2.6 Systems Questionbank

Algorithmic recommendation systems

Algorithmic recommendation systems are used across a wide range of digital platforms, including YouTube, Spotify, Amazon, and dating applications. These systems analyze large volumes of user data such as viewing history, clicks, likes, and purchases in order to suggest content, products, or connections that are predicted to be relevant. Recommendations are continuously updated as users interact with the platform.

These systems consist of interconnected components, including users, algorithms, data inputs, and content providers. Changes in one part of the system can affect other parts, creating feedback loops that influence both individual behaviour and wider cultural trends. While recommendation systems are designed to improve user experience and engagement, they can also produce unintended consequences such as filter bubbles, bias amplification, and reduced diversity of content. This case highlights how algorithmic systems operate as complex and interdependent digital systems.

Case study: Virtual worlds and the construction of digital space

Virtual world platforms such as Minecraft, Second Life, and emerging metaverse environments allow users to create, organize, and inhabit digital spaces. These environments are often built using user-generated content and governed by social norms, community rules, and platform design choices. Virtual spaces serve a wide range of purposes, including education, business meetings, creative expression, and social interaction.

Unlike physical spaces, virtual environments are not constrained by geography, distance, or physical laws. Users can move instantly between locations, interact across global networks, and design spaces that reflect cultural values or social structures. However, access to virtual spaces depends on factors such as technology, cost, and digital literacy. As virtual worlds expand, questions arise about how space is organized, who controls access and movement, and how virtual environments operate at different scales from small communities to global platforms.

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.

* re-offending: committing another crime in the future

Source A

Source B

It is a paradox to brand a technological innovation as tainted goods by its very name. ‘Deepfake’ is a victim of its own capabilities. Negative connotations and recent incidents have pigeonholed this innovation in the taboo zone. The rise of deepfake technology has ushered in very interesting possibilities and challenges. This synthetic media, created through sophisticated artificial intelligence algorithms, has begun to infiltrate various sectors, raising intriguing questions about its potential impact on education and employability.

The dawn of deepfake technology introduces a realm of possibilities in education. Imagine medical students engaging in lifelike surgical simulations or language learners participating in authentic conversations. The potential for deepfake to revolutionise training scenarios is vast and could significantly enhance the educational experience. Beyond simulations, deepfake can transport students to historical events through realistic reenactments or facilitate virtual field trips, transcending the boundaries of traditional education. The immersive nature of deepfake content holds the promise of making learning more engaging and memorable.

Source C

However, while these potential abuses of the technology are real and concerning that doesn't mean we should turn a blind eye to the technology’s potential when using it responsibly, says Jaime Donally, a well-known immersive learning expert.

“Typically, when we're hearing about it, it's in terms of the negative – impersonation and giving false claims,” Donally says. “But really, the technology has the power of bringing people from history alive through old images that we have using AI.”

Donally, a former math teacher and instructional technologist, has written about how a type of deep fake technology called deep nostalgia technology that went viral in 2021 can allow students to form a stronger connection with the past and their personal family heritage. The technology, available on the MyHeritage app, allows images to uploaded that are then turned into short animations thanks to AI technology.

Here are some of the ways in which teachers can utilize deep fake technology in the classroom utilizing the MyHeritage app. Teachers have used the deep fake technology in the My Heritage app to bring historical figures such as Amelia Earhart and Albert Einstein to life. One teacher Donally has communicated with used an animation of Frederick Douglass (above) to help students connect with Douglass’ famous 1852 speech about the meaning of the Fourth of July to Black enslaved Americans. Another teacher has plans to use the app to have students interview a historical figure and create dialogue for them, then match the dialogue to the animation.

Donally herself has paired animations she's created with other types of immersive technology. “I layered it on top in augmented reality,” she says. “When you scan the photo of my grandfather, it all came to life. And it became something that was much more relevant to see in your real-world space.”

With proper supervision, students can use the technology to animate images of family members or local historical figures, and can experiment with augmented reality (AR) in the process. “It makes you want to learn more,” Donally says of animations created using deep fake technology. “It drives you into kind of the history and understanding a bit more, and I think it also helps you identify who you are in that process.”

Source D

Education platforms are harnessing deepfake technology to create AI tutors that provide customised support to students. Rather than a generic video lecture, each learner can get tailored instruction and feedback from a virtual tutor who speaks their language and adjusts to their level.

For example, Anthropic built Claude, an AI assistant designed specifically for education. Claude can answer students’ natural language questions, explain concepts clearly, and identify knowledge gaps.

Such AI tutors make learning more effective, accessible, and inclusive. Students feel like they have an expert guide helping them master new skills and material.

AI and deepfake technology have enormous potential to enhance workforce training and education in immersive new ways too. As Drew Rose, CSO and founder of cybersecurity firm Living Security, explains, “educators can leverage deepfakes to create immersive learning experiences. For instance, a history lesson might feature a ‘guest appearance’ by a historical figure, or a science lesson might have a renowned scientist explaining complex concepts.” Ivana Bartoletti, privacy and data protection expert at Wipro and author of An Artificial Revolution – On Power, Politics and AI envisions similar applications.

“Deepfake technologies could provide an easier and less expensive way to train and visualise,” she says. “Students of medicine and nursing currently train with animatronic robots. They are expensive and require special control rooms. Generative AI and augmented or virtual reality headsets or practice rooms will be cheaper and allow for the generalisation, if not the gamification, of simulation.”

Medical students could gain experience diagnosing and treating simulated patients, while business students could practice high-stakes scenarios like negotiations without real-world consequences. These immersive, gamified environments enabled by AI and deepfakes also have vast potential for corporate training.

Bartoletti notes, “A similar use case could be made for other types of learning that require risky and skill-based experiences. The Air Force uses AI as adversaries in flight simulators, and humans have not beaten the best AIs since 2015.

The Impact of Cloud Networks on Data Accessibility

Cloud networks allow for data storage and access over the internet, making data accessible from anywhere. This accessibility supports remote work, file sharing, and collaboration but also raises concerns about data security and control over personal information.

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.

Source A: Sensors Used in Autonomous Vehicles

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.

The Role of Firewalls in Network Security

Firewalls are critical for network security, acting as barriers between internal networks and external threats. They control incoming and outgoing traffic, protecting against unauthorized access and cyber attacks. However, configuring firewalls effectively can be challenging, especially in large organizations.