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Ethics

As explained in Effective Data Analysis

Ethics in science concerns principles governing responsible scientific practice and the treatment of people, animals, and society.

Ethics provides principles for responsible scientific and analytical practice, helping practitioners recognize how data, experiments, and deliverables can affect people, organizations, and society. section 8.0 section 8.1

Ethical analysis asks practitioners to consider:

  • How research subjects, including animals, should be treated and which investigative practices should be avoided. section 8.1
  • Who benefits from scientific knowledge and whether advances reach people who need them across socioeconomic groups. section 8.1
  • How findings or models might be misused and what safeguards can reduce unintended harm. section 8.1
  • How sensitive information can be handled, anonymized, and analyzed responsibly. section 8.0

Biased hiring model

Amazon developed a resume-ranking model trained on a decade of hiring data. Because the dataset reflected a male-dominated technology workforce, the model penalized candidates associated with women’s colleges or the word “women.” After attempts to remove the bias, Amazon abandoned the project because the rankings could not be trusted as accurate and reliable. section 8.0

Figure 8.1 The outcomes of scientific discovery can lead society down a path of advancement or collective harm.
figure

Use the figure to trace how scientific discoveries can produce social advancement or collective harm, depending on their effects and use. section 8.1

How Effective Data Analysis covers it

How deep it goes
Connects ethical principles to protected data, anonymization, consent, bias, access, and misuse without providing detailed procedures.
Examples
Uses animal research debates, unequal access to advances, misuse of knowledge, and Amazon’s biased resume-ranking model.
What you can do after
Evaluate data and scientific work for stakeholder effects, potential bias, unintended consequences, and opportunities to reduce harm.
How it connects
Connects sensitive data, anonymization, consent, bias, machine learning, scientific practice, and stakeholders.

Also explained in

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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.