Napoleon said “what is history but a fable agreed upon?” So is social justice. More famously Churchill said “history is written by the victors.” Victorious social justice does history’s victors one better: it writes the present. Now it wants to write the future.
The Narrative proceeds as continuous real-time fable-making and up-to-the-minute historical revision. The progressive left casts the future as a utopia always receding on the horizon–it works like the promised return of a messiah. Nobody really wants it or knows what it means, but their faith in it is their bedrock, until it becomes no longer tenable. The utopia is now a problem for the powerful who rule by the mandate of “social justice”–like the messiah it isn’t coming and if it did it would mean the end of the game and a surrender of power.
But, thankfully, progressive utopia is laughable as a possibility and no one really wants it anyway. Not the various ethnic groups coalescing around “social justice”, certainly. They all have their respective visions of it–with them on top, or near as can be, and whitey on the bottom. It would be comic if it didn’t involve our demise.
No, the left now fears only the future. The present is in the bag, the past has been ritually killed, but the future looms outside control.
A bogus research non-profit calling itself AI Now is worried about AI:
There is a diversity crisis in the AI sector across gender and race. Recent studies found only 18% of authors at leading AI conferences are women, and more than 80% of AI professors are men. This disparity is extreme in the AI industry: women comprise only 15% of AI research staff at Facebook and 10% at Google. There is no public data on trans workers or other gender minorities. For black workers, the picture is even worse. For example, only 2.5% of Google’s workforce is black, while Facebook and Microsoft are each at 4%. Given decades of concern and investment to redress this imbalance, the current state of the field is alarming.
One of the paper’s recommendations of course is more data “transparency” and progress reports. If activists have their way, of course, these become federal requirements. Boldface added: social justice is blind. Those “decades of concern and investment” aren’t the industry’s failure but social justice’s failure–if anyone was paying attention, this argument would be a laughable self-own.
The AI sector needs a profound shift in how it addresses the current diversity crisis. The AI industry needs to acknowledge the gravity of its diversity problem, and admit that existing methods have failed to contend with the uneven distribution of power, and the means by which AI can reinforce such inequality. Further, many researchers have shown that bias in AI systems reflects historical patterns of discrimination. These are two manifestations of the same problem, and they must be addressed together.
The paper conflates two goals, diversifying the ranks of AI tech and, more importantly I suspect, beginning the groundwork for taking control of its content and direction; among its recommendations is AI that looks too threatening to dogma should be forbidden from the start.
The overwhelming focus on ‘women in tech’ is too narrow and likely to privilege white women over others. We need to acknowledge how the intersections of race, gender, and other identities and attributes shape people’s experiences with AI. The vast majority of AI studies assume gender is binary, and commonly assign people as ‘male’ or ‘female’ based on physical appearance and stereotypical assumptions, erasing all other forms of gender identity.
The left simply couldn’t ease up on the transsexualism front if it wanted to, so their momentum and trajectory has them heading for direct conflict with basic science. Algorithms using what would have been non-controversial assumptions before–definitions of “man” and “woman”, say–are problems now–problems of the left’s own making in its trans enthusiasm.
Fixing the ‘pipeline’ won’t fix AI’s diversity problems. Despite many decades of ‘pipeline studies’ that assess the flow of diverse job candidates from school to industry, there has been no substantial progress in diversity in the AI industry. The focus on the pipeline has not addressed deeper issues with workplace cultures, power asymmetries, harassment, exclusionary hiring practices, unfair compensation, and tokenization that are causing people to leave or avoid working in the AI sector altogether.
In one breath he social justice industry bemoans a lack of minority achievement in higher education and in the next in condemns industry for its lack of diversity–as if the latter wouldn’t necessarily follow from the former. A compliant press allows them to get away with this and a lot worse, but the fact there was something called “pipeline studies” suggests someone over there understood the problem wasn’t hiring bias but minority ability–and they appear to have failed miserably. The paragraph above is a declaration there will be no more acknowledgement of this massive contradiction, not that there was.
The use of AI systems for the classification, detection, and prediction of race and gender is in urgent need of re-evaluation. The histories of ‘race science’ are a grim reminder that race and gender classification based on appearance is scientifically flawed and easily abused. Systems that use physical appearance as a proxy for character or interior states are deeply suspect, including AI tools that claim to detect sexuality from headshots, predict ‘criminality’ based on facial features, or assess worker competence via ‘micro-expressions.’ Such systems are replicating patterns of racial and gender bias in ways that can deepen and justify historical inequality. The commercial deployment of these tools is cause for deep concern.
The future looks bright–like a mushroom cloud on the horizon.