C.3: Ethical Impact(s)/Issue(s). For each OKR discuss the ethical impact/issues that can arise related to this outcome for your entrepreneur startup. Example: you are developing technology to monitor at-home stroke patients and you are using cameras and machine learning to monitor the patient's facial changes. EX1: One potential OKR would be to achieve 99% accuracy with the machine learning model. What are the ethical impacts -one would be that the data collected was too narrow or simple to achieve higher accuracy. For instance, the data may be biased and only taken from young subjects. This is unethical as it could mean in use that older subjects will not have the same accuracy as what is reported. EX2:Another OKR could be to have 80% user acceptance based on the average score of a user survey given to 100 customers. This could be interpreted as having the potential ethical issue of not collecting enough customer data or again some kind of bias in the selected customers (say customers with technical backgrounds that are comfortable with using technical systems) which could lead to skewed metric data
C.3: Ethical Impact(s)/Issue(s). For each OKR discuss the ethical impact/issues that can arise related to this outcome for your entrepreneur startup. Example: you are developing technology to monitor at-home stroke patients and you are using cameras and machine learning to monitor the patient's facial changes. EX1: One potential OKR would be to achieve 99% accuracy with the machine learning model. What are the ethical impacts -one would be that the data collected was too narrow or simple to achieve higher accuracy. For instance, the data may be biased and only taken from young subjects. This is unethical as it could mean in use that older subjects will not have the same accuracy as what is reported. EX2:Another OKR could be to have 80% user acceptance based on the average score of a user survey given to 100 customers. This could be interpreted as having the potential ethical issue of not collecting enough customer data or again some kind of bias in the selected customers (say customers with technical backgrounds that are comfortable with using technical systems) which could lead to skewed metric data