The following pages link to (Q4586629):
Displaying 22 items.
- Can statistics predict the Fields Medal winners? (Q722131) (← links)
- Statistical science in the world of big data (Q1642375) (← links)
- Adapting a kidney exchange algorithm to align with human values (Q1989404) (← links)
- Robustness and approximation for the linear contract design (Q2039672) (← links)
- Commonsense explanations of sparsity, Zipf law, and Nash's bargaining solution (Q2086143) (← links)
- An approach for combining ethical principles with public opinion to guide public policy (Q2211866) (← links)
- History of mathematics: a global cultural approach. Abstracts from the workshop held December 13--19, 2020 (online meeting) (Q2232324) (← links)
- One bad formula can spoil everything: a simple adjustment that would improve the UN's gender inequality index (Q2315956) (← links)
- Doing math in jest: reflections on useless math, the unreasonable effectiveness of mathematics, and the ethical obligations of mathematicians (Q2325853) (← links)
- Data science vs. statistics: two cultures? (Q2329839) (← links)
- Epistemic injustice in mathematics (Q2690220) (← links)
- Credal Calculi, Evidence, and Consistency (Q5020164) (← links)
- On Experimental Mathematics and Mathematics Education (Q5163938) (← links)
- Boosting Insights in Insurance Tariff Plans with Tree-Based Machine Learning Methods (Q5165011) (← links)
- Enhancing Data Science Ethics Through Statistical Education and Practice (Q6066712) (← links)
- Fair and Efficient Allocation of Scarce Resources Based on Predicted Outcomes: Implications for Homeless Service Delivery (Q6135979) (← links)
- Mathematics, ethics and purism: an application of MacIntyre's virtue theory (Q6142474) (← links)
- Addressing confirmation bias in middle school data science education (Q6154258) (← links)
- Integrating Ethics into the Guidelines for Assessment and Instruction in Statistics Education (GAISE) (Q6562806) (← links)
- The opportunities, limitations, and challenges in using machine learning technologies for humanitarian work and development (Q6577744) (← links)
- When causality meets fairness: a survey (Q6615565) (← links)
- Auditing and debugging deep learning models via flip points: individual-level and group-level analysis (Q6664405) (← links)
