AI News, Artificial Intelligence Can Be Biased. Here's What You Should Know ... artificial intelligence
An Intro to AI for people that hate math and can’t code
This is a high-level crash course on AI for managers, business owners, and other non-technical roles that want to get a grasp on Artificial Intelligence to start using it to drive value in their organizations.
AI can out-play humans in complex board and video games, make better investment decisions (sometimes), and can even beat lawyers in assessing the quality of legal documents.
Artificial Intelligence is incredibly complex, and breakthroughs in the space are driven by teams with deep experience in computer science, mathematics, and neuroscience- and lots of funding.
It will take time to tune it, learn how to act on the program outputs, and properly structure your data, but you don’t need to go back to college for this.
In English, the machine “learns” by thinking, “There was a white blob in that photo with a pink blob in the middle of its face (the cat’s nose).
The program will use those features to determine what a cat looks like, and pretty accurately decide if a photo contains a cat or not.
When you create a machine learning program, you set up a neural network which is supposed to roughly mimic the structure of firing neurons in the human brain.
The inputs are your data points, the hidden layer is where calculations are made, and the outputs are the end results: this could be classification, recommendations, or probabilities, based on how it is programmed.
But by adding more hidden layers (the ones between input and output), the network becomes “deep”, more complex, and ideally more accurate.
Building Ethical AI for Talent Management
Artificial intelligence has disrupted every area of our lives — from the curated shopping experiences we’ve come to expect from companies like Amazon and Alibaba to the personalized recommendations that channels like YouTube and Netflix use to market their latest content.
This group of individuals is referred to as a “training data set” and often includes managers or staff who have been defined as “high performers.” AI systems process and compare the profiles of various job applicants to the “model” employee it has created based on the training set.
If the training set is diverse, if demographically unbiased data is used to measure the people in it, and if the algorithms are also debiased, this technique can actually mitigate human prejudice and expand diversity and socioeconomic inclusion better than humans ever could.
Recruiters spend just afew secondslooking at a resume before deciding who to “weed out.” Hiring managers make quick judgments and call them “intuition” or overlook hard data and hire based oncultural fit— a problem made worse by the general absence of objective and rigorousperformance measures.
The outcry of criticism about theirbiased recruiting algorithmignored the overwhelming evidence that current human-driven hiring in most organizations isineradicably worse.It’s akin to expressing more concern overa very small number of driverless car deathsthan the 1.2 milliontraffic deathsa year caused by flawed and possibly also distracted or intoxicated humans.
1) Educate candidates and obtain their consent.Ask prospective employees to opt in or to provide their personal data to the company, knowing that it will be analyzed, stored, and used by AI systems for making HR-related decisions.Be ready to explain thewhat,who,how, andwhy.
In short, AI systems should be designed to predict and explain “causation,” not just find “correlation.” You should also be sure to preserve candidate anonymity to protect personal data and comply withGDPR, Californiaprivacy laws, and similar regulations.
For example, much academic research indicates that while cognitive ability tests are aconsistent predictorof job performance, particularly inhigh-complexity jobs, their deployment hasadverse impacton underrepresented groups, particularly individuals with a lower socioeconomic status.
4) Follow the same laws — as well as data collection and usage practices — used in traditional hiring.Any data that shouldn’t be collected or included in a traditional hiring process forlegal or ethical reasonsshould not be used by AI systems.
If organizations address these issues, we believe that ethical AI could vastly improve organizations by not only reducing bias in hiring but also byenhancing meritocracyand making the association between talent, effort, and employee success far greater than it has been in the past.
To make the above happen, however, businesses need to make the right investments, not just in cutting-edge AI technologies, but also (and especially) in human expertise — people who understand how to leverage the advantages that these new technologies offer while minimizing potential risks and drawbacks.
How China’s Government Is Using AI on Its Uighur Muslim Population
It’s been estimated that China’s government has detained as many as a million members of the country’s Muslim population in so-called “re-education camps,” in part of a campaign that has alarmed human rights activists across the world.
“They’re in a training school set up by the government to undergo collective systematic training, study and instruction,” the talking points read, adding, “You have nothing to worry about.” The Chinese government’s campaign against those it says have been exposed to extremism is centered on an autonomous region, Xinjiang, where nearly half of the 25 million residents are a Muslim people called the Uighurs.
“The kinds of behavior that’s now being monitored — you know, which language do you speak at home, whether you’re talking to your relatives in other countries, how often you pray — that information is now being Hoovered up and used to decide whether people should be subjected to political reeducation in these camps,” Sophie Richardson, China Director for Human Rights Watch, tells FRONTLINE in the below excerpt from the documentary:
- On 4. december 2020
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