Bad Applications Of Machine Learning Are A Humanitarian Crisis

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First Get To Know How ML Works By Covering Some Terms

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Unethical ML Models Are A Problem

Whew — now that we got the jargon out of the way, let’s focus on the large problems that bad applications of ML cause and what it means for humanity.

The Information Apocalypse

Facial Recognition & ML In Decision Making

Facial recognition is very common around the world as 50% of the world currently allows the use of it. And only 3 countries have partial bans on the technology!

Facial recognition usage

GPT-3 Making Biased Predictions

GPT-3 is a language model developed by OpenAI, and developers have built an impressively diverse range of applications using the GPT-3 API. Such as:

Reason For Unethical Models

Using Data That Is Biased

The number of parameters that trained GPT-3

AI Research Is Secretive

Another reason contributing to ML models being unethical is the fact that only 15% of AI papers publish their code. To understand why that is a problem imagine giving a 10-year-old homework and saying that they can tell the teacher if they have completed it and checked over their answers.

So, How Can You Help Keep ML Ethical?

One major way is to take steps to improve the decentralization of AI and to be educated upon what it is. SingularityNET is one big company that allows anyone to create, share, and monetize AI services at scale.

To Recap

  • Bad applications of ML are a humanitarian crisis as they can cause an information apocalypse, cloud judgment, and make biased predictions.
  • The reasons for unethical models is because models use data that is biased to get trained, and it is hard to get data that is not biased.
  • The fact that AI research is secretive doesn’t force companies to regulate the way their AI works.
  • One step everyone can take to improve unethical applications ML is to learn about the decentralization of AI.

I am passionate about health and technology especially when the two are used together to help others.