Addressing Potential Bias in AI

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Brookings defines artificial intelligence (AI) as “a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence.”  Replicating human intelligence in machines has positively influenced data collection, manufacturing processes, solving efficiency issues, and other business processes.

Even with its various benefits, AI has been running into some challenges when it comes to bias. It’s important to continue researching these biases and implementing anything capable of ridding AI of discrimination completely.

Here are three situations when AI is regularly biased but shouldn’t be, guidance to move forward in each, and a bit more on why it’s essential to innovate AI so we can reap its benefits responsibly.

Three Cases of AI Bias

Artificial intelligence is an excellent idea in theory. There’s a thin line between responsible and irresponsible when it comes to using machines to predict future behavior based on past interactions, sift through data, identify critical information, or make decisions without emotional distraction.

But with its continued use, scientists, data analysts, and developers are noticing some apparent biases that have to be addressed for AI to be used effectively.

Here are three hurdles AI has been running into regarding bias and tips on how to overcome these challenges to leverage the benefits of artificial intelligence.

Recruiting and Hiring

One of the most highlighted bias challenges in artificial intelligence is when it’s used in hiring and recruiting processes. Chatbots, résumé-screening tools, and online assessments, among other tools, are all used to automate various hiring and recruiting strategies. Bias in recruiting and hiring processes is hugely detrimental to forming a diverse workforce.

Your application may never make it past an Applicant Tracking System (ATS) if the system’s data is biased. Gender, names, and race have excluded perfectly qualified candidates from being invited for interviews because the AI system was trained this way. Eliminating bias in AI used for recruiting and hiring would ensure that every candidate is getting a fair shot at a position based on their qualifications versus being eliminated despite them.

Recruiting and hiring processes should be personalized. Ensure that you’ve found a balance between human influence and AI use to ensure candidates are consistently chosen based on the proper company criteria.

Creation and Development Process

The creation and development process is largely where bias starts in AI. If the people who create and develop artificial intelligence machines, tools, and software are biased, they’ll consciously or subconsciously program the system with that same bias.

Artificial intelligence is only as good as the data inputted and the quality and fullness of the data collected. Those involved in the creation and development process should be required to detach their personal experiences from anything created at work.

The AI field should be diversified first to help dismantle any bias in the creation and development process. When bringing people on board for your implementation of AI, structure your hiring process to eliminate any candidates that display any significant bias, discriminatory, or racist behaviors and thought-processes. Ensure anyone you hire is committed only to diversity, change, and wholly supporting individuals across various cultures, races, ethnicities, and backgrounds.

Social Media Algorithms

Billions of people in the world use social media. If you’re one of those people, you know how vital algorithms are to the content we’re showed and how our content shows up on other people’s timelines and pages. When algorithms are biased, it adversely affects the relevancy of content offered and how influential you can become on these platforms.

For example, in 2019, Facebook allowed its advertisers to intentionally target adverts according to gender, race, and religion. Women were shown jobs geared toward nurturing roles and excluded from seeing job ads for masculine roles like janitors, drivers, and construction work. After discovering this bias in their options for targeted ads, they eliminated any ability to target individuals based on race, gender, or age in their ads.

All social media platforms should follow Facebook’s lead and be intentional about eliminating any ability to target people based on things like age, gender, race, and ethnicity. If you’re running an ad of any sort, ensure they’re rooted in diversity.

Why it’s Important to Innovate AI

AI can help identify and reduce the impact of human bias. The benefits of artificial intelligence include:

  • Reducing the costs of labor.
  • Streamlining production.
  • Collecting and organizing large amounts of data.
  • Interpreting data.
  • Guidance on how to move forward with the data.

Will AI ever be completely unbiased? Not without innovation, further research, consistent monitoring, and improved implementation techniques. If we’re unable to achieve an entirely fair AI system, we’ll never fully be able to leverage all of the benefits of machine learning.

Someone should also monitor your AI use and pinpoint when it’s displaying a bias and redirect it, reset or retrain it.

Addressing potential bias in AI starts with acknowledging the current challenges. To move forward, we need to explore how humans and machines can work together to mitigate bias.

Beau Peters is a freelance writer based out of Portland, OR. He has a particular interest in covering workers’ rights, social justice, and workplace issues and solutions. Read other articles by Beau.
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Beau Peters | Radio Free (2021-12-06T08:17:38+00:00) » Addressing Potential Bias in AI. Retrieved from https://www.radiofree.org/2021/03/30/addressing-potential-bias-in-ai/.
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