Research Methodology

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Research Ethics and Intellectual Property Rights

Topic 5.8 of Research Ethics and Intellectual Property Rights

Case Studies in Ethics from Data Science and ML Research

Concrete historical cases where ML and data science research produced harmful outcomes — algorithmic bias in criminal justice, discriminatory facial recognition, data harvesting without consent, and chatbots weaponised at scale — and the ethical lessons each case established.

~25 min total·4 quadrants of structured content

By the end of this topic, you will

Explain how the COMPAS recidivism algorithm produced racially disparate outcomes and what this reveals about using historical data to predict future behaviour
Describe the Gender Shades study's findings on commercial facial recognition systems and the research design that exposed them
Analyse the Cambridge Analytica case as a failure of informed consent and data minimisation
Identify the ethical obligations that research involving personal data, predictive models, or dual-use tools places on the researcher before, during, and after a study

Quadrant 1 · e-Tutorial

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COMPAS Gave Biased Scores to Judges. Facial Recognition Failed Real People. Both Were Engineering Decisions.

93s

Video lesson

Quadrant 2 · e-Content

Articles and case studies

Case Studies in Ethics from Data Science and ML Research

The ethical problems in AI and ML research are not hypothetical. COMPAS deployed racially biased risk scores to judges. Facial recognition systems failed systematically on dark-skinned women. Cambridge Analytica harvested personal data from millions without their knowledge. Each case was a research and engineering decision that someone made — and that someone could have made differently.

Quadrant 3 · Web Resources

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Research Methodology — eBook

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Research Methodology — Question Bank

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Research Methodology — MCQ Bank

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Quadrant 4 · Self-Assessment

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Research Ethics and IPR

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This is the discussion thread for the lesson "Case Studies in Ethics from Data Science and ML Research" (Research Methodology, Unit 5). Ask questions, share real-world examples, and help classmates who are stuck. Conversation starters. Try explaining one of these in your own words: - Explain how the COMPAS recidivism algorithm produced racially disparate outcomes and what this reveals about using historical data to predict future behaviour - Describe the Gender Shades study's findings on commercial facial recognition systems and the research design that exposed them - Analyse the Cambridge Analytica case as a failure of informed consent and data minimisation

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