How do we understand and model the role of values and biases in scientific research?

Introduction

The role of values and biases in scientific research is a critical area of study, as it influences the reliability and integrity of scientific findings. Understanding and modeling these aspects can help in mitigating the effects of biases and promoting the application of desirable values in scientific practices.

Key Insights

Cognitive biases are deviations in thought processes that can systematically lead scientists to incorrect conclusions, and debiasing mechanisms are suggested to counteract these biases in research outcomes.
Systematic biases affect the salience of scientific reasoning, and intellectual virtues can help manage these biases by setting community standards for scientific inquiry.
Values are integral to scientific practice, influencing observations and experiments, and there is a debate on the role of social and ethical values in scientific theory assessment, with arguments supporting the rational application of values to avoid biases.
The aim of understanding in science is intertwined with other epistemic aims and is influenced by contextually determined values, which interact with the process of scientific explanation.

Conclusion

In summary, the understanding and modeling of values and biases in scientific research involve recognizing cognitive biases and their impact on scientific conclusions, the importance of intellectual virtues in managing biases, the debate on the role of social and ethical values in science, and the interaction between understanding and other epistemic aims influenced by values. Addressing these factors is essential for enhancing the rationality and reliability of scientific research.

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How do we understand and model the role of values and biases in scientific research?

Summary

Introduction

Key Insights

Conclusion

Sources and full results

Methodological and Cognitive Biases in Science: Issues for Current Research and Ways to Counteract Them

Cognitive biases in science can lead to wrong conclusions, and debiasing mechanisms can potentially counteract these biases and improve research reliability.

Managing Salience: The Importance of Intellectual Virtue in Analyses of Biased Scientific Reasoning

Intellectual virtues play a crucial role in determining what is salient in scientific research, and explicit public standards for intellectual virtue can improve reasoning in biased conditions.

Values in Science: Cognitive-Affective Maps

Cognitive-affective maps can help understand how values influence scientific deliberations and help identify rationally appropriate ways to incorporate them in scientific practice.

Understanding, Values, and the Aims of Science

Understanding science's central epistemic aims, such as prediction and accurate description, is linked to intelligible theories and contextually determined values, as demonstrated through a historical case study.

Addressing problems in profit-driven research: how can feminist conceptions of objectivity help?

Feminist philosophy of science can help address commercial bias in biomedical research by minimizing biases and providing accounts of objectivity that don't require value-neutral or disinterested scientists.

Bias in science: natural and social

Values influence both social and natural sciences equally, with bias in both fields being a natural and social part of human nature.

Herding, social influences and behavioural bias in scientific research

Bias in scientific research can distort the evolution of knowledge and potentially impact public policy and decision-making.

Good practices for quantitative bias analysis.

This paper provides a guide to good practices for applying quantitative bias analysis to epidemiological data, aiming to encourage more widespread use of this method to estimate biases and their uncertainty in estimates.

The role of values in educational research: the case for reflexivity

Reflexivity is crucial in educational research, as the complex interaction of moral, competency, personal, and social values significantly influences the research process and ethical decisions.

Distinguishing between legitimate and illegitimate values in climate modeling

Non-epistemic values in climate modeling are legitimate when they promote democratically endorsed epistemological and social aims of research, while addressing concerns about bias and disproportionate power.

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