Practice IB Digital Society (DS) Topic 3.2 Algorithms with authentic exam-style questions for both SL and HL students. This question bank focuses on the exact syllabus content for 3.2 Algorithms and mirrors Paper 1, 2, 3 style where relevant.
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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
Identify two characteristics of artificial intelligence (AI) systems.
Outline one problem that may arise if proprietary software rather than open-source software is used to develop algorithms.
The developers of the AI software decided to use supervised machine learning to develop the algorithms in the sentencing software.
Identify two advantages of using supervised learning.
The developers of the AI software used visualizations as part of the development process.
Explain one reason why visualizations would be used as part of the development process.
Explain two problems the developers of the AI system could encounter when gathering the data that will be input into the AI system.
To what extent should the decisions of judges be based on algorithms rather than their knowledge and experience?
Define the term “finite” in the context of algorithms.
Identify two reasons why an algorithm should have well-defined inputs and outputs.
Explain why an algorithm must be unambiguous to function correctly.
Describe one example where the feasibility of an algorithm impacts its use in a real-world application.
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.
In criminal justice, "black box" algorithms are increasingly used to make decisions about bail, parole, and sentencing. However, the lack of transparency and potential for bias raise serious ethical concerns about fairness and accountability.
Evaluate the challenges of implementing algorithmic transparency and accountability in criminal justice, particularly with “black box” algorithms.
Facial recognition algorithms, used for security in airports, rely on large datasets and are sometimes criticized for algorithmic bias. For instance, these algorithms have been known to misidentify individuals of certain racial backgrounds, raising fairness and transparency issues.
Identify two issues related to algorithmic bias in facial recognition software.
Explain why transparency is essential for accountability in facial recognition algorithms used in security.
Discuss one risk associated with “black box” algorithms in facial recognition systems.
Evaluate the impact of algorithmic bias on fairness in facial recognition, particularly concerning racial and ethnic disparities.