Question

You are a technical manager for a large city courthouse. The judges have asked you to implement a new system that will make criminal sentencing recommendations. As part of your testing, your team has the system make sentencing recommendations for past court convictions. Your team finds that the new system is much more likely to recommend longer sentences for some groups of people. What is the Main Ethical Challenge with implementing this system?

A. Impartial judges should make sentencing recommendations. AI systems should not be involved.
B. The courthouse obviously does not have the technical expertise to improve the system.
C. The city courthouse might not be able to afford the service.
D. It magnifies existing biases rather than mitigating them.

Answer

D. It magnifies existing biases rather than mitigating them.

Explanation 1

The correct answer to the question is D. It magnifies existing biases rather than mitigating them. This is because the system is using historical data that reflects the existing disparities and prejudices in the Criminal Justice system. The system is not able to account for the social, economic, and cultural factors that might influence the outcomes of past court convictions. Therefore, the system is likely to reinforce and amplify the existing inequalities and injustices in the sentencing process.

A comprehensive explanation of the answer would include the following points:

– The system is an example of generative AI, which is a type of artificial intelligence that can create new content or data based on existing data. Generative AI can be used for various purposes, such as generating text, images, music, or recommendations.
– However, generative AI also faces many ethical challenges, such as ensuring the quality, accuracy, fairness, and transparency of the generated content or data. One of the main ethical principles for generative AI is to avoid harm and promote well-being for all stakeholders involved.
– In this case, the system is generating sentencing recommendations based on past court convictions. However, past court convictions are not a reliable or fair source of data, because they are influenced by many factors that are not related to the actual crime or the defendant’s circumstances. For example, past court convictions might reflect the biases of judges, juries, prosecutors, defense attorneys, witnesses, media, or public opinion. These biases might be based on factors such as race, gender, ethnicity, religion, socioeconomic status, education level, or geographic location.
– Therefore, the system is not able to capture the complexity and diversity of human situations and contexts. The system is not able to consider the mitigating or aggravating circumstances that might affect the severity of the crime or the appropriateness of the sentence. The system is not able to account for the potential impacts of the sentence on the defendant’s rehabilitation, reintegration, or recidivism.
– As a result, the system is likely to generate sentencing recommendations that are disproportionate, unfair, and discriminatory for some groups of people. The system is likely to recommend longer sentences for people who belong to marginalized or disadvantaged groups, such as racial minorities, low-income individuals, or immigrants. The system is likely to recommend shorter sentences for people who belong to privileged or dominant groups, such as white people, wealthy individuals, or citizens.
– This would violate the ethical principle of fairness and justice, which requires that generative AI systems should treat all people equally and impartially. It would also violate the ethical principle of human dignity and autonomy, which requires that generative AI systems should respect and protect the rights and freedoms of all people. It would also violate the ethical principle of social responsibility and accountability, which requires that generative AI systems should contribute to the common good and be subject to oversight and regulation.
– Therefore, implementing this system would pose a serious ethical challenge for the city courthouse. It would undermine the trust and legitimacy of the criminal justice system. It would harm the well-being and dignity of the defendants and their families. It would exacerbate the existing social problems and conflicts in the city.

Explanation 2

Explanation 3

The correct answer is D. It magnifies existing biases rather than mitigating them.

• AI systems are not neutral or objective. They are based on data that may reflect historical or social biases, such as racial, gender, or class discrimination. For example, if the data used to train the system comes from past court convictions that were influenced by human prejudices, the system will learn and reproduce those prejudices in its recommendations.
• AI systems are not always transparent or explainable. They may use complex mathematical models or algorithms that are difficult to understand or interpret by humans. This makes it hard to scrutinize or challenge the system’s decisions, especially for the defendants or their lawyers. It also raises questions about the accountability and responsibility of the system’s developers, users, and overseers.
• AI systems may violate human rights and fundamental freedoms. They may infringe on the right to a fair trial, the right to privacy, the right to equality and non-discrimination, and the right to dignity and autonomy of the defendants. They may also undermine the rule of law and the trust in the justice system.

These ethical challenges have been recognized and discussed by various scholars, experts, and organizations. Some countries have also experimented with or implemented AI systems in criminal justice settings, such as Malaysia, but they have faced criticism and controversy from lawyers, civil society groups, and human rights defenders.

Therefore, implementing such a system in a large city courthouse would pose serious ethical risks and challenges that need to be carefully addressed and regulated.

Explanation 4

The main ethical challenge with implementing AI systems for criminal sentencing recommendations is that it can magnify existing biases rather than mitigating them. Bias can be introduced into AI in many different ways. If the AI is a neural network, it must be fed training data. The training data can be biased if it is not representative of the population.

Explanation 5

Explanation 6

The main ethical challenge with implementing an AI system that makes criminal sentencing recommendations is that it can magnify existing biases rather than mitigating them. Bias can be introduced into AI in many different ways. If the AI is a neural network, it must be fed training data. The data used to train the AI system may contain biases that reflect past discrimination or prejudice.

Explanation 7

The correct answer is D. It magnifies existing biases rather than mitigating them.

AI ethics are the principles and values that guide the development and use of AI systems to ensure that they are beneficial, trustworthy, and respectful of human dignity and rights. AI ethics are important because AI systems can have significant impacts on individuals, societies, and the environment, both positive and negative.

