System: You are a helpful and precise assistant in checking the quality of the answer.
User: We would like to request your feedback on the performance of the response of the assistant to the user instruction displayed below. In the feedback, I want you to rate the quality of the response in these 3 categories according to each scoring rubric:

Commonsense Understanding: Is the model accurately interpreting world concepts for instructions that require a simulation of the expected result or necessitate commonsense or spatial reasoning?
Score 1: The model completely misinterprets world concepts or misunderstands commonsense knowledge.
Score 2: The model misinterprets crucial world concepts, potentially leading to misinformation.
Score 3: The model shows a few errors in its understanding of world concepts.
Score 4: A single, minor error exists in the model's comprehension of world concepts.
Score 5: The model accurately interprets world concepts without any errors.
Comprehension: Does the response fulfill the requirements of the instruction by providing relevant information especially when the instruction is complex and includes multiple requirements? This includes responding in accordance with the explicit and implicit purpose of given instruction.
Score 1: The response is completely unrelated to the instruction, or the model entirely misunderstands the instruction.
Score 2: Most of the key points in the response are irrelevant to the instruction, and the response misses major requirements of the instruction.
Score 3: Some major points in the response contain irrelevant information or miss some requirements of the instruction.
Score 4: The response is relevant to the instruction but misses minor requirements of the instruction.
Score 5: The response is perfectly relevant to the instruction, and the model fulfills all of the requirements of the instruction.
Factuality: Did the model extract pertinent and accurate background knowledge without any misinformation when factual knowledge retrieval is needed? Is the response supported by reliable evidence or citation of the source of its information?
Score 1: The model did not extract pertinent background knowledge and provided inaccurate or misleading information. There is no support for the response through reliable evidence or source citations.
Score 2: The model extracted some relevant background knowledge but included inaccuracies or incomplete information. The response has minimal support through evidence or citations, with questionable reliability.
Score 3: The model extracted generally accurate and pertinent background knowledge, with minor inaccuracies or omissions. The response is partially supported by evidence or citations, but the support may not be comprehensive or fully reliable.
Score 4: The model extracted mostly accurate and relevant background knowledge but missed minor evidence or citations to support the response.
Score 5: The model extracted complete and accurate background knowledge without any misinformation. The response is fully supported by reliable evidence or citations that are accurate, relevant, and comprehensive in addressing the instruction.
[Instruction]
Artificial intelligence (AI) is the ability of a computer or a robot to perform tasks that normally require human intelligence, such as reasoning, learning, and decision making. Do you know what kind of rules or principles are currently followed by AI systems or developers to ensure that AI is trustworthy, ethical, and beneficial for society?

[Ground truth Answer]
Yes, several organizations and researchers have proposed guidelines to ensure that AI is trustworthy, ethical, and beneficial for society. While there isn't a universal set of rules, some key principles that are commonly followed by AI developers and systems include:

1. Fairness: AI systems should be designed to minimize bias and prevent unfair discrimination. They should treat all individuals equitably and promote diversity and inclusivity.

2. Accountability: Developers should be responsible for the AI systems they create and should be held accountable for their impact on society. This requires a clear chain of responsibility and appropriate mechanisms for redress.

3. Transparency: AI systems should be designed to be open, explainable, and understandable. Users should know the purpose, capabilities, and limitations of the AI systems they interact with and how their information is being used.

4. Privacy and data protection: AI systems should respect users' privacy and protect their data. Data should be collected, stored, and used in a secure manner and in accordance with applicable laws and regulations.

5. Safety and security: AI systems should be developed to minimize the risk of harm or unintended consequences for users and society. Developers should prioritize robustness, reliability, and safety in the design, testing, and deployment of AI systems.

6. Human oversight: AI development should involve human input and oversight throughout the development process. This could include incorporating human monitoring of AI decisions or providing users with the ability to contest AI decisions.

7. Societal and environmental well-being: AI systems should take into account their impact on society and the environment, working towards improving overall well-being and sustainability.

8. Collaboration: AI developers should work together with stakeholders to share knowledge, establish best practices, and collaborate on the responsible development and deployment of AI systems.

These principles aim to ensure that AI is developed in a way that upholds human values, respects human rights, and benefits society as a whole. However, specific laws and regulations vary by country and organization.

[Assistant's Response]
Yes, there are several key principles that AI systems and developers follow to ensure that AI is trustworthy, ethical, and beneficial for society. Here are some of the most important ones:

1. **Transparency**: AI systems should be designed to be understandable and explainable, allowing users to grasp how decisions are made and what data is used.

2. **Fairness**: Developers aim to eliminate biases in AI algorithms to ensure that the technology treats all individuals and groups equitably, avoiding discrimination.

3. **Accountability**: There should be clear lines of responsibility for the outcomes of AI systems, ensuring that developers and organizations are held accountable for their actions and the impacts of their technologies.

4. **Privacy**: Protecting user data is a priority, and AI systems should handle personal information responsibly, ensuring that privacy is maintained.

5. **Safety and Security**: AI systems should be designed to operate safely and securely, minimizing risks to users and society as a whole.

6. **Beneficence**: The development of AI should aim to promote positive outcomes and enhance human well-being, ensuring that technology serves the greater good.

7. **Collaboration**: Engaging with diverse stakeholders, including ethicists, policymakers, and the public, helps ensure that AI development aligns with societal values and needs.

These principles guide the responsible development and deployment of AI technologies, fostering trust and ensuring that they contribute positively to society.

[The End of Assistant's Response]

Please give feedback on the assistant's responses. Also, provide the assistant with a score on a scale of 1 to 5 for each category, where a higher score indicates better overall performance. Make sure to give feedback or comments for each category first and then write the score for each category. Only write the feedback corresponding to the scoring rubric for each category. The scores of each category should be orthogonal, indicating that 'Efficiency of User Alignment' should not be considered for 'Readability of User Alignment' category, for example. 

Lastly, return a Python dictionary object that has skillset names as keys and the corresponding scores as values.

[System]



Assistant: