How might legal responsibility influence AI development?
학생이 쓴 원문
Legal responsibility influence pretty much AI development. First, companies should focus on stability and reliability. To avoid legal responsibility, they try to decrease error probability and make strict test process. Especially, in medical and auto-driving field, a very small mistake can causes huge legal problem. Also, data management and preserving privacy is strengthened. Abuse of data and privacy violation is directly linked with legal response, so they have to keep the rule during data collection and processing time.
강사 첨삭
Happy Midweek, Mary! You showed strong understanding of how legal responsibility affects AI development, especially in areas like safety, testing, and data privacy. Your examples from medical and autonomous driving fields make your answer more concrete and relevant. To improve, focus on subject-verb agreement (influences, are strengthened) and using more precise terms like legal risks and regulations. Overall, your explanation is logical, clear, and technically insightful — great job!
-Teacher Wela
Legal responsibility influence pretty much AI development.
>> Legal responsibility greatly influences AI development.
To avoid legal responsibility, they try to decrease error probability and make strict test process.
>> To avoid legal risks, they try to reduce the probability of errors and implement strict testing processes.
Especially, in medical and auto-driving field, a very small mistake can causes huge legal problem.
>> Especially in the medical and autonomous driving fields, even a very small mistake can cause serious legal problems.
Also, data management and preserving privacy is strengthened.
>> Also, data management and privacy protection are strengthened.
Abuse of data and privacy violation is directly linked with legal response, so they have to keep the rule during data collection and processing time.
>> The misuse of data and privacy violations are directly linked to legal consequences, so companies must follow regulations during data collection and processing.
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