Artificial Intelligence : The Definitions Of Artificial Intelligence

Most police departments still rely on officers to verify that the suspect chosen by the face recognition software actually matches the camera footage. However, humans shockingly make an error in this process once in every two cases (Bedoya, Alvaro, et al 2016). In some instances, it is not only this human error that is leading to false convictions but rather the system itself. A study, co-authored by the FBI, noted that the facial recognition software is less accurate when identifying African Americans. Systems relying on mugshot databases have a disproportionate number of African Americans due to their high arrest rates in America. This creates “racial biased error rates” that perpetuate implicit and systemic racism in our society (Bedoya, Alvaro, et al 2016). This presents yet another reason regulations should be put in place solely to limit the use of this technology to cases where its use is essential to solving a crime.

In today’s society, announcing one’s aversion towards people different from them is the fastest way receive ridicule. In general, society has progressed to a point where being xenophobic, racist, and sexist is socially unacceptable. Despite this progress, society still suffers from the effects of racism, xenophobia, and sexism. Why is that? The answer has to do with the fact that while explicit expressions of these behaviors are seen as unacceptable, implicit associations still permeate our society. These associations lead to implicit bias. Implicit bias is readily becoming the most pervasive form of bias in our society. It contributes to both micro and macro-level inequalities, and raising awareness of implicit bias is crucial in order to suppress its effects on society. This paper will start with a discussion of what implicit bias is. This will be followed by a discussion of the effects of implicit bias. It will conclude with a discussion of how implicit bias can be combatted.

Facial recognition is a technological product of the digital age that is continuing to evolve. The efficiency of the technology is an asset to crime detectives apprehending criminals, or the government tracking suspected terrorists for the safety of the country. It can also be used on a corporate level to allow a business to understand their ideal customer, or to increase the intractability of their website. While these abilities are positives to the people using them, the data collection may be harming more than it is helping. Considering the benefits of facial recognition, it is important for people to see what they are giving up for a more efficient experience. Facial recognition technology forces users to relinquish control of their own

In the present contemporary era, facial recognition technologies are being installed by the companies in an extensive sense that surely reflects a continuum of growing hi-tech superiority and complexity. At the most ordinary level, facial detection is done by this technology which means that a photo is just detected and located for a face ("Facing Facts: Best Practices for Common Uses of Facial Recognition Technologies," 2012).

The interesting thing about bias is how and why it develops. Often it is rooted in fear and even denial. People are easily intimidated by things that are different, and they are motivated by acceptance. So, sometimes we pretend to be something we are not in order to fit in. The irony in this is that bias may develop against things that are actually a part of our own makeup. If we are honest, it becomes evident that

In effect, people are unknowingly being put in suspect lineups without their awareness or consent. This can lead to false accusations against innocent citizens. Most police departments still rely on officers to verify that the suspect chosen by the face recognition software actually matches the camera footage. However, humans shockingly make an error in this process once in every two cases (Bedoya, Alvaro, et al 2016). In some instances, it is not only this human error that is leading to their conviction but rather the system itself. A study, co-authored by the FBI, said that the facial recognition software is actually less accurate when it comes to identifying African Americans. Systems relying on mugshot databases have a disproportionate number of African Americans due to their high arrest rates in America. This creates “racial biased error rates,” that perpetuate implicit and systemic racism in our society (Bedoya, Alvaro, et al 2016). But for American citizens in general, in any crime being solved by using face databases, anyone is a potential suspect. Because of this, regulations should be put in place to limit the use of this technology to cases where its use is relevant and vital to solving the issue.

There are many approaches and algorithms involved in machine learning. Some such algorithms are decision tree learning, association rule learning, reinforcement learning and rule-based machine learning. All these algorithms sound similar to what the names suggest in which decision trees are made to make decisions and learn, association rule learning is from discovering relations between variables, reinforcement learning is when it learns what to do through reinforcement, and rule- based learning is where rules are stored about how to use knowledge. While these all can mirror to some extent variations of human learning and decision making, not every aspect that occurs in a human brain is in the algorithms that exist such as the higher-level processes that occur within decision making and learning so therefore it is difficult to mirror all human aspects of learning and behavior in AI. Due to these differences in learning, prejudice in AI is different than the usual mechanism seen in people. While AI is able to learn stereotypes, and pick up on language bias like humans do through types of

Bias is defined as “prejudice in favor of or against one thing, person, or group compared with another, usually in a way considered to be unfair”. Each and everyone of us have a bias perspective towards one thing or another whether we are brave enough to admit or not. A person becomes bias through personal experiences or things they have read or heard. My brother and I used to play mario kart together and he would refuse to play unless he had the character “Wario” because he thought Wario was the only good character. This was an example of him being biased against the other characters in the game.

This bias is the most misunderstood social problem in America today. Since studies are showing that people are less racist than they were a few decades ago, now this bias is now harder to recognize meaning harder to treat. Most people possess this bias, proven by the most famous test an IAT test conducted by a group of Yale social scientists the test comprehends hidden bias states that 88% of people indicate a bias towards other people. Since the IAT shows a high number of bias seen in people the problems become pervasive. This means that we

There are so many instances of unconscious bias in daily life. It has become absolutely necessary to tackle the problem before it crosses over from the classroom to the workplace. Signs of intolerance are increasing day by day. Many are due to prejudices developed over a period of time, and the only solution remains to tackle the problem head-on with professional help who could teach people about emotional intelligence and how to recognize and work upon emotional states leading to

6. Implicit bias is the attitudes or stereotypes that affect our understanding, actions, and decisions in an unconscious manner. It is our honest opinion about something, even if we didn’t know that was our opinion.

Racial Biases have specifically come to the forefront in recent times for a multitude of reasons. As research has shown, most racial biases are often not the result of overt racism, more Implicit biases. Implicit Bias describes the automatic association people make between groups of people and stereotypes about those groups (Keesee, 2015).

Bias is the conviction that with people from no less than one race are below average contrasted with people from various races. Generally, this perspective furthermore incorporates the conviction that one 's own race is superior to anything distinctive races. People are

Selection Bias occurs when researchers choose nonequivalent for comparison. Taking example the boot camp program which was developed to put offenders into group to try give them the need rehab in not repeat criminal offenses again. This problem has researched both groups, which were prisoners and the other group going straight into the boot camp program to prevent prison time (Hagan, 2010). Researcher’s studies the actions of inmates in the boot program study and can to the conclusion that some offenders that graduated from the program formed better attitudes when entering back into society. Like the boot camp program, select bias can have a negative impact on the ability to any group that is studied. This mean that each time this program is used, there are always going to be some bias results, because the offenders have a less than positive impact throughout society (Hagan, 2010). As we all know, society looks down on criminals and even those who have paid for their crimes and just want to re-enter society and live a normal

There are different types of artificial intelligence that many institutes and companies believe in. However, they all share the same belief of a self-aware, conscious, human-like, computer system, the type of AI people fear. Disregarding that an AI is a computer much like a phone or laptop, one must consider that if something is sentient like a person or animal, should they be treated like one? And if not, is it discrimination or racism when it is treated like an item? Slavery treated people based on race or social status differently, the bottom being slaves. Some might argue that machines don’t have a “soul” or aren’t alive. There are hundreds of questions and ideas that have no clear