How Bias in Machine Learning Affects Gen Z: What You Need to Know

As a Gen Z, you're probably used to hearing about the latest technological advancements and how they're going to change the world. From social media to virtual reality, our generation is constantly adapting to new technologies. However, there's one thing that often goes unnoticed - bias in machine learning. It's a topic that's important to understand because it has the potential to affect all of us in ways we may not even realize. In this blog post, we'll discuss how bias in machine learning affects Gen Z and what you can do to prevent it.

What is Bias in Machine Learning? Machine learning is the process of teaching computers to learn from data. However, the data that computers are fed can contain biases that are unintentionally introduced by humans. This can result in the computer making inaccurate or unfair decisions. For example, a machine learning algorithm used in hiring might unfairly discriminate against certain candidates based on their race or gender. Bias in machine learning can occur at any stage of the process, from data collection to model training to decision-making.

How Bias in Machine Learning Affects Gen Z As a generation that's grown up with technology, Gen Z is especially vulnerable to the effects of bias in machine learning. We're more likely to use social media and other online platforms, which means our data is being collected and used in ways we may not be aware of. For example, algorithms used by social media platforms can inadvertently promote certain types of content while suppressing others, leading to an echo chamber effect.

Additionally, Gen Z is a diverse generation that values inclusivity and equality. Bias in machine learning can perpetuate existing inequalities and prevent marginalized groups from accessing opportunities. This can have a lasting impact on our generation and future generations to come.

Preventing Bias in Machine Learning Preventing bias in machine learning starts with being aware of its existence. As individuals, we can take steps to protect our data and be mindful of the information we share online. We can also advocate for more transparency and accountability from companies that use machine learning algorithms. By demanding that these algorithms are fair and unbiased, we can create a more equitable future for all.

Chat GPT-generated list of action items to prevent bias in machine learning:

1. Be aware of the data being collected about you.
2. Limit the amount of personal information you share online.
3. Advocate for transparency and accountability from companies that use machine learning algorithms.
4. Educate others about the potential consequences of bias in machine learning.

Real-Life Examples of Bias in Machine Learning To further illustrate the impact of bias in machine learning, here are some real-life examples:

• In 2018, Amazon scrapped a machine learning algorithm used in hiring because it was found to be biased against women. The algorithm was trained on resumes submitted to the company over a 10-year period, which meant it was trained on data that was already biased towards male applicants.

• In 2019, Apple's credit card algorithm was accused of gender bias because it gave women lower credit limits than men, even when they had similar financial histories.

• In 2020, a study found that facial recognition algorithms were less accurate in identifying people of color and women. This has implications for law enforcement and surveillance, as these algorithms are increasingly being used in these areas.

Conclusion Bias in machine learning is a complex issue that has the potential to affect us all. As a generation that values inclusivity and equality, it's important that Gen Z takes steps to prevent bias in machine learning. By being aware of the data being collected about us and advocating for fair and unbiased algorithms, we can create a more equitable future for all.