Artificial Intelligence (AI) is a powerful tool that can help us make better decisions, but it can also be prone to bias if the data it’s based on is not collected properly.
This is especially true in cases where the data used by AI algorithms shows unchecked racial bias. AI models are essentially taking the biases found in the data that we feed them, and amplifying them. This means that if we don’t create an equitable dataset of information, then AI-driven decisions can perpetuate racism and other forms of discrimination in society.
In this article, we will explore why data collection matters when it comes to AI and how we can ensure that our datasets do not reflect or propagate existing biases or inequality. We will also discuss the importance of using equitable data sets for decision making and how this could help prevent further injustice.
How and Why AI Perpetrates Racism
There have been numerous cases in which AI was designed and trained on data that was collected through human biases, leading to prejudiced decisions. In 2015, an automated hiring tool created by Amazon was found to be discriminating against women. This was due to the machine learning system being trained on existing resumes of mostly male applicants.
In the development and execution of AI-driven systems, we must take into account these biases that drive the data and design of current technologies in order to have fair outcomes. Without properly taking into consideration how race and ethnicity can be used to collect data, AI tools can perpetuate racism, sexism or other forms of discrimination.
Therefore, it’s essential for designers and researchers developing AI tools to assure accuracy regarding gender, ethnic identity and demographic information in any database used for training a machine learning system. This type of data collection will create a standard of fairness across all tools implemented in the future.
The Need to Address Bias in How Data Is Collected
AI technology has the potential to revolutionize many aspects of our lives, from medical care to customer service. However, the data that is used to “teach” AI algorithms can come with an inherent bias if it is not collected and analyzed in a thoughtful, responsible way.
This can be seen in the field of facial recognition. Injuries caused by misidentifications are much higher when applied to darker-skinned subjects as compared to lighter-skinned subjects. This bias has also been documented in other areas involving AI like hiring tools, credit scoring and loan decisioning systems.
To avoid perpetuating existing racial injustices, steps must be taken to address potential bias before it gets built into the AI systems we use. Data science teams must actively look for and identify any potential bias in their datasets, develop strategies for addressing it upfront and build features into their models that reduce or eliminate it altogether. Moreover, decisions makers should assess whether the data being used adequately reflects their target population and consider using tools such as algorithmic audits and fairness testing to ensure prejudice isn’t seeping into their models.
It’s clear that while AI offers tremendous promise, its successful deployment relies on data being collected in a responsible way that won’t result in unintentional bias creeping into the systems we rely on every day.
What Data Structures Should Be Designed to Prevent Racist Outcomes
AI algorithms are powered by data. No matter how sophisticated the technology, without the proper data, AI models can become dangerously biased. To ensure AI does not perpetuate pernicious systems of racism and inequality, it is important to consider how data structures are designed.
Data structure designs must be intentional to ensure fairness in output. Data should be collected in ways that do not perpetuate systemic racism or bias against certain groups. Here are a few tips for designing data structures that promote fairness:
- Collect demographic information on participants so one can understand how biases may be affecting outcomes.
- Create blind studies to minimize bias when collecting data or developing algorithms.
- Remove sensitive and personal attributes when collecting data – such as race, gender and religious affiliation – that may unintentionally lead to biased results.
- Analyze models regularly to identify any biases or patterns of discrimination in outputs and revise as needed to eliminate any inequities in output or outcomes.
By taking these steps, companies and researchers can help ensure AI systems promote equity and inclusion rather than perpetuate existing systemic oppression and discrimination
How Algorithms Impact Behavior in Communities of Color
Data driven decisions can have a profound impact on the lives of communities of color, specifically African-American communities. Algorithms are used to make decisions related to policing, healthcare, education and financial services. Understanding how data is collected, processed and managed is important for analyzing the potential bias in algorithms that are used to make decisions about African-American people.
Examples of Algorithmic Bias
Research into the use of algorithms has uncovered many examples of bias including:
- Health care: Research found that artificial intelligence (AI) models used to recommend treatments for diseases were often biased towards white patients over others.
- Education: AI models used for student admission decision making are also known to be biased towards white students with similar credentials as minority students.
- Policing: AI-driven predictive policing models have been found to be racially biased, leading to more aggressive enforcement in black and Latinx neighborhoods even when controlling for crime rate differences.
- Financial services: AI-driven mortgage underwriting models can be biased against people of color with similar qualifications compared to whites.
These examples demonstrate how AI can perpetuate racism if not designed and implemented carefully. Therefore, it is critical that data collection and analysis methods are designed ethically, taking into account cultural differences and avoiding implicit or explicit bias in order to foster equitable decision making in artificial intelligence systems.
The Importance of Public Policy to Check AI-powered Decisions
Data collection plays a key role in how AI is used, and it’s important to recognize that the way data is captured can influence the decisions made by AI-powered systems. This is why public policy must be put in place to ensure that these algorithms are equitably built and tested.
Specifically, public policy should create:
- rules around data protection and privacy, so that data is accessible only to those who need it, and that no one can be discriminated against based on the data they provide;
- regulations for collecting contextualized data about a user’s environment;
- guidelines for assessing the accuracy of models; and
- oversight of AI-enabled decision-making processes so bias in algorithms can be identified and addressed quickly.
By putting these policies in place, we can make sure that AI-driven decisions are more equitable and transparent – which ultimately helps us get closer to achieving a colorblind future.
Strategies for Holding Companies Accountable for Their Use of AI
When it comes to holding companies accountable for their use of AI, there are a number of strategies that can be employed. From increased oversight and regulation to public-private partnerships and direct intervention by Congress, data collection and responsible use of AI must be a priority.
Increase Oversight
Oversight of the development, implementation and use of AI must be increased so that any existing biases in data or algorithms can be identified, addressed and minimized. Stronger oversight measures can also ensure that companies who are experimenting with algorithmic decision-making processes understand the risks associated with such technology.
Public-Private Partnerships
The public sector, private industry and advocacy groups should work together to develop comprehensive guidelines for responsible development, implementation and use of AI, as well as enforcement mechanisms to ensure those guidelines are followed. This collaborative effort will help ensure that any potential harms caused by AI – such as the perpetuation of racism – can be identified, addressed and minimized quickly and effectively.
Congressional Intervention
Congress should pass legislation requiring companies to publicly disclose the types of data they collect about their users, how they are using it, what algorithms they are using to make decisions about their users’ experiences online, and what steps they have taken to prevent algorithmic bias from impacting their users’ experiences. Such legislation would provide much-needed transparency into how companies are using our data in order to protect against potential abuse or misuse.
AI may not be colorblind, but we can make sure it’s not a tool used to perpetuate racism. To do that, we need to remember that data is not neutral, it is contextualized and should be treated as such. We must prioritize communities of color and ensure that their data is collected justly, accurately and without bias when creating AI algorithms. All too often communities of color have been excluded from data collection processes, an oversight that leads to AI systems that can be dangerous and unfair. It is up to us to ensure that our data is collected and used correctly, to create more fair and equitable AI systems. We are in possession of the power to make sure that AI is no longer used to perpetuate racism and discrimination, but instead works for the benefit of communities of color.
