Many people are familiar with the idea that algorithms can be used for good - they can help us find recommendations for movies and music, suggest which restaurants to try, and even match us with potential romantic partners.
What happens when these same algorithms are used to make decisions about who has access to essential services like healthcare, housing, and employment? In this article, we'll explore how algorithmic risk assessments can lead to new forms of economic exclusion and how we can mitigate their impact.
What is an Algorithmic Risk Assessment?
An algorithmic risk assessment is a method of predicting someone's likelihood of defaulting on a loan or committing fraud based on historical data. These assessments are often used in lending, insurance, and other industries to determine whether or not someone should receive credit or coverage. They work by analyzing a person's credit score, income, job history, address, and other factors to determine their level of risk.
How do Algorithms Lead to Economic Exclusion?
Algorithmic risk assessments have the potential to exclude certain groups from receiving essential services, especially those who may already face discrimination in traditional systems.
People of color, low-income individuals, immigrants, and others may be more likely to be denied credit or insurance coverage due to systemic bias in the data used by algorithms. This can lead to further marginalization and increased inequality.
Mitigating the Impact of Algorithms
There are several ways that we can mitigate the impact of algorithmic risk assessments. First, we can ensure that the data used by algorithms is representative and free of bias. Second, we can use alternative measures such as behavioral signals (e.g., bank account activity) to supplement credit scores and make decisions about access to services.
We can implement human review processes to check for errors or unfairness in automated decision-making. By taking these steps, we can help reduce economic exclusion and create a fairer society.
What new forms of economic exclusion arise when algorithmic risk assessments determine access to essential services?
While algorithmic risk assessment is meant to streamline decision-making processes by reducing human error, it may also lead to new forms of economic exclusion. The system may be biased against individuals who lack sufficient data, have low social capital, or exhibit behavioral patterns that are not easily quantifiable. This can result in unequal access to essential services such as loans, insurance, employment, healthcare, housing, and education.