3 Ways to Assess and Mitigate Bias in AI
As artificial intelligence continues to evolve, it’s crucial to understand how bias in AI can affect marketers and consumers. To avoid a lack of trust with our consumers and employees, marketers need to assess and mitigate bias to detect and address potential unfairness.
Keep reading for three ways to evaluate and reduce bias in your company.
Dealing with Bias in People, Processes, and Platforms
Tackling bias requires a three part approach through people, processes, and platforms. We have to address all three areas because if there is a focus on only one, we will continue dealing with contamination from others.
How do we mitigate bias in people? It comes from two things: awareness and hiring.
For awareness, you need to understand your underlying biases and the biases of your staff. These intrinsic feelings can manifest themselves in our work based on skin color, religion, ethnicity, national origin, sexual orientation, and gender identity. There are great free resources to help you understand your unconscious biases (i.e. Implicit Bias Test by Harvard).
These evaluations can be a part of your training and professional development to help your employees become aware of what their underlying biases are.
The second part of people is where and how you hire. You can look for biases in the hiring process.
For example, Christopher S. Penn (@cspenn) of Trust Insights, worked at a company based in Atlanta, Georgia where the population was 56% African American. Although, at this company with over 100 employees, there were zero African Americans employed. When Chris was tasked with hiring a marketing director, he received hundreds of resumes and purposely cut out all parts of the resume except for work experience and skills. When they reached their top ten candidates, they realized that there was not a single Caucasian person in the mix.
When you decontaminate the hiring pool from bias, it gives hiring managers the opportunity to be diverse and representative.
We deal with bias in our processes through screening and detection.
You have to build processes in your organization that proactively look for and assume biases in your data, algorithms, and models.
Second, the company should consider implementing a governance checkpoint as a standard procedure. In this procedure, the company will have different people in charge of the governance process and different people who own the Q&A of the governance process to avoid biases in the governance procedure itself.
Third, we have to mitigate and prevent bias in our technologies, systems, and platforms.
There are great resources to help you mitigate and prevent bias in our systems through reweighing, optimized pre-processing, and adversarial debiasing.
Reweighing weighs examples in each group and label combination differently to ensure fairness before classification.
Optimized pre-processing learns a probabilistic transformation that can modify the features and labels in the training data.
Adversarial debiasing learns a classifier that maximizes prediction accuracy and simultaneously reduces an adversary’s ability to determine the protected attribute from the predictions. This approach leads to a fair classifier as the predictions cannot carry any group discrimination information that the adversary cannot exploit.
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