What Is AI Bias and How to Avoid It?
The digital landscape is changing at unprecedented rates with the rise of artificial intelligence. AI systems have become an integral aspect of the world of technology with a formidable impact on different sectors and jobs. One of the most crucial changes introduced by artificial intelligence is the way in which people interact with technology. However, different types of AI bias could lead to unpredictable consequences. As a matter of fact, many users have pointed out concerns about the adoption of AI due to the issues of bias.
Could AI bias stop the adoption of AI? Machine learning and AI are revolutionary technologies and could change the future of mankind with promising improvements. At the same time, it is also important to mitigate any potential harm emerging from the use of AI. Let us learn more about bias in AI systems and their variants, alongside the recommended methods for fighting against AI bias.
Definition of AI Bias
The first highlight in answers to “What is AI Bias?” would point towards the capability of AI systems for transforming the use of technology. Until a few years ago, artificial intelligence was just a topic you would come across in science-fiction novels or films. However, the futuristic concept has turned into reality and has transformed different aspects of our daily lives. In addition, AI systems have also emerged as crucial tools for introducing major transformations in different industries, such as healthcare, manufacturing, automotive, and retail industry.
The benefits of AI systems should not overshadow the inherent risks associated with artificial intelligence. You should understand that an AI bias guide could help you identify how bias could affect the working of artificial intelligence. AI systems work by replicating the human thought process, and AI systems could also bring human biases in decision-making. The impact of AI bias is visible in different use cases, such as facial recognition, risk assessment, and chatbot interactions. Bias in AI systems could have a negative impact on businesses, individuals, and society.
AI bias is also known as machine learning bias or algorithm bias. The bias in AI systems refers to a phenomenon in which the algorithm used in decision-making features systematic errors that are derived from preconceived stereotypes and notions. The review of AI bias examples would help you understand that AI bias is almost similar to human biases and leads to inaccuracy of decisions. AI biases emerge when AI algorithms draw narrow conclusions or use training datasets that do not offer an accurate representation of the concerned population.
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Impact of AI Bias
The next crucial factor you need to learn to prevent AI bias would be the impact of AI bias. You can notice the impact of AI bias in different ways, including subtle changes to large-scale errors or false assumptions. For instance, societal bias against women could lead to the development of AI systems that would favor male candidates over female candidates in making hiring decisions. On the other hand, cognitive bias against women with darker skin tones in the healthcare industry could lead to development of AI systems that would create diagnosis issues for women with darker skin tones.
As a matter of fact, certain studies have reported that AI facial analysis techniques could have a higher misidentification rate in the cases of people with dark skin tones. A study by the Stanford University Human-Centered AI Institute showed that Contrastive Language-Image Pre-training or CLIP model resulted in misclassification of people with dark skin tones.
It serves as one of the earliest examples of AI bias, which shows the impact of racial bias in AI-based facial recognition systems. It is important to understand that such types of bias could be problematic in the era of increasing diversity and representation. For example, racial bias could have a negative impact on people from minority groups.
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What are the Popular Types of AI Bias?
Bias in artificial intelligence could assume multiple forms and could be difficult to identify. You can find two broad categories of AI bias such as algorithmic bias and data bias, with unique implications. The first entry among the types of AI bias is algorithmic bias, which emerges when AI algorithms generate incorrect or unfair results due to the incorrect assumptions or underlying datasets used for training the algorithm.
Algorithmic bias could arise in situations when the data used for training the algorithm features biases. For example, if a training dataset emphasizes a specific demographic group, it would not consider other populations. As a result, the AI algorithm would generate results that could reflect the biases in the training dataset. Therefore, it would change the balance of fairness in the algorithm.
Another popular addition to an AI bias guide would point at data bias. It is a type of bias that emerges when training datasets for AI algorithms have biases or errors. Data bias could emerge from different sources, such as data cleaning process, data collection methods, and inherent biases associated with source data. For example, you can assume that a dataset for training AI systems to draw predictions for customer behavior could have an over-representation of specific ethnicities or genders. In the case of data bias, AI algorithms would lead to results that favor certain groups.
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How Does AI Bias Affect Different Industries?
Artificial Intelligence has become a popular technology trend and influences different aspects of the lives of people. Therefore, AI technology should be unbiased and fair. The answers to “What is AI bias?” point to the real-world implications of biased AI, including unfair treatment of groups or individuals or generating inaccurate decisions or predictions.
AI systems could be beneficial to society and introduce powerful changes. However, it is important to understand the pros and cons of AI for ensuring effective use of AI systems, especially with the different biases. Here is an outline of the different ways in which AI bias could affect different industries.
