Everyone everywhere seems to be working to appear as AI-savvy as possible. Organizations are frequently hearing that if they aren’t using AI, they’re already behind. As its use becomes more mainstream, AI bias becomes an increasingly crucial topic to address.
What is AI Bias?
AI bias refers to the occurrence of unfair, stereotypical, skewed, and/or prejudiced results generated by AI systems.
How does AI Bias Occur?
Humans, at our core, are natural creatures of bias, which we reflect in our work; complete objectivity is a nearly impossible feat. AI is trained by identifying patterns in massive amounts of human-generated data and using it to craft new data. As a result, biased results are to be expected.
What are some real examples we have seen of bias in AI?
AI bias in health care has included…
- Offering different medical advice when a prompt indicates a patient is black, such as omitting medication
- Underrepresenting darker skin tones in skin cancer screenings
- Misdiagnosing women for numerous disorders by focusing on symptoms more common in men
AI bias in image generation has included…
- Presenting men in leadership roles significantly more often than women
- Using lighter skin tones as a default race, grossly underrepresenting other races
- Presenting homeless people with darker skin
AI bias in the job market has included…
- Prioritizing men over women due to stronger associations with typically sought after qualities in the hiring process, like leadership and management skills
- Automatically excluding applicants from certain zip codes
- AI interview platforms evaluating candidates on tone, accent, and facial movements
Why does this matter?
Instead of increasing inclusivity, understanding, and opportunity, AI has sometimes shown the opposite impact, using historically biased data to exclude certain populations, enforce stereotypes, and perpetuate prejudice as fact. Without keeping AI bias in check, we can take continuous steps backward in the struggle for a more equal and fair society.
What can we do?
You don’t need a technology background to take action against AI bias. Just a few practical steps can make a big difference in the accuracy and fairness of AI-assisted work:
- Know your tools. Learn how the AI systems you use are trained and what data they rely on. This will inform the type of information upon which the output is based.
- Cross-check results. AI output should be treated as a starting point, not a final answer. Fact-checking and editing with human judgment is always a good idea rather than placing your trust in AI.
- Diversify perspectives. When building content or making hiring decisions, involve people with different backgrounds and viewpoints. This ensures critical human thinking from varied backgrounds and perspectives.
- Ask questions. If an AI-generated result seems “off,” pause before you approve or publish it.
WebServes will be posting regularly on issues related to AI adoption and its implications for effective AND responsible use of AI.
