Law in Artificial Intelligence

Artificial intelligence, typically known as ‘AI,’ uses computer systems to perform human tasks typically requiring intelligence, such as visual perception and recognition, decision making, and translation of languages. Most people think of artificial intelligence as robots, but it is not the case. Artificial intelligence laws address the rights and responsibilities of manufactured products and services and their users.

The Government setting up rules and regulations with regards to artificial intelligence is complicated. The complexity of technology makes it challenging to implement laws. Additionally, it is a broad sector that implements artificial intelligence applications ranging from the health, financial, education, and transport sector to mention a few. Many governments are taking an exploratory approach with regards to laws governing artificial intelligence. Let us brush over some of the statutes governing artificial intelligence, which apply to specific areas of artificial intelligence.

• Autonomous vehicles
Laws concerning autonomous vehicles have been implemented in 24 countries; the USA is one of them. This particular artificial intelligence requires regulation as it poses a human risk to individuals. So, it is because they operate close to human proximity, and any error in the software or hardware may cause lethal consequences. In the USA, most states have enacted some laws concerning these vehicles through deployment or testing their capability.

• Data privacy and sharing
Data is the primary source of artificial intelligence. It is the main driver that trains the AI to make decisions and perform on their own. As a result, many countries have set laws to prohibit sharing and exchanging data without prior consent. The regulations apply across all sectors that employ the use of AI.

• Ban on the further development of weaponized AI
The ban is to control weapons that would go beyond human control. Society is impacted by the explosion of AI used in other sectors. For this reason, when used in the manufacture of weapons may cause grievous infliction to individuals.

Final thoughts
Artificial intelligence is slowly taking center stage and is expected to play a prominent role in the future. It is significant in our daily lives. There needs to be attention to this technology to avoid human conflict. For this reason, there need to be laws governing this type of intelligence. Regulations are moving at a slow pace compared to technology, especially in the weak AI, which is currently used in search engines and self-drive cars.

Originally published on ChartWestcott.org

How AI is Preventing Fraud

Today’s fraud crimes come in many forms, such as identity and credit card theft, phishing, and false chargebacks, to name just a few. The next wave of AI-based fraud prevention is going to need to rely heavily on a mix of both unsupervised and supervised machine learning algorithms in order to combat those intelligent types of threats. Decades of transactions can be looked at within a few milliseconds in order to calculate a company’s risk score. Together, AI and machine learning programs can produce this fraud score for any digital business within seconds, providing a way for companies to take measures to protect themselves.

There are several main differences between unsupervised and supervised machine learning. Unsupervised learning just examines data and the relationships between data sets. These are input variables with no established output formula, which means there is only the incoming data to examine. Unsupervised algorithms can only sort and categorize data. There is no machine model to learn from because there is no answer to compare anything to. It is, however, very valuable when it comes to datasets because it can pinpoint any outliers that don’t match with other data, then cluster those abnormalities to be studied at a later date.

Supervised learning is rule-based. Supervised machine learning says that for every input X, there is a function that results in output Y. The program learns from past successes and errors, almost as if it’s being supervised or taught by a teacher. This gives it the ability to examine past trends and learning models and search for patterns in predictive models.

Both of these types of computer models bring something to the table with regard to protective measures, but neither one can do enough on their own to tackle modern-day hazards. That’s why they work together to form the ultimate security. Because fraud might display a completely before unseen pattern or follow a unique set of rules, a predictive model might miss something. That’s when unsupervised data clusters might hold the necessary clues needed to catch a problem.

Speed is another one of the reasons that AI-based data analytics far surpasses the manual equivalent. In the case of chargebacks, there is a six to eight week waiting period when done manually. With AI, analysis based on machine learning is done in real-time.

There are a few leaders in the field when it comes to combinatory AI approaches to fraud detection. Kount is one example of a software company that specializes in fraud detection and prevention.

This article was originally published on chartwestcott.com.