Organizations in the education sector utilize AI for making decisions about admissions of students in schools, colleges, and universities. However, AI bias examples in the education sector could show that biased decision-making could lead to unfair exclusion of students. For instance, an AI algorithm could use a training dataset with biases for a specific race or gender. In such cases, the AI system could turn the results of the admission process in favor of the concerned groups.
Have you ever wondered how you find the results on search engines? AI algorithms are responsible for determining the news articles that you would see on your screen. In such cases, AI algorithms could prioritize news articles that skew towards certain biases and result in biased news that does not represent the population.
The best recommendation to prevent AI bias would point to the example of AI’s impact on financial services. Artificial intelligence has become an important component in transforming financial services, such as helping firms make decisions about credit ratings and loan approvals.
When the algorithm used for decision-making in financial services has societal biases, it could lead to unwanted circumstances. For example, an AI algorithm for passing loan approvals could use a dataset featuring data from samples with white people. The AI algorithm could lead to unfair loan denials for people with darker skin tones.
The different types of AI bias could also influence law enforcement, primarily due to biases in facial recognition technology. Biased AI in facial analysis technology could result in false positives that could lead to wrongful accusations and arrests. It is a particularly complicated problem for people of darker skin tones who are commonly implicated in arrests and are a major percentage of the prison population.
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Popular Instances of AI Bias
The bias associated with AI systems has created multiple issues in the real world. It is important to learn about examples of AI bias and their implications for people and different industries. Here are some of the notable instances of the concerns emerging from AI bias.
Racism in American Healthcare Systems
AI technology could help in reducing health inequalities, especially at a time when a country like the US has been experiencing problems with systematic racism. AI systems that have been trained on datasets that do not represent the actual state of healthcare could perform negatively for underrepresented populations.
According to a 2019 study, researchers discovered that AI algorithms for predicting which patients need additional medical care could be influenced by bias. For example, the algorithms prioritized white patients over black patients. The AI algorithm emphasizes individual healthcare needs for determining healthcare expenses. However, the algorithm assumed that past healthcare expenditures could help in determining its effectiveness.
Hiring Algorithm of Amazon
Automation has been one of the key factors in the domain of ecommerce, and Amazon has leveraged automation for its own interests. Interestingly, the answers to “What is AI bias?” would draw attention to the experimental recruitment tool with AI functionalities. It was designed for assigning ratings to job applicants. However, the AI algorithm was biased towards women and called for changes in the algorithm.
The computer models of Amazon have been working on analyzing resumes for almost a decade and could identify similarities in the candidate applications. Most of the applications were from males and reflected the male dominance in the industry. The AI algorithm learned that all applications should be from males, and thus, it penalized resumes from female applicants. Amazon made the programs to become completely neutral.
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How Can You Resolve Biases in AI?
The necessity to prevent AI bias has become an important highlight in the AI ecosystem. If you want to make the best of AI systems, then you must familiarize yourself with the following best practices to resolve AI bias.
Don’t Depend Only on Real-World Data
Real-world data might appear as a trusted and credible source of information for training AI models. At the same time, it is also important to verify that real-world data could include unintentional societal and human biases. You should not rely completely on real-world data for training datasets of AI algorithms. On the other hand, you should utilize a blend of real-world and synthetic data. It would ensure that the training dataset is completely accurate and unbiased.
AI algorithms can be extremely complex, and it is difficult to identify biases without a comprehensive understanding of dataset and working mechanism of the algorithm. Any AI bias guide could help you understand that AI algorithms can be fair only if they are transparent. AI algorithms should provide a clear explanation of the decision-making process underlying their design. In addition, an organization should also offer an explanation of the mechanisms used for making decisions with responsible AI algorithms.
The best approach for preventing bias visible in real-world examples of AI algorithms involves the use of human beings and computers in unison. For instance, if a machine cannot solve the issue, then humans should contribute to solving the problem. Therefore, the system would create a continuous feedback loop that would learn and improve continuously with every iteration. Subsequently, human-in-the-loop could help in ensuring accurate datasets that could lead to better precision and safety.
Consistent Testing of AI and Machine Learning Models
Another notable approach for resolving different types of AI bias involves consistent testing of AI and ML models. Regular testing of AI and machine learning models ensures that AI algorithms can mitigate bias and ensure accuracy. Furthermore, you should also compare the results of the AI algorithm to the algorithms designed by humans. It can help ensure that the algorithm makes fair decisions.
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Conclusion
The review of different examples of AI bias has shown that AI systems can improve and make accurate decisions. You should note that AI bias can be a major problem for effectiveness of AI algorithms. The impact of AI on real-world use cases could lead to negative sentiment around adoption of AI.
However, a comprehensive understanding of different types of biases in AI and effective ways for mitigation of the biases. For instance, you should use a mix of real-world and synthetic data for training AI models to ensure fairness. Learn more about the best practices for preparing AI models to fight against bias.
