The long-run effects of affirmative action bans

Why Meritocracy Doesn't Work: The Math Behind the Myth

Even a 1% hiring bias compounds dramatically across hierarchies and growth. Here's the math proving why meritocracy fails and diversity initiatives matter.

Why Diversity Programs Fail

After Wall Street firms repeatedly had to shell out millions to settle discrimination lawsuits, businesses started to get serious about their efforts to increase diversity. But unfortunately, they don’t seem to be getting results: Women and minorities have not gained much ground in management over the past 20 years. The problem is, organizations are trying to reduce bias with the same kinds of programs they’ve been using since the 1960s. And the usual tools—diversity training, hiring tests, performance ratings, grievance systems—tend to make things worse, not better. The authors’ analysis of data from 829 firms over three decades shows that these tools actually decrease the proportion of women and minorities in management. They’re designed to preempt lawsuits by policing managers’ decisions and actions. But as lab studies show, this kind of force-feeding can activate bias and encourage rebellion. However, in their analysis the authors uncovered numerous diversity tactics that do move the needle, such as recruiting initiatives, mentoring programs, and diversity task forces. They engage managers in solving the problem, increase contact with women and minority workers, and promote social accountability. In this article, the authors dig into the data, executive interviews, and several examples to shed light on what doesn’t work and what does.

Choosing​ ​a​ ​Sex​ ​Worker-Affirmative​ ​Therapist

Due to the stigma of sex work, sex workers can be vulnerable to micro-aggressions and minority stress and experience discrimination when the

AI Could Set Back Women at Work. Here’s the Risk.

JP Morgan and Brookings research shows women face higher risks from AI due to lower representation, slower adoption, and job exposure.

A Growing Gender Divide in the AI Economy — The Honest Economist

The article argues that AI is reshaping gender inequality even before the pay gap visibly widens. Women are more concentrated in clerical and administrative roles where AI can restructure work, while men are more concentrated in technical, AI-complementary roles. Among college graduates, the exposur

The myth of meritocracy: who really gets what they deserve?

The long read: Sorting people by ‘merit’ will do nothing to fix inequality

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...

Will Generative AI Disproportionately Affect the Jobs of Women?

Generative AI such as ChatGPT holds the potential to alter many kinds of work, but analysis of a new report shows the occupations most likely to be affected are populated by more women than men.

A Proposed Conceptual Framework for Understanding Gender Disparities in Artificial Intelligence Adoption

This conceptual paper investigates the issue of gender disparities in artificial intelligence adoption within organizational settings. It highlights the importance of addressing these differences for workplace inclusivity and performance. The problem lies in the unequal of artificial intelligence adoption between male and female employees’ perspectives. The proposed framework is developed through a review of existing literature highlights differences between male and female employees in their artificial intelligence adoption. The methodology is based on conceptual analysis, derive on previous studies to build a comprehensive understanding of gender disparities in artificial intelligence adoption. An Independent T-Test will be used to analyze the disparities between male and female Malaysian sports graduates in testing the hypothesis development. By addressing these disparities, the paper provides insights into creating inclusive strategies that promote gender equity in the workforce. Overall, the findings aim to guide both researchers and practitioners in designing gender equality policies that address challenges to equal adoption of artificial intelligence.

Women at Risk: Economic Inequalities in the Age of AI - TWF

As we come to the end of 2024, we are humbled by the impact we have been able to make together to improve the lives of […]

Algorithmic Bias in Lending: Evidence from a Fintech Audit

Algorithmic lending has transformed the consumer credit landscape, with machine learning models commonly facilitating underwriting decisions. To comply with fair lending laws, these algorithms exclude legally protected characteristics, such as race and gender. Yet algorithmic underwriting can still inadvertently favor certain groups, prompting concerns about whether lending algorithms exhibit discriminatory behavior. Using proprietary loan-level data from a major U.S. fintech platform, we audit lending decisions across approximately 80,000 personal loans. We find that loans made to men and Black borrowers yielded lower profits than loans to other groups, suggesting that men and Black borrowers benefited from relatively favorable pricing. We trace these disparities to miscalibration in the platform's underwriting model, which overestimates risk for women and underestimates risk for Black borrowers. We then show that one could correct this miscalibration -- and the corresponding disparities -- by including race and gender in underwriting models, illustrating a tension between competing notions of fairness.

Paid Program: Why More Women Are Needed in AI

When the world of AI isn’t inclusive, it creates biases in what the data shows and how it’s used.

