Sentencing criminals using artificial intelligence (AI)

In 10 states in the United States, artificial intelligence (AI) software is used for sentencing criminals. Once criminals are found guilty, judges need to determine the lengths of their prison sentences. One factor used by judges is the likelihood of the criminal re-offending*.

The AI software uses machine learning to determine how likely it is that a criminal will re-offend. This result is presented as a percentage; for example, the criminal has a 90 % chance of re-offending. Research has indicated that AI software is often, but not always, more reliable than human judges in predicting who is likely to re-offend.

There is general support for identifying people who are unlikely to re-offend, as they do not need to be sent to prisons that are already overcrowded.

Recently, Eric Loomis was sentenced by the state of Wisconsin using proprietary AI software. Eric had to answer over 100 questions to provide the AI software with enough information for it to decide the length of his sentence. When Eric was given a six-year sentence, he appealed and wanted to see the algorithms that led to this sentence. Eric lost the appeal.

On the other hand, the European Union (EU) has passed a law that allows citizens to challenge decisions made by algorithms in the criminal justice system.

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* re-offending: committing another crime in the future

Students should be provided with the pre-release document ahead of the May 2018 HL paper 3 examination, this can be found under the 'Your tests' tab > supplemental materials > May 2018 HL paper 3 pre-release document: Accessibility.

Improving the accessibility to the curriculum for children with special educational needs and disabilities (SEND)

Source 1: Tayton School

Tayton School is a primary school that teaches 500 children aged between 5 and 12. There are three classes in each year group, with a maximum of 24 students in each class. The school’s motto is “Education for Everyone”, and inclusion is at the heart of the school’s mission.

The school’s Inclusion Department consists of five full-time staff, led by Sandra, and 10 learning support assistants who are active in working with the children. Sandra has recently produced a report on the students with special educational needs and disabilities (SEND) in the school, in which she found that the increasing numbers of students, and the types of SEND, means that the schools needs to invest in expanding the amount of support for the students (see Table 1).

Table 1: SEND at Tayton School

Sandra’s report argues that, next year, the work of the Inclusion Department would be more effective if the school purchased educational digital technologies, such as social robots and assistive technologies.

Source 2: Social robots in education

Sandra researched social robots and came back to the department meeting with this information:

In 2020, a report on the use of social robots in education was published by a prestigious university professor, who concluded that social robots have the potential to be a key player in education in the way textbooks and whiteboards have been in the past. A social robot has the potential to support students in ways that could never have been envisaged 20 years ago. However, there are significant technical limitations, particularly linked to the social robot’s ability to interact with students, that will restrict their usability for the next few years

Source 3: Mary sees the positives

Mary, one of the learning assistants at Tayton School, says:

“As a parent of two school-age children, I think the potential introduction of social robots has both advantages and disadvantages. My children thought the idea of having a robot that sits with them very exciting, and I think they would do what the robot asks without questioning it. The robot will also be much more patient while they are learning their times tables!” (See Figure 1).

Figure 1: Students interacting with a social robot

[Source: pexels.com]

Source 4: James has doubts

James, another learning assistant at Tayton School, is wary of the overuse of digital technology in schools for children with special needs based on his experiences in other schools. He has found some research that supports his ideas.

[Source: pexels.com]

The role of portable digital devices in health

Jaime is an athlete and uses his sports watch to monitor his training sessions. He also uses it to keep a record of his health and well-being. The sports watch can monitor Jaime’s vital signs. It is also global positioning systems (GPS) enabled, so it can track his location (see Figure 4).

Figure 4: Data collected by a sports watch

The information that is recorded by Jaime’s sports watch is synchronized with a mobile application (app) installed on his cellphone/mobile phone.

Wildfire modelling

The fire control centre in the Kinakora National Park in New Zealand often has to cope with the natural phenomenon of wildfires. Staff have been collecting data about wildfires since 1970.

The size of each wildfire is measured, and the vegetation types affected are recorded. Data on the weather conditions is collected from sensors in the park. The staff at the fire control centre use this information to fight the wildfire.

A new computer modelling system is being developed using data collected from previous wildfires. This new system will improve the quality of the information available when fighting future wildfires.

The new system will also enable staff at Kinakora National Park to send information to tourists in the park to warn them when they are in danger from a wildfire.

Cameras in school

The principal at Flynn School has received requests from parents saying that they would like to monitor their children’s performance in school more closely. He is considering extending the school’s IT system by installing cameras linked to facial recognition software that can record student behaviour in lessons.

