We all know how important a robust method for fair and accurate artificial intelligence is. Even if we don’t understand it on a technical level, the results from any form of AI will affect every aspect of our lives.
But we live in a world fraught with racism, sexism, ableism, xenophobia and many other -isms you may not have even realized exist.
You might not be able to change society overnight or choose what kind of society your children grow up in but in your everyday interactions, jobs and families there is plenty you can do to either further drive the wedge or help bring us together.
Why work on fairness in artificial intelligence?
When it comes to AI, the potential for unfairness and inaccuracy is high. That’s why it’s important to work on making artificial intelligence more fair and accurate. Here are some ways to do that:
- Use data from a variety of sources to train your AI system. This will help you to ensure that your AI is not biased towards any one perspective.
- Test your AI system regularly to check for accuracy and fairness. Make sure to correct any errors you find.
- Be transparent about how your AI system works. Share information about how it makes decisions so that people can understand and trust it.
By taking these steps, you can help make artificial intelligence more fair and accurate. This is important work that will benefit everyone in the long run.
How do we define fairness?
When it comes to artificial intelligence (AI), fairness is often thought of in terms of accuracy. That is, if AI algorithms are accurately able to identify individuals or groups that pose a threat, then they are considered fair.
However, this definition of fairness ignores the potential for AI algorithms to unintentionally discriminate against certain groups of people. To address this issue, researchers have proposed a number of different ways to make AI more fair and accurate.
One approach is to develop algorithms that can detect and correct for bias. Another approach is to create transparent AI systems that can explain their decision-making process to users.
Finally, some researchers have suggested that we need to rethink the way we define fairness itself. No matter which approach we take, making AI more fair and accurate will require a concerted effort from all stakeholders involved in the development and use of AI technologies.
What are the potential benefits of fair AI?
AI has the potential to be immensely beneficial to society. It can help us diagnose and treat diseases, make our transportation systems more efficient, and even solve crimes.
However, AI is only as good as the data that goes into it. If that data is biased, then the AI will be biased as well. That’s why it’s important to make sure that AI is fair and accurate.
There are a number of ways to make sure AI is fair and accurate.
First, we need to ensure that the data used to train AI systems is representative of the population as a whole.
Second, we need to monitor how AI systems are performing over time to make sure they are not biased against certain groups of people.
Finally, we need to be transparent about how AI systems work so that people can understand how they are being used and why they may be making certain decisions.
By taking these steps, we can help make sure that AI is a force for good in society and that its benefits are available to everyone.
What are some recent results in Fairness and Accurate Machine Learning applied to Healthcare Informatics, Banking and Credit Systems, Criminal Investigations, and Communities?
Recent results in Fairness and Accurate Machine Learning applied to Healthcare Informatics, Banking and Credit Systems, Criminal Investigations, and Communities show that these systems can be made more fair and accurate.
However, there is still work to be done in order to make these systems perfect. Some of the recent results include:
- A study published in the Proceedings of the National Academy of Sciences found that a machine learning system trained on data from a large hospital was able to predict which patients would develop sepsis (a potentially life-threatening condition) with 90% accuracy. The system was also able to identify patients at risk of sepsis 24 hours before they developed the condition, giving doctors time to intervene.
- A team of researchers from Harvard University and MIT have developed a machine learning system that can automatically detect bank fraud. The system is said to be able to detect fraudulent activity with 99% accuracy.
- A study published in the journal Science found that a machine learning system was able to accurately predict whether or not a person would commit a crime. The system was able to predict crime with an accuracy of up to 95%.
- Researchers from Vanderbilt University have developed a machine learning system that can help identify traumatic brain injuries early on, which could enable doctors’ to intervene earlier and provide life-saving treatment.
- Researchers used machine learning to convert a smartphone or laptop into a EMG (electromyography) sensor so that the device can read nerve signals. The team hopes that devices equipped with such sensors could replace nerve conduction tests, which requires doctor visits and the patient having their nerves prodded with metal needles.
- Google built a prototype of an AI system that learns how to do simple household tasks without being told what to do. In one demonstration, a person pushes objects onto a dinner table but doesn’t say where they’re supposed to go—the system senses that they belong in a bowl on top of the table, and rolls them in just so. (Acquisition of chef robots.)
- Twitter released an API that allows developers to access tweets as they’re being posted. For example, instead of directly tapping into Twitter’s fire hose, a bot or app could at first request only the most popular links currently being shared across the network.
- YouTube announced their in-app creation capability allowing you to create videos right from your smartphone, instead of having to log in to another platform.
- Facebook is beginning a major expansion into mobile messaging. The new Facebook Messenger app lets users communicate with both email and Facebook friends without leaving the Facebook mobile OS Wearable Tech-Google Glasses launch!
- Also check out our other article on How AI and machine learning are helping to tackle COVID-19?
Conclusion
In order to make artificial intelligence more fair and accurate, we need to put in the effort to understand how these systems work and what factors influence their decision-making. Only then can we hope to create algorithms that are truly equitable and effective. What do you think is the most important thing we can do to improve AI? Let us know in the comments below.