AI Adoption and Inequality

There are competing narratives about artificial intelligence’s impact on inequality. Some argue AI will exacerbate economic disparities, while others suggest it could reduce inequality by primarily disrupting high-income jobs. Using household microdata and a calibrated task-based model, we show these narratives reflect different channels through which AI affects the economy. Unlike previous waves of automation that increased both wage and wealth inequality, AI could reduce wage inequality through the displacement of high-income workers. However, two factors may counter this effect: these workers’ tasks appear highly complementary with AI, potentially increasing their productivity, and they are better positioned to benefit from higher capital returns. When firms can choose how much AI to adopt, the wealth inequality effect is particularly pronounced, as the potential cost savings from automating high-wage tasks drive significantly higher adoption rates. Models that ignore this adoption decision risk understating the trade-off policymakers face between inequality and efficiency.

How AI Is Fueling the Gender Pay Gap in Tech

A new Wharton study finds that too few women are working with emerging tech, and that exclusion is driving a growing divide in pay.

The idea that oppressions are subconscious scares me - Lemmy

In order to understand oppression as a subconscious thing, let’s take a couple of examples. Many women complain that they express an idea that is immediately dismissed, only for a man to be cheered for saying the exact same thing, minutes later. I have witnessed time and again a woman pointing to the correct explanation or solution for a bug and a man just ignoring her repeatedly, only to exhaust all other solutions before arriving to the same conclusion and solution. There is a ton of lab research to show this goes beyond anecdotal evidence. Some researchers recently showed that for the same length of speech, women speakers were judged as being overtaking the dialog. In fact, the balance that the average listener gauged as fair was 70% male speech vs 30% female speech. Don’t get me started about bias of doctors, not only against women and transgender people, but, let’s face the elephant in the room, black people. A recent study had found that doctors hold outdated racist tropes, for instance, that black people are more resistant to pain. Doctors are also more likely to discount complains from any marginalized group, and send them home with a “have less stress” tap on the back, when in reality they do have something serious. The list goes on and on. There is a whole book, called “Dying of Whiteness”, which shows that white people are opposed to welfare measures, when they think that black people will benefit from welfare too. The number of leftist and anarchist people who maintain some blatant form of medicalized cisgenderism is astonishing. And this is not about transphobia only. The track record of many collectives with sexism is harrowing. Someone recently wrote “Why I am not giving to your mutual aid project”, TL;DR because every collective has a couple of skeletons of sexual abuse in their closet. Haven’t we all seen the silent machismo and the solidarity between male authority figures in these spaces? Whenever a story like this pops up, the whole repertoire of discount tropes for sexual abuse allegations is taken out from the secret place it is kept for emergencies like this. This is consequential for broader organization as well, not only gendered and racialized oppression. In a strict Bakunian sense, the informality of a circle of buddies pulling the strings on any topic of field of activity is a form of State. A kyriarchy, or oppression. And there are more oppressions that we don’t even know, in jobs, universities, hobbies, and more. Some of them can be traced back to sexism, like the hard-science superiority complex has sexist undertones, but wherever there is inequality, there is oppression. It seems that people have a predisposition to form groups and make rules that reify social categories, for instance when some universities are considered elite and others shitholes, or some squat is seen as true hardcore anarchists and the others as alternative lifestyle hippies. ALL of this operates subconsciously. For quite some time I thought that the channels of anarchist organization would remedy human predisposition towards inequality, oppression, and bias. I now realize that this can not happen automatically, but a serious component of self-reflection to transcend internalized kyriarchies must be necessary. But we now see the prevail of the most vulgar and raw inequality instincts, enabled by systemic capitalist indoctrination and consent manufacturing campaigning. And we know that this seed is also present in leftists and anarchists. I believe that something horrible is about to happen the following five to ten years. We will then reflect, as we did in the aftermath of the two first world wars, on human predisposition to outgroup, other, dehumanize and eliminate. We might as well reinvent the science of the human psychology of obedience, conformity, prejudice, oppression and kyriarchy. And anarchists might have to reinvent the principles of political organization, this time to include the understanding of subconscious oppression, and embrace procedures to address and transcend all kyriarchies. That would stay truth to the spirit of complete abolition of all State, including internalized states of mind. (The pun is not intended.) In fact, I finally tend to agree with some anarchist pedagogists, that preparing people to be active members in an equal society, free of oppressions, the work must start in childhood, to eradicate the instincts of property, selfishness, power and supremacy. So, although it might seem there could not be a bleakest time to raise such issues, that in fact the struggle for visibility, for the normalization of queer lives, for the subversion of verbal sexism, and so on and so forth, all were important and integral to the anarchist cause. The white male racist kleprocratists might shed ours and many of our siblings’ blood over the next ten years. But 2030’s anarchism will re-discover the procedures that engender these demands and bake the relentless struggle for complete equality right into the channels of anarchist social organization and generational reproduction.