The facial recognition software can determine a student’s attention level and behaviour, such as identifying if they are listening, answering questions, talking with other students, or sleeping. The software uses machine learning to analyse each student’s behaviour and gives them a weekly score that is automatically emailed to their parents.

The principal claims that monitoring students’ behaviour more closely will improve the teaching and learning that takes place.

Discuss whether Flynn School should introduce a facial recognition system that uses machine learning to analyse each student’s behaviour and give them a score that is automatically emailed to their parents.

Online learning

TailorEd is a free online learning system that personalizes students’ learning by providing teachers with data about how students are progressing in their courses. Students create a personal profile and work through the assignments at their own pace. Teachers can log in to the learning system to see how the students are progressing. However, concerns have been expressed about the amount of data that is being collected.

The school has found that when students access the course platform, some content is being blocked. The network administrator has been asked to investigate the situation. Teachers believe that it would be more appropriate to train the students to use the platform responsibly, rather than use technology to block their access to certain websites.

Can digital technologies be used sustainably?

Many organizations claim that the most efficient use of information technology (IT) equipment, such as laptops and printers, is to replace them on a regular basis. For example, an organization’s strategy may be to do this every three years.

Other organizations purchase IT equipment that can easily be upgraded by increasing the storage and memory or upgrading the processing capabilities only when required. They claim they do not need to replace their IT equipment on such a regular basis and believe this is a more sustainable practice.

Evaluate the sustainability of these two strategies.

Airport luggage control

Large airports need to handle thousands of pieces of luggage (including suitcases and other types of baggage) from the moment passengers check them in at the counter until the moment they arrive at their final destination. Sometimes a passenger will change airplanes during their journey, so their bags will need to be transferred by conveyor belt from one plane to another.

When the passenger checks in at the airline counter, a tag is printed and attached to each piece of luggage (see Figure 2). This tag has information about the passenger and their journey printed on it and also shows both a barcode and a ten-digit number that are unique to each piece of luggage.

Figure 2: A luggage tag

[Image by AlexStef at English Wikipedia. Dca-baggage-tag.jpg (https://commons.wikimedia.org/wiki/File:Dca-baggage-tag.jpg). Under copyright and licensed under a Creative Commons Attribution 3.0 International License, https://creativecommons.org/licenses/by-sa/3.0/deed.en]

The luggage then goes on to a number of conveyor belts that take each bag to where it needs to go. Conveyor belts connect to other conveyor belts that direct luggage from the airport building to the correct airplane, from one airplane to the next if the passenger changes airplanes during the journey, or to the baggage reclaim area at the end of the journey. The airport luggage control system will know when to push the bag from one conveyor belt to another to ensure it gets to the correct destination.

The barcode allows the airport’s luggage control system to access a database containing information about each piece of luggage.

Using a Segway with machine learning capabilities?

The Segway Patroller is a two-wheeled, battery-powered electric vehicle. Recently, Segway Patrollers have been used for security purposes in cities as well as in public spaces such as concerts, railway stations and shopping malls.

The Segway Patroller can travel up to a speed of 20 kilometres per hour (about 12 miles per hour) and travel about 40 kilometres (25 miles) in distance before the battery needs to be recharged.

Figure 3: A Segway Patroller

[Copyright: Urban Mobility GmbH – fromhttps://en.wikipedia.org/wiki/File:Segway_Polizei_4.jpg]

Each Segway Patroller can be customized by adding the following features.

• A global positioning system (GPS)-based navigation system
• Machine learning capabilities that include speech, image and pattern recognition

The managers at Oliverstadt Station claim the introduction of upgraded Segways that have a GPS navigation system and machine learning capabilities would lead to improvements in the customer service provided.

Discuss whether the Segway Patrollers at Oliverstadt Station should be upgraded to include machine learning capabilities.

Artificial intelligence (AI) predicts earthquakes

Scientists at universities are developing a machine learning system to detect the warning signs that an earthquake is likely to occur. Using pattern-recognition algorithms similar to those in image and speech recognition, the system would be able to predict earthquakes a few days before they occur. Using machine learning, researchers will be able to run earthquake analysis 500 times faster than they could previously.

It is also possible to use machine learning to predict where the aftershocks* of an earthquake may occur (see Figure 4).

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Figure 4: Prediction of aftershocks using machine learning**

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* aftershock: a smaller earthquake that follows a large earthquake