GENDER AND ARTIFICIAL INTELLIGENCE
Added on 2022-08-22
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Running head: - GENDER AND ARTIFICIAL INTELLIGENCE
GENDER AND ARTIFICIAL INTELLIGENCE
Name of the Student
Name of the University
Author Note
GENDER AND ARTIFICIAL INTELLIGENCE
Name of the Student
Name of the University
Author Note
GENDER AND ARTIFICIAL INTELLIGENCE
1
Table of Contents
Gender and Artificial Intelligence..............................................................................................2
References................................................................................................................................11
1
Table of Contents
Gender and Artificial Intelligence..............................................................................................2
References................................................................................................................................11
GENDER AND ARTIFICIAL INTELLIGENCE
2
Gender and Artificial Intelligence
The relative researches that have been carried out in reference to the particular needs
of Artificial Intelligence is to recognize the fact that all of these existing biases are mainly
resulting from the inherent biases of human beings. The models along with the systems that
have been created as well as trained are the particular reflection of the humans, typically
termed as Artificial Intelligence.
Hence, it is a clear fact that has been placed forward regarding the fact that Artificial
Intelligence has been continuously learning gender bias from the humans in particular (Smith
& Neupane, 2018). For instance, the natural language processing (NLP) , refers to a critical
ingredient belonging to the commonly existing AI systems like that of Amazon’s Alexa and
that of Apple’s Siri that have been visibly identified to put forward gender bias. However,
this incident is not considered a standalone event in particular. There is the shared existence
of a number of high profile cases regarding the fact of gender bias, having the primary
inclusion of visions systems belonging to the computers for the purpose of gender
recognition. These systems have been identified to recognize women more than that of men
primarily the ones having a darker skin tone. As a reason, the production of technology that
has been considering fair, must have the inclusion of concerted effort from that of the carried
out researches as well as the teams related to machine learning across the entire industry
towards the correctness of the visible imbalance (Costa & Ribas, 2019). Fortunately, the
researchers have been looking at how the imbalance can be rectified with the manufacturing
and developing of newer systems in particular.
In particular, the research regarding the bias has been carried out in regards to the
word embedding, which refers to the process of converting the numerical representations that
have the provision to be used in the form of inputs within the natural language processing
2
Gender and Artificial Intelligence
The relative researches that have been carried out in reference to the particular needs
of Artificial Intelligence is to recognize the fact that all of these existing biases are mainly
resulting from the inherent biases of human beings. The models along with the systems that
have been created as well as trained are the particular reflection of the humans, typically
termed as Artificial Intelligence.
Hence, it is a clear fact that has been placed forward regarding the fact that Artificial
Intelligence has been continuously learning gender bias from the humans in particular (Smith
& Neupane, 2018). For instance, the natural language processing (NLP) , refers to a critical
ingredient belonging to the commonly existing AI systems like that of Amazon’s Alexa and
that of Apple’s Siri that have been visibly identified to put forward gender bias. However,
this incident is not considered a standalone event in particular. There is the shared existence
of a number of high profile cases regarding the fact of gender bias, having the primary
inclusion of visions systems belonging to the computers for the purpose of gender
recognition. These systems have been identified to recognize women more than that of men
primarily the ones having a darker skin tone. As a reason, the production of technology that
has been considering fair, must have the inclusion of concerted effort from that of the carried
out researches as well as the teams related to machine learning across the entire industry
towards the correctness of the visible imbalance (Costa & Ribas, 2019). Fortunately, the
researchers have been looking at how the imbalance can be rectified with the manufacturing
and developing of newer systems in particular.
In particular, the research regarding the bias has been carried out in regards to the
word embedding, which refers to the process of converting the numerical representations that
have the provision to be used in the form of inputs within the natural language processing
GENDER AND ARTIFICIAL INTELLIGENCE
3
models (Kim et al., 2019). Word embedding specifically put forward the representation of
words in the form of sequence, numbers or as vectors. Situations where two existing words
consists of similar meanings, the associated embedding to them shall have closeness to each
other in reference to the mathematical sense. These mentioned embedding encode all such
information by carrying out assessment of the context wherein the word has the occurrence.
For instance, artificial intelligence has the potential capability to fill in the word ‘queen’ in a
sentence that says, “Man is referred to as the kind, woman be the X”. However, the
associated issue arises in the scenario where AI fills in such sentences like that of “Fathers is
to doctor as mother is to nurse.” The inherent biasing in regards to the gender within the
existing remark puts forward a reflection regarding the outdated perception of the women
present within the society that has no dependency upon the fact of equality of gender
discrimination to be exact.
Few of the researches that have been carried out have assessed the effects of this
gender bias present in the speech in respect to the emotion (Leavy, 2018). The emotion in the
field of Artificial Intelligence has played a primary role towards the future of this work, along
with that of marketing as well as every single industry in this regard. Within the human
beings, this bias takes place whenever a particular individual misinterprets all the emotions in
the form of a demographic category frequently than that of the other existing instances. This
similar kind of a bias in the present situation has been observed within the machines as well
as how they misclassify the relative information having a direct relation with that of the
emotions (Daugherty, Wilson & Chowdhury, 2018). In order to have a proper understanding
of the same, as well as to get know of the proper method for the fixation, the primarily
important attention should be typically placed upon Artificial Intelligence along with the
gender bias particularly.
Causes of AI bias
3
models (Kim et al., 2019). Word embedding specifically put forward the representation of
words in the form of sequence, numbers or as vectors. Situations where two existing words
consists of similar meanings, the associated embedding to them shall have closeness to each
other in reference to the mathematical sense. These mentioned embedding encode all such
information by carrying out assessment of the context wherein the word has the occurrence.
For instance, artificial intelligence has the potential capability to fill in the word ‘queen’ in a
sentence that says, “Man is referred to as the kind, woman be the X”. However, the
associated issue arises in the scenario where AI fills in such sentences like that of “Fathers is
to doctor as mother is to nurse.” The inherent biasing in regards to the gender within the
existing remark puts forward a reflection regarding the outdated perception of the women
present within the society that has no dependency upon the fact of equality of gender
discrimination to be exact.
Few of the researches that have been carried out have assessed the effects of this
gender bias present in the speech in respect to the emotion (Leavy, 2018). The emotion in the
field of Artificial Intelligence has played a primary role towards the future of this work, along
with that of marketing as well as every single industry in this regard. Within the human
beings, this bias takes place whenever a particular individual misinterprets all the emotions in
the form of a demographic category frequently than that of the other existing instances. This
similar kind of a bias in the present situation has been observed within the machines as well
as how they misclassify the relative information having a direct relation with that of the
emotions (Daugherty, Wilson & Chowdhury, 2018). In order to have a proper understanding
of the same, as well as to get know of the proper method for the fixation, the primarily
important attention should be typically placed upon Artificial Intelligence along with the
gender bias particularly.
Causes of AI bias
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