y a un demi-siècle : elles instaurant un traitement inégal des individus en vue de réduire les inégalités. Aux Etats-Unis, des politiques de discrimination positive (sous le nom d’« affirmative action...
Characterizing Fairness Over the Set of Good Models Under Selective Labels
Algorithmic risk assessments are used to inform decisions in a wide variety of high-stakes settings. Often multiple predictive models deliver similar overall performance but differ markedly in their predictions for individual cases, an empirical phenomenon known as the “Rashomon Effect.” These models may have different properties over various groups, and therefore have different predictive fairness properties. We develop a framework for characterizing predictive fairness properties over the set of models that deliver similar overall performance, or “the set of good models.” Our framework addresses the empirically relevant challenge of selectively labelled data in the setting where the selection decision and outcome are unconfounded given the observed data features. Our framework can be used to 1) audit for predictive bias; or 2) replace an existing model with one that has better fairness properties. We illustrate these use cases on a recidivism prediction task and a real-world credit-scoring task.
How Field Experiments in Economics Can Complement Psychological Research on Judgment Biases
This review summarizes results of field experiments examining individual behaviors across several market settings—from open-air markets to rideshare markets to tax-compliance markets—where people sort themselves into market roles wherein they make consequential decisions. Using three distinct examples from my own research on the endowment effect, left-digit bias, and omission bias, I showcase how field experiments can help researchers understand mediators, heterogeneity, and causal moderation involved in judgment biases in the field. In this manner, the review highlights that economic field experiments can serve an invaluable intellectual role alongside traditional laboratory research.
Choice Bracketing
When making many choices, a person can broadly bracket them by assessing the consequences of all of them taken together, or narrowly bracket them by making each choice in isolation. We integrate research conducted in a wide range of decision contexts which shows that choice bracketing is an important determinant of behavior. Because broad bracketing allows people to take into account all the consequences of their actions, it generally leads to choices that yield higher utility. The evidence that we review, however, shows that people often fail to bracket broadly when it would be feasible for them to do so. In addition to documenting the diverse effects of bracketing, we also discuss factors that determine whether people bracket narrowly or broadly. We conclude with a discussion of normative aspects of bracketing and argue that there are some situations in which narrower bracketing results in superior decision making.
If you rise, I fall: Equality is prevented by the misperception that it harms advantaged groups
Nine preregistered studies ( n = 4197) demonstrate that advantaged group members misperceive equality as necessarily harming their access to resources and inequality as necessarily benefitting them. Only when equality is increased within their ingroup, instead of between groups, do advantaged group members accurately perceive it as unharmful. Misperceptions persist when equality-enhancing policies offer broad benefits to society or when resources, and resource access, are unlimited. A longitudinal survey of the 2020 U.S. voters reveals that harm perceptions predict voting against actual equality-enhancing policies, more so than voters’ political and egalitarian beliefs. Finally two novel-groups experiments reveal that advantaged participants’ harm misperceptions predict voting for inequality-enhancing policies that financially hurt them and against equality-enhancing policies that financially benefit them. Misperceptions persist even after an intervention to improve decision-making. This misperception that equality is necessarily zero-sum may explain why inequality prevails even as it incurs societal costs that harm everyone. , Advantaged groups misperceive equality as harming their resource access, which predicts voting against equality policies.
Psychological Perspectives on Legitimacy and Legitimation
Legitimacy is a psychological property of an authority, institution, or social arrangement that leads those connected to it to believe that it is appropriate, proper, and just. Because of legitimacy, people feel that they ought to defer to decisions and rules, following them voluntarily out of obligation rather than out of fear of punishment or anticipation of reward. Being legitimate is important to the success of authorities, institutions, and institutional arrangements since it is difficult to exert influence over others based solely upon the possession and use of power. Being able to gain voluntary acquiescence from most people, most of the time, due to their sense of obligation increases effectiveness during periods of scarcity, crisis, and conflict. The concept of legitimacy has a long history within social thought and social psychology, and it has emerged as increasingly important within recent research on the dynamics of political, legal, and social systems.
A More Perfect Meritocracy
Two new books take aim at the moral failures of meritocracy. But we can advocate for a more just society without giving up on merit.
How the Cases You Choose Affect the Answers You Get: Selection Bias in Comparative Politics
This article demonstrates how the selection of cases for study on the basis of outcomes on the dependent variable biases conclusions. It first lays out the logic of explanation and shows how it is violated when only cases that have achieved the outcome of interest are studied. It then examines three well-known and highly regarded studies in the field of comparative politics, comparing the conclusions reached in the original work with a test of the arguments on cases selected without regard for their position on the dependent variable. In each instance, conclusions based on the uncorrelated sample differ from the original conclusions.
