AI bias can have negative consequences for businesses and their customers. Examples include a facial recognition software that misidentified images of minority people, and an algorithm that showed racial bias in diagnosing heart attacks.
The most common cause of AI bias is a skewed or noninclusive dataset used to train or program an algorithm. To prevent this, organizations should document their methods of selecting and cleansing data sets.
Identify the Source of Data
Often the first step in mitigating AI bias is identifying where it originated. There are many possible sources of data bias in machine learning, ranging from the type of data used to the selection and curation process to the model training and deployment steps. Each of these processes involves potential points for human error.
The GIGO principle (“garbage in, garbage out”) is a common saying in computer science and data analytics that describes the fact that the quality of output data is directly proportional to the quality of input data. Consequently, if the source data has a skewed or incomplete representation of a particular population, the resulting AI results can have a negative impact on that group.
There are a variety of sources for AI data, including internal company data from customer relationship management (CRM) systems and surveys or data-capture forms. But even this information must be carefully checked for bias. For example, CRM data might contain timestamps that connect purchases with amounts spent, which could lead to aggregation bias, where the AI model draws conclusions about purchasing patterns that don’t actually exist.
Some types of data set bias are inherent in the way the data is collected or recorded, while others are a result of human decision-making and bias. A simple example of intrinsic data set bias is the tendency of an experimenter or observer to favor observations that confirm preconceived ideas. This is also known as confirmation bias and can lead to false conclusions that can have real-world consequences. While many argue that some level of censorship is necessary to prevent harmful content, there’s a growing concern that censorship of AI could lead to bias. When AI is programmed to filter content based on specific guidelines, those guidelines can inadvertently reflect the biases of the developers or the political climates in which they’re created.
Other examples of data set bias include how the data is categorized or filtered. For example, if an AI model is trained to identify wedding dresses, it will likely recognize them only by recognizing shades of white. As a result, the model will fail to detect that non-white dresses are equally as beautiful.
Other types of data set bias include demographic parity and equality of predictive odds. To mitigate this, machine learning models can be modified to optimize for different metrics. These can be achieved by modifying or adding to the objective function of a given algorithm or by using techniques such as adversarial classification, which involves optimizing a model for inaccurate predictions, which highlights potential weaknesses and can then be fixed.
Identify the Target Population
Many AI biases occur because of a skewed or non inclusive data set used to create the model. This is a problem that can be mitigated by using a dataset that is as diverse and inclusive as possible.
In addition, developers need to pay close attention to how the data is processed when creating an algorithm. This includes any aggregation, imputation, or other data-processing techniques that could lead to bias. This can be done by ensuring that all patient demographics are represented in the data, and that different factors, including racial/ethnic, cultural, and language differences are captured and considered.
Another common source of AI bias is due to the personal biases and assumptions of the programmer who created the model. This can be a serious problem, as it often leads to algorithms that are unfair or inaccurate in real-world applications. For example, a facial recognition system that is biased toward certain racial groups can lead to wrongful arrests and unequal job opportunities. Similarly, an AI-driven cancer diagnostic tool that is biased against individuals with dark skin can lead to misdiagnoses and potentially harmful treatments.
Lastly, bias can also occur during the testing and validation of an AI model. This can be caused by a number of issues, such as aggregation bias (where information is combined from datasets with different conditional distributions) or evaluation bias (where an individual favors information that confirms existing beliefs). This can have significant impacts on the accuracy and effectiveness of the model when it’s deployed in high-stakes applications.
Companies that deploy AI models and algorithms that are found to be biased risk a loss of trust from their customers, employees, and the general public. This can lead to a decline in business, a lack of innovation, and even resistance to the use of AI technology.
As such, companies need to be vigilant in their efforts to avoid AI bias and ensure that their AI is fair and effective for all of their users. This includes identifying potential sources of bias, developing appropriate mitigation measures, and continuously testing and validating their models to ensure they remain free from bias.
Identify the Problem
When developing AI models, it is important to understand that algorithms can reflect prejudices and biases that exist in society. The first step in preventing these biases is to identify where they originate. The most common source of bias is found in the data used to train an AI model. This data may contain existing prejudices and a lack of diversity, which will lead to biased results.
This is known as training data bias. It can be caused by a variety of factors, including the selection of data points that are used to train an algorithm and the way these data points are weighted. For example, an algorithm that is trained on data that focuses on income or education may reinforce existing stereotypes and discrimination against people from low-income backgrounds. This type of bias can be difficult to identify as it may not be visible in the model itself, but in the underlying data.
Another common cause of bias in AI systems is the decision making process that leads to the creation of an algorithm. These processes often involve a number of complex mathematical decisions, and they can be susceptible to bias. For example, an AI algorithm that is developed to predict the likelihood of a patient returning for follow-up care after a hospitalization may be biased against patients who live in less affluent neighborhoods. This can lead to a lack of access for these patients, and it can also result in poor outcomes for them.
Other causes of bias in AI include skewed or noninclusive datasets and the way an algorithm is programmed. Various techniques have been implemented to mitigate these problems, including objective function modification and adversarial classification. These methods are designed to ensure that an AI model is not biased by ensuring that it is optimized for different metrics such as demographic parity or equality of predictive odds. Increasing the diversity of the data set is another way to prevent these types of biases. Additionally, making sure that an AI model is interpretable can help to reduce these types of biases by enabling the creators of the AI system to evaluate how different features of the data impact the resulting predictions.
Identify the Solution
AI-based systems are becoming increasingly pervasive in our lives, automating decisions and taking on tasks that previously required human intervention. However, AI bias can skew results and lead to unfair and inaccurate outcomes. It’s essential to learn how to prevent bias in AI models and algorithms before using them in your business.
AI bias can occur in any stage of the AI lifecycle, from problem definition, data set curation, model building and evaluation to deployment. It can be caused by explicit biases that are encoded in the algorithm, or implicit biases that result from the design of the AI system itself. It can also be caused by the environment in which the model is being used, such as when aggregating data from different groups leads to misrepresentation of differences or when a model is evaluated on unrepresentative datasets.
The most common types of AI bias include aggregation bias, demographic bias, confirmation bias and adverse impact bias. An example of aggregation bias is when an algorithm uses a single metric to evaluate the performance of multiple subpopulations. This can lead to the conclusion that a model is performing well for all groups, when in fact it may not be suited for any of them. An example of demographic bias is when an AI system evaluates patients on a single metric such as age or race, leading to incorrect conclusions about the conditional distribution of those groups.
A final type of bias is adverse impact bias, which occurs when an AI system causes harm to a group. An example of this is when an AI-based system is integrated into police department software, where it can lead to discriminatory treatment and even physical injury or unlawful imprisonment.
Identifying and preventing AI bias is a complex task that requires multidisciplinary expertise. It’s important to involve ethicists and social scientists in the design of AI systems and to make sure that the AI team is diverse. This will help to ensure that bias is identified and addressed before it has a negative impact on your organisation.
Another way to reduce bias is to build interpretable AI models. This allows you to understand how different features of the underlying data contribute to your predictions and can allow for a fairness assessment to be conducted. It’s also important to test the model on a variety of datasets, and not just your own internal data. This will help to ensure that your predictions are not influenced by the underlying data set.