One of the main ethical challenges in AI is the problem of bias and discrimination, which means that AI systems may produce unfair or harmful outcomes for certain groups of people based on their characteristics, such as race, gender, age, or disability. Bias and discrimination in AI can arise from various sources, such as:

• The data used to train or test the AI system, which may reflect historical or social biases or inequalities that exist in our society.
• The design or implementation of the AI system, which may introduce or amplify biases due to human choices, assumptions, or errors.
• The use or deployment of the AI system, which may result in biases due to inappropriate or malicious actions by users, operators, or adversaries.

Bias and discrimination in AI can have serious consequences for human rights, justice, and well-being, such as:

• Violating the right to equality and non-discrimination, which is a fundamental principle of human dignity and a cornerstone of international human rights law.
• Undermining the trust and confidence in the AI system and its creators or providers, which can affect the adoption and acceptance of the technology by the public and the stakeholders.
• Reducing the quality and accuracy of the AI system and its outputs, which can affect its performance and usefulness for its intended purposes.

In this scenario, you are a technical manager for a large city courthouse. The judges have asked you to implement a new system that will make criminal sentencing recommendations. As part of your testing, your team has the system make sentencing recommendations for past court convictions. Your team finds that the new system is much more likely to recommend longer sentences for some groups of people.

The main ethical challenge with implementing this system is that it magnifies existing biases rather than mitigating them. This means that:

• The system is trained on data that reflects the biases and inequalities that exist in the criminal justice system, such as racial disparities, socioeconomic factors, or judicial discretion.
• The system produces recommendations that are unfair or discriminatory for certain groups of people based on their characteristics, such as race, gender, age, or disability.
• The system affects the human rights and well-being of those people who are subject to its recommendations, such as their right to a fair trial, their right to liberty and security, or their right to rehabilitation.

Therefore, you should be careful not to implement this system without addressing its ethical issues and ensuring that it is fair, transparent, and accountable.

Explanation 8

Explanation 9

The correct answer to the question is D. K-nearest neighbor. Here’s a detailed explanation to elaborate on this answer:

In the described scenario, the online movie-streaming business aims to create an artificial neural network for movie recommendations based on customers’ viewing history. The team uses XY diagrams representing different film genres, where the film rating is plotted on the X-axis and the duration people watch on the Y-axis. Recommendations are then made based on the proximity of movies to each other on the chart. The machine learning algorithm used in this case is:

D. K-nearest neighbor (KNN): K-nearest neighbor is a supervised learning algorithm used for both classification and regression tasks. It operates based on the principle that similar data points tend to have similar outcomes. In the context of movie recommendations, KNN can be employed to identify movies that are similar to each other based on their attributes, such as film rating and duration watched.

The team’s approach of creating XY diagrams with film rating on the X-axis and duration on the Y-axis suggests that the attributes (film rating and duration) are being used as the input features for the KNN algorithm. Each movie is represented as a point on the chart based on its attributes, and movies that are close to each other on the chart are considered similar.

To make recommendations, the KNN algorithm calculates the distances between the new movie (for recommendation) and the movies on the chart. It then selects the k-nearest neighbors based on proximity and suggests movies that are similar to the ones the customer has already seen. The recommendation is based on the movies’ proximity in the attribute space defined by the film rating and duration.

Options A, B, and C are incorrect because they do not align with the described scenario:

A. Naive Bayes: Naive Bayes is a probabilistic classification algorithm and is not directly applicable to the described scenario of movie recommendations based on movie attributes and proximity.

B. Reinforcement learning: Reinforcement learning involves training an agent to make decisions or take actions in an environment to maximize rewards. While reinforcement learning can be used for recommendation systems, the approach described in the scenario, based on movie attributes and proximity, does not align with reinforcement learning.

C. Q learning: Q learning is a specific algorithm within reinforcement learning that involves learning an optimal action-selection policy in a Markov decision process. Similar to reinforcement learning, the Q learning approach is not directly applicable to the movie recommendation scenario described.

In summary, the team in the described scenario uses the K-nearest neighbor (KNN) algorithm to recommend movies to customers based on attributes such as film rating and duration watched. By identifying movies that are close to each other on the XY diagram, the KNN algorithm suggests movies that are similar to the ones the customer has already seen. KNN leverages the proximity of movies in the attribute space to provide personalized recommendations.

Reference

• Criminal courts’ artificial intelligence: the way it reinforces bias and discrimination | SpringerLink
• Criminal justice, artificial intelligence systems, and human rights | SpringerLink
• Artificial Intelligence: examples of ethical dilemmas | UNESCO
• Malaysia tests AI court sentencing despite ethical concerns raised by lawyers – Tech (mashable.com)
• Artificial Intelligence (AI) & Criminal Justice System: How Do They Work Together? (pixelplex.io)
• Ethical concerns mount as AI takes bigger decision-making role – Harvard Gazette
• As Malaysia tests AI court sentencing, some lawyers fear for justice | The Star
• The Ethics of AI: Navigating the Challenges of Bias and Fairness (linkedin.com)
• AI Ethicist & AI Bias | Deloitte US
• Ethical concerns mount as AI takes bigger decision-making role – Harvard Gazette

The latest Generative AI Skills Initiative certificate program actual real practice exam question and answer (Q&A) dumps are available free, helpful to pass the Generative AI Skills Initiative certificate exam and earn Generative AI Skills Initiative certification.

The post Generative AI Certificate Q&A: Main ethical challenge with implementing system to make criminal sentencing recommendations? appeared first on PUPUWEB - Information Resource for Emerging Technology Trends and Cybersecurity.