Race differences in crime are covered up 1. Recruitment requires biased affirmative action. Finance suffers from affirmative loans. Insincerity is at the root. Computers caught PC with its pants down.
UK government probes algorithm bias in crime, recruitment, and finance
The UK government is launching an inquiry into whether algorithms used in areas such as criminal justice, recruitment, and finance are biased against people based on gender or race.
Is it not preposterous to think computers are biased? Maybe computers stumble upon the truth, because they have not been programmed and brainwashed like our media and population. Maybe computer algorithms are NOT biased, but our society has egalitarian bias.
The truth about Sex differences and race differences are societies’ taboo.
- Equality dogma: all races are equal
- Code of Silence:
Conspiracies to hide the truth about racial differences, black and immigrant crime, etc. The Silence Must be Broken. This systematic coverup is the foundation of leftist brain washing, the foundation of anti-racist policies, the reason why mis-led citizens vote for leftist pro-immigration, pro-affirmative action left-racist policies and parties
The Department for Culture, Media, and Sport (DCMS) announced the new inquiry on Wednesday. It will be led by the Centre for Data Ethics and Innovation, an independent watchdog set up by the department in 2018 to come up with best practice policy on how to police artificial intelligence.
Sincere honest scientists parsimoniously assume: “ computers are unbiased”. But #TheTruthIsRacist! Politically correct programmers must dishonestly fiddle the input data with an anti-racist pre-processor to deceive the AI program into getting the desired results.
The Cabinet Office’s Race Disparity Unit, which highlights discrimination in public services, will also work on the new review.
[…] Tech giants such as Amazon have already run into issues of algorithmic bias in recruitment and Google established a team last year to review the ethics of its AI. […]
Highlighting examples of potential pitfalls, DCMS said algorithms used to scan CVs and shortlist candidates could discriminate against certain groups of people due to the unconscious, or conscious, bias of programmers who wrote the code.
LOL! Maybe the problem is that the computer algorithms are NOT biased, and bias has to be built in. So computers get fed the same biased media information we get fed.
It also warned that the growing use of algorithms in finance could lead to a lack of transparency when applied to loans.
Transparency is not desired. People should not know about affirmative housing loans2
Amazon ran into trouble with an AI-powered recruiting tool that discriminated against women. The algorithm was trained using thousands of CVs from past hires, but the male dominance of the tech industry meant that the algorithm came to favour men over women, reinforcing the gender imbalance.
Could we use the scientific method to test the alternative hypothesis, that it is logically correct to favor men over women in the tech industry? Larry Summers3 was punished for breaking that taboo. We are not allowed to know that imbalance is being reduced by unjust quota systems?
Google announced a set of AI principles last year to help guide the ethical development of the technology and introduced a “formal review structure” to oversea AI development across the company. Demis Hassabis, the cofounder of AI division Google DeepMind, is already an advisor to the UK government on AI ethics through the Office for Artificial Intelligence.
Sincere honest scientists parsimoniously assume: “ computers are unbiased”.
But #TheTruthIsRacist! Politically correct programmers must dishonestly fiddle the input data with an
anti-racist pre-processor to deceive the AI program into getting the desired
“I’m pleased [the Centre for Data Ethics and Innovation]’s team of experts is undertaking an investigation into the potential for bias in algorithmic decision-making in areas including crime, justice, and financial services,” Wright said.
Honest scientists would also look into the alternative hypothesis that computers are logically correct and discover our society’s bias, as demonstrated in Sincerity.net
[…] “We want to work with organisations so they can maximise the benefits of data driven technology and use it to ensure the decisions they make are fair. As a first step we will be exploring the potential for bias in key sectors where the decisions made by algorithms can have a big impact on people’s lives.”
[continue at UK government probes algorithm bias in crime, recruitment, and finance]
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Subprime crisis’ cause: mandatory affirmative housing loans
Racism could have saved both the world economy from subprime meltdown, and saved Blacks from bankruptcy. Anti-Racist affirmative housing rules forced banks to give mortgage loans to unqualified minorities. Blacks did not need to fulfill the same tried-and-proven loan prerequisites as whites.
Summers Vindicated (again) – Marginal REVOLUTION
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Sex Differences in Mathematical Aptitude