All Flashcards
How is bias manifested in criminal risk assessment tools?
Historical data reflects societal biases, leading to disproportionate flagging of specific groups.
How is bias manifested in facial recognition systems?
Systems trained on datasets lacking diversity may not accurately recognize women or minorities.
How is bias manifested in recruiting algorithms?
Algorithms may learn to prefer candidates similar to past successful candidates, perpetuating existing imbalances.
How can diverse data sets be applied in facial recognition?
Training facial recognition systems with images from various demographic groups to improve accuracy across all users.
How can fairness metrics be applied in loan applications?
Using metrics to ensure loan approval rates are similar across different demographic groups, preventing discrimination.
How can algorithm reviews be applied in AI-powered healthcare?
Regularly checking algorithms used for diagnosis to ensure they don't disproportionately misdiagnose certain groups.
How can addressing human bias be applied in software development?
Training development teams to recognize and mitigate their own biases, leading to more inclusive software designs.
How can increasing tech diversity be applied in product design?
Having diverse teams create products that cater to a wider range of user needs and preferences.
How can bias be identified in a search engine algorithm?
Analyzing search results to see if they disproportionately favor certain viewpoints or demographics.
How can bias in a chatbot be identified?
Testing the chatbot with various prompts to see if it responds differently based on user demographics.
What are the steps to mitigate bias in a machine learning model?
Gather diverse data, review the algorithm, incorporate fairness metrics, address human bias, increase tech diversity.
What are the steps in identifying bias in an algorithm?
Define the problem, collect data, analyze the data for skews, test the algorithm with different groups, evaluate outcomes.
What are the steps in ensuring fairness in AI-powered hiring?
Use diverse data, audit the algorithm, monitor outcomes, involve diverse stakeholders, address human bias.
What are the steps in creating a diverse dataset?
Identify target demographics, gather data from diverse sources, clean and preprocess the data, balance representation, validate the dataset.
What are the steps in performing an algorithm review for bias?
Understand the algorithm's logic, identify potential bias points, test with diverse data, analyze outcomes, adjust the algorithm.
What are the steps in incorporating fairness metrics?
Define fairness goals, select appropriate metrics, integrate metrics into the algorithm, monitor performance, adjust as needed.
What are the steps in addressing human bias in tech?
Raise awareness, provide training, establish guidelines, promote diverse teams, encourage feedback.
What are the steps in increasing tech diversity?
Promote STEM education, offer scholarships, create inclusive workplaces, support minority-owned businesses, mentor diverse talent.
What are the steps in validating a dataset for representativeness?
Compare data distribution to population demographics, use statistical tests, consult with experts, iterate on data collection, document findings.
What are the steps in monitoring algorithm outcomes for bias?
Track performance metrics across groups, analyze disparities, investigate causes, adjust the algorithm, continuously monitor.
Why do computing innovations often reflect existing biases?
They use data from the world, which is already influenced by human perspectives.
How can diverse datasets help prevent bias?
They reduce the risk of skewed results by representing the entire population.
Why is it important to review algorithms for potential biases?
Helps catch and fix biases early on, ensuring fairer outcomes.
Why is it important to address human bias in tech development?
Human bias can easily creep into the design process, influencing outcomes.
Why is diversity important in the tech industry?
A wider range of perspectives leads to less biased and more inclusive systems.
What is the impact of biased criminal risk assessment tools?
Can lead to unfair judicial decisions, disproportionately affecting certain groups.
What is the impact of biased facial recognition systems?
Can result in misidentification or exclusion, especially for underrepresented groups.
What is the impact of biased recruiting algorithms?
Can perpetuate gender and racial imbalances in the workforce.
Why is it important to use fairness metrics?
Ensures the system doesn't discriminate and promotes equitable outcomes.
What is the role of historical data in creating bias?
Historical data often reflects existing societal biases, which can be perpetuated by algorithms.