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Algorithms are used in many different industries, most notably in artificial intelligence. Bias happens in algorithms when data is collected. The data can be incomplete, misused, mislabeled, or even misrepresentative

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Algorithms and biases are things that go hand in hand in today's world. This is because many people directly use or interact with algorithms in some way every single day. Social media, online shopping, job searching, anything you do online has some sort of algorithm built in.

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Ehtically algorithms have many upsides if done correctly, but equally as many downsides if done incorrectly.

Pros include increased efficiency, fast training, saves energy.

Cons include the many ways biases can be introduced to algorithms. skewed data, feedback loop, incomplete training data, and biased training data.

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Algorithmic Bias in AI: What It Is and How to Fix It

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Data misrepresentation is one example of what can happen when an algorithm bias occurs. Data misrepresentation happens when the coding on the algorithm is pointed intentionally or unintentionally regardless of what the data statistically portrays. Many times this is used by people to sway people towards their agenda.

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It was recently discovered that Amazon's hiring algorithm favored men over women. This created an unequal opportunity for candidates and was clear discrimination against female applicants. It was discovered that they trained the algorithm with past employee resumes who were primarily male.

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Facial recognition has been found to have higher error rates in recognizing people of color. While white people have been found to have a much higher percentage of success.

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Real world example credit scoring models

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Real world example credit scoring models

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Research has shown that people of color are not represented fairly in starting their credit journey. This is because it uses previous historical data that is already biased.

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Regulation and review are important for doing anything in life. It allows products and services to become better through critique and evaluation. To regulate biases in algorithms there needs to be longer testing periods with the algorithms and data needs to be presented as is with no blemishes. Lawmakers should also step in and make companies clarify how often they can be used and make users aware of when they are being used.

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Real world example Illinois AI video Act

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Real world example Illinois AI video Act

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In 2019 Illinois required employers to notify applicants that AI was being used to judge their video interviews

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In 2021 New York City passed local law 144 which required companies to use AI in the hiring process to conduct audits on the algorithms they are run on.

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What are the consequences of companies relying on algorithms?

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What are the consequences of companies relying on algorithms?

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There are both good and bad consequences of companies relying on algorithms.

The good include cost reduction, increased efficiency, and data-driven insights and results.

The bad include unfair hiring practices, financial discrimination, and a lack of transparency and accountability.

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Future Ready Leadership With Jacob Morgan

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The Dangers of Relying on Data and Algorithms for Decision Making - Jacob Morgan

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Real world Example Washington DC school district

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Real world Example Washington DC school district

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They created an algorithm to fair the lowest-performing teachers in the district. The algorithm sought to fire around 200 teachers in that district many of whom received many honors for their high level of teaching

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From the years 2016-2020, Meta was found guilty of fixing their algorithms on Facebook to promote misinformation in an attempt to influence the election and sway public opinion.

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Deep dive into Meta’s algorithms shows that America’s political polarization has no easy fix

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A team of some of the world’s leading social media researchers has published four studies looking at the relationship between the algorithms used by Facebook and Instagram and America’s widening political divide.

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Algorithms offer businesses many ways to streamline and utilize data like never seen before in human history. However, companies need to be aware of they way they use them and how much they use them. Humans are complex and intricate creatures that can't be boiled down to just a number.

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Should we worry about AI and algorithms in government? | Lyria Bennett Moses | TEDxSydneySalon

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Consumers just like businesses can enjoy many benefits from algorithms as well like the personalization of data and the ability to make decisions based on data faster and more efficiently. However, algorithms unchecked can cause discrimination, lack of transparency, overreliance, and a decrease in critical thinking skills.

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How I'm fighting bias in algorithms | Joy Buolamwini

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ESLBuzz. (n.d.). Plural of bias. ESLBuzz. https://eslbuzz.com/plural-of-bias/

The Ladders. (2018, October 10). Amazon reportedly scraps AI recruiting tool biased against women. The Ladders. https://www.theladders.com/career-advice/amazon-reportedly-scraps-ai-recruiting-tool-biased-against-women

McGill, M. (2018, July 26). Amazon's facial recognition shows bias against people of color. Axios. https://www.axios.com/2018/07/26/amazon-facial-recognition-racial-bias

DeWees, B. (2023). Why businesses need 3rd party algorithm audits. BDeWees.com. https://www.bdewees.com/business-needs-3rd-party-audit/

AP News. (2021, October 26). Facebook knew it was polarizing, but let it happen anyway. AP News. https://apnews.com/article/facebook-instagram-polarization-misinformation-social-media-f0628066301356d70ad2eda2551ed260

Stocksy. (n.d.). Teacher helping her young pupils in a class activity [Photograph]. Stocksy. https://www.stocksy.com/photo/2433618/teacher-helping-her-young-pupils-in-a-class-activity

TEDx Talks. (2019, March 18). How AI can enhance our memory, work, and social lives [Video]. YouTube. https://www.youtube.com/watch?v=UG_X_7g63rY

CNBC Television. (2022, April 14). The impact of AI bias on hiring and business [Video]. YouTube. https://www.youtube.com/watch?v=og67qeTZPYs

Vox. (2020, August 17). How AI-powered facial recognition is changing our world [Video]. YouTube. https://www.youtube.com/watch?v=y6FDBDGNYFc