How can biased AI hurt users or increase business profits?

I'm not sure why people are so worried about AI that goes beyond the collective intelligence of humanity.We can't even get the systems we have today to stop mimicking our ignorant tendencies. Or more, perhaps we humans have to separate ourselves from the same prejudice before they can expect it to be eliminated from our algorithms.In the Citizen Handbook for Artificial Intelligence, John Zerilli leads a renowned team of researchers and authors in AI and machine learning to present readers with a holistic review that is accessible to both the history and current state of Art,

which may arise from ดาวน์โหลด สล็อต xo And how will the challenges facing this constantly evolving and rapidly advancing AI technology influence society for decades to come?Human prejudice is a combination of hardwired and learned prejudice, some of which make sense (eg, "You should wash your hands before eating") and others that are clearly false (eg "unethical atheists"). At the same time, artificial intelligence suffers from both self and learned prejudices. But the mechanisms that create prejudice in AI are different from the evolution that gives rise to the analysis of psychological behavior and human rational bias.

A group of mechanisms arise from deciding how to solve practical problems in AI.These decisions often incorporate programmers' sometimes biased expectations about how the world works. Let's say you have been tasked with designing a machine learning system for landlords looking to find good tenants. It is an ideal question to ask. But where should you find information to answer questions? There are many variables you may choose to implement in your system training, age, income, gender, zip code, current attendance in high school, character solubility, alcohol consumption? Omit the variables that are often misreported

(Eg, consumption of alcoholic beverages) or is prohibited by law as a reason for discrimination. (Eg gender or age). The choices you make may depend on your own belief, at least to some extent, whether something influences tenants' behavior. Algorithm, especially if the developer ignores the predictable variables that are good tenants and adversely affect the individuals who will make good tenants. But it will not be identified as that.The same problem reappears when decisions have been made about how data is collected and labeled. People using the algorithm will not see these decisions. Some of the information is considered commercially sensitive.

Some will be forgotten Failure to document the source of bias can be a problem, especially when one purpose-designed AI is chosen to join the other's services, as well as when credit scores are used to assess fitness. Of the person as an employee The inherent dangers of shifting AI from one context to another have recently been dubbed the "carry trap" because it has the potential to diminish both accuracy and reliability. Theology of algorithms that are reused Consider a system like TurnItIn to be one of the anti-counterfeiting systems used by universities. The authors say it has surveyed 9.5 billion pages (including common research resources such as online course notes and citations like Wikipedia) .

It also maintains a database of previously submitted articles. Through TurnItIn, which according to the company's marketing materials, it is growing more than fifty thousand articles per day. The essays that the students submit are then compared with this information to detect plagiarism. Of course, there will always be some similarities if student work is compared to many of the other student essays written on common academic topics. To address this issue, manufacturers chose to compare the relatively long string of characters. Lucas Introna, Lancaster University professor of technology and ethics organization, claimed TurnItIn was biased.
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