Transparency and Fairness in AI Model Development

This is a guide on transparency and fairness in AI model development.

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While there is likely always going to be bias, considering transparency and fairness can help to mitigate those biases and create a more equitable environment. By examining the fairness of a model from the beginning of the process, there is a higher chance of producing outputs that are accurate.

 

Ensuring that the system is fair, builds trust and good faith with the users. At all stages of the development process, bias can make its way into the model. From when the issue was first identified, to the model being iterated and improved, there are opportunities to counteract bias. In the first stage, there needs to be an awareness of how the problem is framed, so the research is balanced. When discovery and data collection occurs, are all groups being fairly represented?

 

Testing and confirming the model is also a good place to question the balance of perspectives in the dataset. Once the model is implemented, there are still opportunities to mitigate bias. Collecting feedback around the ways the model fails to capture the whole unbiased picture, can go into the next iteration of the model. Improving the model's transparency along each step, helps to keep the model's goals aligned with ethical goals and values. While development of the model is taking place, continuously consider the level of explainability to the users.

 

Will users be able to understand how the system is working overall? The higher the transparency of the way the system works to model the data and produce insights, the better capable the user will be in providing the right information and feedback in order to build a better iteration and more trust between the user and the model. Being transparent with the way the system is using data, ensures the right information is provided to the system.

 

Transparency in the way the model works leads to fairness in the AI, because the AI is open for accountability and inspection. When models are transparent, informal auditing occurs from users and other stakeholders interested in seeing how the algorithm works. Fairness in AI is complex and occurs when there is intentional effort given to counteracting the biases that happen in development, data collection, and beyond.

 

When identifying bias within the development process, the goal is to produce a model that has unbiased and fair results. When fairness is not considered within the process of developing an AI model, there is the possibility for people to end up harmed using the model. The risk of negative outcomes decreases with an increase of fairness. When decisions are made from the results of an AI model, it is important for the model to be fair and unbiased.

 

If the model is discriminative to a population, it can cause harm to people in that population. The stakes can be extremely high. Consider the impact of AI being used in law enforcement, healthcare, and financial decisions, and the importance of the AI producing results that will not cause a group of people harm. Bias can occur when there is a lack of data or when there is a missing perspective in the model development.

 

These biases can lead to potentially negative impact, and that is why fairness is so important to consider when developing models. Although fairness is not as tangible and can be more subjective, it is still important to consider while making and using machine learning systems. It is also important to identify fairness constraints earlier in the process to ensure respect is shown to all users. There are two ways to think of fairness: fairness at the individual level and fairness for the group.

 

These might have conflicting and competing interactions. However, it is important to find the balance between them. Adjusting and calibrating for fairness throughout the development and implementation process improves the model. This can be accomplished by asking questions about fairness and perspectives throughout the process.

 

Within the development of the model, there are ways to counteract bias by continuing to question and evaluate the model at each step. From examining the framing of the problem and goal of the model, to identifying any gaps in the dataset, there are many ways that bias can be avoided. Is the dataset diverse? Are there missing groups or groups that do not include many data points?

 

Asking the right questions can help identify and eliminate the biases that exist in the data and the model during development. This will help support a smoother implementation that can focus on the functionality of the model, versus the faults and gaps to be closed. Identify and eliminate any present biases during development to support more accurate insights from the outputs.