The Dark Side of AI and Algorithm Biases

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Introduction
How Does AI Operate, and How Can It Promote Biases?
How Can This Happen?
Real-world Examples of AI Bias
Why AI Bias Matters
What Can We Do to Stop AI Bias?
AI Bias Future Outlook

The Dark Side of AI and Algorithm Biases

Curated by

cdteliot

4 min read

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As reported by Actian, algorithmic bias in artificial intelligence systems can lead to significant disparities and unfair outcomes, exemplified by Amazon's hiring algorithm favoring male candidates and a criminal justice system disproportionately affecting African American offenders. This dark side of AI raises concerns about the potential for technology to perpetuate and amplify existing societal inequalities.

How Does AI Operate, and How Can It Promote Biases?

actian.com

First, it's important to know how AI operates. AI systems rely on machine learning, where a training algorithm is put in place

. Before diving into the specifics of AI biases, it's important to understand how AI works in general. Most systems rely on machine learning, which involves training an algorithm on massive datasets to make predictions

. The more data the machine is fed, the better it will work. However, the problem is that the data it learns from can be biased

. Algorithmic bias sees discrimination that's caused by algorithms based on biases per the datasets they were trained on

. The biases often (but not always) are a result of societal prejudices. They can surround race, gender, and more

5 sources

How Can This Happen?

Bias in AI aren't normally on purpose but can occur for many reasons. The most common source of AI bias is from the data it's fed. For example, say an AI model that surrounds hiring data from a male-focused industry might decide that men are more qualified than women, which keeps gender inequality raging in hiring practices

. AI is trained on historical data, but recorded history is filled with injustices and biases. Whether we're talking about racial profiling in criminal justice or housing discrimination, AI systems can keep perpetuating these injustices

. Also, bias often emerges because of how an algorithm is put together. If the AI model doesn't account for variables like race and gender when making their decisions, it can discriminate against certain groups

. Finally, when AI systems are used in the real world, they can create feedback loops. For example, if a predictive policing algorithm unevenly targets a minority neighborhood that's based on historical data that's biased, increased police presence in the neighborhood could result in more arrests and police presence

6 sources

Real-world Examples of AI Bias

datatron.com

AI bias can go deeply into applications used in the real world and can lead to devastating problems

. Here are some key examples:

Facial Recognition Software: These technologies have wrongly targeted people with darker skin tones and women. A study by MIT Media Lab found social recognition systems can lead to error rates of nearly 35% when identifying women of color, resulting in numerous wrongful arrests
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Predictive Policing: AI-powered systems used to predict crime hotspots can lead to increased racial profiling. In Oakland, California, such technology increased police activity in predominantly Black neighborhoods, even without an increase in crimes
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Hiring Algorithms: AI tools for recruiting have shown bias against minorities and women. Amazon discontinued a recruiting tool after its algorithm was proven to be biased against women
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Loan Approval: AI used by banks for loan approvals has been found to reject applications from minorities regardless of their credit scores
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These examples highlight how AI bias can perpetuate and amplify existing societal inequalities across various domains, from law enforcement to financial services.

5 sources

Why AI Bias Matters

hbr.org

Sadly enough, so far, it's been proven that AI bias is higher than anyone would like to admit. As AI systems become more and more popular and widely used in hiring, the medical world, criminal justice, finance, and other fields, there must be some important algorithm shifts made.

Here are some obvious reasons why AI bias must be addressed:

Continuation of Inequality: AI bias will lead to the same inequalities that have been plaguing society for centuries and will continue to make it harder for groups to break free from systemic oppression.
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Loss of Trust: AI bias completely undermines the root of AI's legitimacy. If AI gives people, corporations, and institutions the impression that it can't be trusted, it simply will fail. If AI is to be a key part of our existence, it should be accurate and fair.
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Legal and Ethical Concerns: AI systems that are rooted in biased algorithms will lead to lawsuits and legal problems for companies of all shapes and sizes.
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Global Impact: AI systems being used around the world can pose numerous risks across borders. For example, say a surveillance technology that is built in Russia, is exported to another authoritarian regime it will become easier and easier to oppress and control their populations.
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6 sources

What Can We Do to Stop AI Bias?

The problem of AI bias is quite monumental, but there are ways that humanity can overcome it including the following:

Diverse Data Sets: One of the key ways to deal with AI bias is to make sure that algorithms are given diverse sets of data that they can train on. This means gaining data from as wide of a range of people as possible. Innovators must not be lax but seek to expand their datasets so that they properly encompass society as a whole.
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Algorithm Audits: Constant audits are a must so that it can be certain that algorithms perform across different demographic groups. Then, it is possible to catch the biases and widen the datasets used.
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Inclusive Development Teams: One of the best and fastest ways to transform AI bias is to make sure that teams are as diverse as possible. When teams include a variety of backgrounds and mindsets, it's most likely that biases will become a thing of the past.
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Regulation and Oversight: Governments must look closely at the AI systems that they deploy and also require that companies that use them comply with a set of rules. These rules must make it mandatory that regular bias audits are run and that governments and companies make fast adjustments to their algorithms when an issue is found.
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6 sources

AI Bias Future Outlook

AI is a powerful tool and with such powers, AI bias must be addressed head-on. They are by far one of the most dangerous challenges that the industry and those affected by biases face. We must all look closely at the root causes, such as biased data, data design, and more. The future of AI is still unknown, and thankfully we all have the power to make sure that it is fair along the way.

3 sources

What are the long-term impacts of AI bias on society

How can AI bias be measured and quantified

What role do educational institutions play in addressing AI bias

How can AI bias be mitigated in real-time applications

What are some successful case studies of companies overcoming AI bias

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The problem of AI bias is quite monumental, but there are ways that humanity can overcome it including the following:

Diverse Data Sets: One of the key ways to deal with AI bias is to make sure that algorithms are given diverse sets of data that they can train on. This means gaining data from as wide of a range of people as possible. Innovators must not be lax but seek to expand their datasets so that they properly encompass society as a whole.
1
2
Algorithm Audits: Constant audits are a must so that it can be certain that algorithms perform across different demographic groups. Then, it is possible to catch the biases and widen the datasets used.
3
4
Inclusive Development Teams: One of the best and fastest ways to transform AI bias is to make sure that teams are as diverse as possible. When teams include a variety of backgrounds and mindsets, it's most likely that biases will become a thing of the past.
5
6
Regulation and Oversight: Governments must look closely at the AI systems that they deploy and also require that companies that use them comply with a set of rules. These rules must make it mandatory that regular bias audits are run and that governments and companies make fast adjustments to their algorithms when an issue is found.
3
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