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(Created page with "{{see also|Machine learning terms}} ===Automation Bias in Machine Learning== Automated bias in machine learning refers to the phenomenon where a model inherits and amplifies any biases present in its training data, leading to biased or discriminatory outcomes. Machine learning algorithms are programmed with the purpose of learning patterns and relationships in training data and making predictions based on this learned information; however, if that data contains biased el...")
 
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{{see also|Machine learning terms}}
===Automation Bias in Machine Learning==
Automated bias in machine learning refers to the phenomenon where a model inherits and amplifies any biases present in its training data, leading to biased or discriminatory outcomes. Machine learning algorithms are programmed with the purpose of learning patterns and relationships in training data and making predictions based on this learned information; however, if that data contains biased elements, these algorithms will also learn and replicate those prejudices in their predictions.
==Sources of Bias in Training Data==
Data training can contain several sources of bias. These include:
- Historical Bias: This refers to prejudices that have existed in society over time and are evident in the data. For instance, if training a model with data disproportionately representing one demographic group over another, the model may learn and replicate these prejudices in its predictions.
- Selection Bias: This occurs when the data used to train a model is not representative of all populations. For instance, if a model is trained on data from one geographic region, it may not generalize well to other areas with different demographics.
- Observer Bias: This refers to any prejudices researchers or annotators may have when collecting or labeling data. For instance, if they are biased towards certain races, genders, or cultural groups this prejudice will be reflected in the labeling of the data.
==Impact of Automation Bias in Machine Learning==
Automation bias in machine learning has the potential for far-reaching effects and can have grave repercussions, including:
- Discriminatory Outcomes: Machine learning models trained on biased data may produce predictions which discriminate against certain groups. For instance, a model trained with such biased information might suggest individuals from certain races or genders are less likely to receive loans or be hired for a job.
- Consolidating Existing Biases: Automation bias in machine learning can amplify existing prejudices and perpetuate discrimination. For instance, a model that predicts individuals from certain races or genders are less likely to receive loans may lead lenders to deny loans to these individuals, further perpetuating the cycle of discrimination.
- Lack of Trust: When machine learning models are perceived to be biased or discriminatory, it may lead to a lack of faith in the technology and its capacity for making impartial decisions.
==Mitigating Automation Bias in Machine Learning==
There are several steps that can be taken to minimize automation bias in machine learning, including:
- Diversifying Training Data: One of the most successful methods for mitigating automation bias is using a diverse and representative set of training data. This ensures that the model receives exposure to different experiences and viewpoints, decreasing its likelihood of reproducing existing prejudices.
- Regular Monitoring and Evaluating Machine Learning Models: It is essential to regularly assess and monitor the performance of machine learning models in order to detect and address any biases present. This can be done by evaluating its performance on diverse data sets using metrics such as demographic parity or equal opportunity to assess fairness.
- Utilizing Bias Correction Techniques: There are various techniques that can be employed to correct for bias in machine learning models, such as re-sampling, weighting and adversarial training. These approaches help reduce the effects of biases present in training data and enhance fairness of predictions made by the model.
==Explain Like I'm 5 (ELI5)==
Automation bias in machine learning refers to when a computer program (known as a machine learning model) learns to be unfair or biased because it was trained on data that was unfair or biased. This can have negative repercussions for the model itself and any future predictions it makes.
==Explain Like I'm 5 (ELI5)==
Say you have a toy box and every time you want to select one for playtime, you ask your big brother for help. He always selects the same toy because it's his favorite even though you enjoy playing with other toys as well.
Similar to teaching a computer to make decisions, we must provide them with plenty of examples. Showing just one type of toy will cause it to pick that toy over others even if there are others available - this is known as automation bias.
It's essential that the computer has a wide array of examples from which to learn, in order for it to make fair judgments and not simply repeat the same thing over and over.
[[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]]
{{see also|Machine learning terms}}
{{see also|Machine learning terms}}
==Introduction==
==Introduction==
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Factors such as:
Factors such as:


===Perceived accuracy and reliability==
===Perceived accuracy and reliability===
The perceived accuracy and dependability of a machine learning algorithm can have an important effect on how much individuals rely on its results. With higher accuracy rates, individuals are more likely to trust the results and may have less reason to doubt them.
The perceived accuracy and dependability of a machine learning algorithm can have an important effect on how much individuals rely on its results. With higher accuracy rates, individuals are more likely to trust the results and may have less reason to doubt them.


===Overreliance on technology==
===Overreliance on technology===
In some cases, people may become too trusting of technology to the point that they put more faith in it than their own judgment. This overdependence can lead to a false sense of security if the technology's output proves inaccurate or unreliable.
In some cases, people may become too trusting of technology to the point that they put more faith in it than their own judgment. This overdependence can lead to a false sense of security if the technology's output proves inaccurate or unreliable.


===Lack of understanding of the technology==
===Lack of understanding of the technology===
Individuals without a comprehensive understanding of how a machine learning algorithm operates may be more likely to trust its output without questioning it. As a result, they may fail to detect potential errors or biases in the results.
Individuals without a comprehensive understanding of how a machine learning algorithm operates may be more likely to trust its output without questioning it. As a result, they may fail to detect potential errors or biases in the results.


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To combat automation bias in machine learning, several strategies can be employed. These include:
To combat automation bias in machine learning, several strategies can be employed. These include:


===Providing training and education==
===Providing training and education===
By providing individuals with training and education on the workings of a machine learning algorithm, they can better comprehend its limitations and potential biases. Doing this enables them to detect errors or biases in the output generated by the algorithm and make more informed decisions.
By providing individuals with training and education on the workings of a machine learning algorithm, they can better comprehend its limitations and potential biases. Doing this enables them to detect errors or biases in the output generated by the algorithm and make more informed decisions.


===Encouraging critical thinking==
===Encouraging critical thinking===
Encouraging individuals to examine the output of a machine learning algorithm with critical eyes can help them detect potential errors or biases in its results. Doing this helps them make more informed decisions and avoid the detrimental consequences of automation bias.
Encouraging individuals to examine the output of a machine learning algorithm with critical eyes can help them detect potential errors or biases in its results. Doing this helps them make more informed decisions and avoid the detrimental consequences of automation bias.


===Combining machine learning with human judgment==
===Combining machine learning with human judgment===
In certain instances, combining the output of a machine learning algorithm with human judgment can help mitigate automation bias. Doing this helps guarantee that all output is thoroughly reviewed and any potential errors or biases identified.
In certain instances, combining the output of a machine learning algorithm with human judgment can help mitigate automation bias. Doing this helps guarantee that all output is thoroughly reviewed and any potential errors or biases identified.


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