Locating ethics in data science: responsibility and accountability in global and distributed knowledge production...

Area of Science:

• Data Science
• Ethics
• Research Integrity

Background:

• Global data science presents challenges in evaluating data quality and impact.
• This complicates accountability and responsibility in data management and analysis.
• Trust in automated data mining tools is questioned due to ethical concerns.

Purpose of the Study:

• To propose a framework for managing data science practices.
• To address challenges in data quality, accountability, and ethical oversight.
• To enhance the reliability and ethical integrity of data science research.

Main Methods:

• Advocating for participative and reflexive management of data practices.
• Encouraging regular examination of ethical implications and historical data lineages.
• Fostering awareness of diverse skills and perspectives in data science.

Main Results:

• Improved ethical oversight for data science initiatives.
• Enhanced quality and reliability of research outputs.
• Clearer understanding of responsibilities and accountabilities in data science.

Conclusions:

• A participative, reflexive approach can improve ethical oversight and research quality.
• Regulatory structures should promote ethical examination and interdisciplinary awareness.
• Addressing distributed data science challenges is crucial for trustworthy research.