Why is it essential to consider "model fairness" and "algorithmic bias" when deploying machine learning models in a capstone project?

Why is it essential to consider "model fairness" and "algorithmic bias" when deploying machine learning models in a capstone project? Correct Answer Prevent biased or unfair outcomes

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Regarding bias and variance, which of the following statements are true? (Here 'high' and 'low' are relative to the ideal model.
i. Models which overfit are more likely to have high bias
ii. Models which overfit are more likely to have low bias
iii. Models which overfit are more likely to have high variance
iv. Models which overfit are more likely to have low variance
Regarding bias and variance, which of the following statements are true? (Here 'high' and 'low' are relative to the ideal model. (i) Models which overfit are more likely to have high bias (ii) Models which overfit are more likely to have low bias (iii) Models which overfit are more likely to have high variance (iv) Models which overfit are more likely to have low variance
What does "scalability" refer to in the context of deploying machine learning models in a capstone project?
In the following passage, some of the words have been left out. Read the passage carefully and select the correct answer for the given blank out of the four alternatives. A century later, the philosophy community needs to take __________ the new challenge of moral deliberation on the most pressing issues of our difficult times. We have to revoke the suspension of ethics and morality ____________ our lives by initiating a dialogue on it. Foregrounding ethics, however, ___________ means undermining the epistemological or ontological questions that are inevitably there. One such beginning can be made by anchoring moral ________________ in fairness and justice —one founded on obligatory fairness towards the people and the planet, towards the earth and the environment, and above all justice towards one_________. by anchoring moral ________________ in fairness
In a data science capstone project, why is "retraining" or "model maintenance" essential for deployed machine learning models?
Which cloud computing platform provides services such as Azure Machine Learning for building and deploying machine learning models?
Below are the two ensemble models: 1. E1(M1, M2, M3) and 2. E2(M4, M5, M6) Above, Mx is the individual base models. Which of the following are more likely to choose if following conditions for E1 and E2 are given? E1: Individual Models accuracies are high but models are of the same type or in another term less diverse E2: Individual Models accuracies are high but they are of different types in another term high diverse in nature
The Weberian model of bureaucracy has been criticised from a variety of stand points.
Consider the following points in this regard:
1. The Weberian model appears to be a 'machine theory' and a closed system model taking little account of organisational interactions with the environment.
2. The structural features of bureaucracy as laid down by Weber might be suitable for routine and repetitive tasks but these would produce dysfunctional consequences in terms of human behaviour if the jobs would involve innovation and creativity.
3. Weberian model has not been found to respond adequately to the tasks of socioeconomic transformation in the developing countries. These tasks require quick changes whereas the capacity of the Weberian model for adaptation to change is rather slow.
4. Weber's model is confined to the study of formal bureaucratic structure and fails to take notice of the informal structure of bureauracy such as informal relations, informal norms and values, an informal power hierarchy and informal power struggles.
Which of the above points make a valid criticism of the Weberian model? Select the correct answer from below:
Which of the following are correct statement(s) about stacking?
1. A machine learning model is trained on predictions of multiple machine learning models
2. A Logistic regression will definitely work better in the second stage as compared to other classification methods
3. First stage models are trained on full / partial feature space of training data.
Which phase of the capstone project involves selecting appropriate machine learning algorithms and models?