What risk do black-box algorithms pose when supporting critical HR decisions?Question 1 options: They always optimize HR processes. They remove all human involvement in decision-making. They ensure absolute fairness. They can entrench and automate biases.Question 2 (1 point) Which step is NOT recommended to address algorithmic bias?Question 2 options: Asking the right questions of the data. Taking affirmative steps to address potential biases. Being aware of the issues. Trusting models that don't include demographic data to be unbiased.Question 3 (1 point) In the context of People Analytics, what issue can arise with hiring decisions based on "cultural fit"?Question 3 options: More effective team dynamics. Perpetuation of demographic imbalances. Improved company culture. Higher employee retention rates.Question 4 (1 point) What is unintended feedback bias?Question 4 options: A cycle where today's biased decisions influence future models Data-driven processes without human involvement Biased employee feedback Intentionally inserting biases into analytics Question 5 (1 point) Why is there no truly "race-blind" or "gender-blind" model? Question 5 options: Because demographics are always included. Features used may still correlate with demographic characteristics. It's technically impossible to create. All models favor one demographic over another.Question 6 (1 point) What happens when algorithms simply reinforce the biased status quo? Question 6 options: They give biases a veneer of objectivity. They promote unbiased hiring. They challenge biased decisions. They increase diversity.Question 7 (1 point) In the example, what outcome was being modeled?Question 7 options: Job application rates. Employee promotion history. Employee satisfaction. Employee attrition rates.Question 8 (1 point) Where was the data for the "Empirical Example" sourced from?Question 8 options: Private analytics companies. Freedom of Information Act request. People Analytics vendors. Department of Defense.Question 9 (1 point) What is the primary purpose of People Analytics models today?Question 9 options: Using past employee data to make decisions. Training law enforcement. Understanding arrest data. Predicting weather patterns.Question 10 (1 point) What are the breakdowns in analytical processes a result of?Question 10 options: Always relying on machines. Over-relying on human decision-making. Understanding root causes of algorithmic bias. Ignoring data entirely.Question 11 (1 point) What is the main goal of People Analytics in the future?Question 11 options: To entirely eliminate the role of humans in decision-making. To make all models fully automated. To achieve high-earning workplaces. To build high-achieving, equitable workplaces.Question 12 (1 point) What causes might contribute to algorithmic bias in people analytics?Question 12 options: Fast processing speeds. Overrepresentation of key groups in the data. Too much data. Biased retrospective data.Question 13 (1 point) What step is not part of the decision support tool process?Question 13 options: Predictions are used as bases for decision-making. Data is manually entered by users. Data is cleaned and transformed. The process starts with raw data.Question 14 (1 point) What is essential in the fight against biased People Analytics tools?Question 14 options: Understanding underlying biases and human-aided analytics. Ignoring potential biases. Implementing all external analytical tools without questions. Using only automated models.Question 15 (1 point) What potential revolutionizing impact does People Analytics have on HR?Question 15 options: Reducing the importance of hiring. Ensuring compensation is random. Decreasing the number of high-performing workers. Identifying skill gaps and optimizing workforce training.
What risk do black-box algorithms pose when supporting critical HR decisions?
Question 1 options:
They always optimize HR processes.
They remove all human involvement in decision-making.
They ensure absolute fairness.
They can entrench and automate biases.
Question 2 (1 point)
Which step is NOT recommended to address algorithmic bias?
Question 2 options:
Asking the right questions of the data.
Taking affirmative steps to address potential biases.
Being aware of the issues.
Trusting models that don't include demographic data to be unbiased.
Question 3 (1 point)
In the context of People Analytics, what issue can arise with hiring decisions based on "cultural fit"?
Question 3 options:
More effective team dynamics.
Perpetuation of demographic imbalances.
Improved company culture.
Higher employee retention rates.
Question 4 (1 point)
What is unintended feedback bias?
Question 4 options:
A cycle where today's biased decisions influence future models
Data-driven processes without human involvement
Biased employee feedback
Intentionally inserting biases into analytics
Question 5 (1 point)
Why is there no truly "race-blind" or "gender-blind" model?
Question 5 options:
Because demographics are always included.
Features used may still correlate with demographic characteristics.
It's technically impossible to create.
All models favor one demographic over another.
Question 6 (1 point)
What happens when algorithms simply reinforce the biased status quo?
Question 6 options:
They give biases a veneer of objectivity.
They promote unbiased hiring.
They challenge biased decisions.
They increase diversity.
Question 7 (1 point)
In the example, what outcome was being modeled?
Question 7 options:
Job application rates.
Employee promotion history.
Employee satisfaction.
Employee attrition rates.
Question 8 (1 point)
Where was the data for the "Empirical Example" sourced from?
Question 8 options:
Private analytics companies.
Freedom of Information Act request.
People Analytics vendors.
Department of Defense.
Question 9 (1 point)
What is the primary purpose of People Analytics models today?
Question 9 options:
Using past employee data to make decisions.
Training law enforcement.
Understanding arrest data.
Predicting weather patterns.
Question 10 (1 point)
What are the breakdowns in analytical processes a result of?
Question 10 options:
Always relying on machines.
Over-relying on human decision-making.
Understanding root causes of algorithmic bias.
Ignoring data entirely.
Question 11 (1 point)
What is the main goal of People Analytics in the future?
Question 11 options:
To entirely eliminate the role of humans in decision-making.
To make all models fully automated.
To achieve high-earning workplaces.
To build high-achieving, equitable workplaces.
Question 12 (1 point)
What causes might contribute to algorithmic bias in people analytics?
Question 12 options:
Fast processing speeds.
Overrepresentation of key groups in the data.
Too much data.
Biased retrospective data.
Question 13 (1 point)
What step is not part of the decision support tool process?
Question 13 options:
Predictions are used as bases for decision-making.
Data is manually entered by users.
Data is cleaned and transformed.
The process starts with raw data.
Question 14 (1 point)
What is essential in the fight against biased People Analytics tools?
Question 14 options:
Understanding underlying biases and human-aided analytics.
Ignoring potential biases.
Implementing all external analytical tools without questions.
Using only automated models.
Question 15 (1 point)
What potential revolutionizing impact does People Analytics have on HR?
Question 15 options:
Reducing the importance of hiring.
Ensuring compensation is random.
Decreasing the number of high-performing workers.
Identifying skill gaps and optimizing workforce training.
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