As much as we might like to imagine that artificial intelligence acts as a neutral system, making decisions based purely on objective data, the truth is that this technology can be just as susceptible to bias as the people who train it. Understanding this capacity for bias — and how to account for it — is critical for any business that expects to deliver on AI’s real potential and avoid its potential pitfalls.
How Does AI Bias Happen, and Why Should Businesses Care?
There are a number of ways bias insinuates itself into AI. Inherent biases from historical data, for instance, commonly make their way into AI’s output. Even if the underlying data is free of inherent bias, however, it can still fail to represent specific segments of the population, resulting in incorrect information that can be detrimental to both businesses and consumers.
Data isn’t the only way bias creeps in. It can also come from the people responsible for the systems and models that lead to AI’s insights. Predispositions from data engineers often lead to confirmation bias in AI, where the systems favor evidence that confirms a conclusion already made by the designers rather than looking at everything as objectively as possible. Errors in the way data is collected or measured can also lead to AI subjectively weighing certain data more than others, ultimately skewing the results.
Left unchecked, these biases lead to inaccurate insights and reinforced stereotypes that can cause real harm to organizations and the people they’re attempting to serve. From a business perspective, incorrect predictions or assessments have financial repercussions, such as misallocating resources or targeting the wrong market segments. In addition, business decisions based on biased insights can lead to serious reputational damage and mistrust from customers.
On the consumer side, AI-driven chatbots have been known to offer incorrect and biased information, while facial recognition technology has a well-documented history of poor performance with people with darker skin tones. AI algorithms that aren’t corrected for bias, meanwhile, have been responsible for discrimination on the basis of gender and race in hiring and lending. Even healthcare isn’t immune: In the past, AI has been less likely to recommend additional medical care for Black patients than for white patients, despite having the same health conditions.
In other words, the impact of bias in AI is very real. Though AI has the potential to revolutionize decision-making by providing rapid and data-driven insights, organizations must be aware of its biases and actively work to mitigate them. If they don’t, then they should continue to expect unfair decisions, financial losses, and significant reputational damage.
How to Prevent AI Bias
We hope it’s clear by now why bias in AI needs to be managed. Now, it’s just a matter of figuring out how to prevent AI bias. Here are seven steps that you can take to improve your company’s AI bias detection and prevention efforts:
1. Diversify your data.
Ensure that the data you collect represents all relevant groups, especially those that tend to be underrepresented. In order to compensate for any deficiency, you may need to generate synthetic samples or use techniques such as stratified sampling, resampling, or reweighting.
2. Build a diverse team.
One of the best ways to ensure your data is properly representing every relevant demographic is to have a team that comes from varied backgrounds and perspectives. Diverse teams are more likely to spot biases that might be missed by less heterogeneous groups.
3. Audit and review
Regularly audit your data for potential biases. Use statistical tests and visualization tools to identify any anomalies or skewed distributions. Consider third-party audits for a truly unbiased review. You can also set up automated tools and software designed to detect and highlight biases in datasets, flagging potential areas of concern for you to review.
4. Encourage transparency.
Whenever possible, use transparent and open data sources. Make sure, too, to document all steps of the data collection and preprocessing stages. Knowing where data comes from and how it’s collected can provide insights into potential biases. Transparency can also help you better understand the historical context of the data. If you can see that the data reflects historical biases, then you can consider ways to adjust.
5. Continuously monitor the process.
Bias detection and mitigation are not one-off tasks. Continuously monitor data and your models for biases. As new data comes in or the external environment changes, so will the steps you need to take to eliminate bias.
6. Create a feedback loop.
Establish mechanisms to gather feedback on data-driven decisions. This can help identify cases where biased data might have influenced a decision. In that same vein, train staff on the importance of unbiased data and the risks associated with biased decision-making. That way, they know what to look for.
7. Engage with stakeholders.
Connect with people invested in your business, especially those from underrepresented groups. This will help you better understand others’ concerns and perspectives when it comes to data collection and usage. With their input, you can then establish ethical guidelines. This can serve as a framework for ensuring fairness and transparency in all your data-driven activities and can help pave the way for more ethical AI.