Book contents
- Ethics in Econometrics
- Ethics in Econometrics
- Copyright page
- Contents
- Figures
- Tables
- Preface
- Acknowledgments
- Recommended Reading
- Introduction
- 1 Ethical Guidelines
- 2 Scientific Misconduct
- 3 Influential Observations
- 4 Model Selection
- 5 Estimation and Interpretation
- 6 Missing Data
- 7 Spurious Relations
- 8 Blinded by the Data
- 9 Predictability
- 10 Adjustment of Forecasts
- 11 Big Data
- 12 Algorithms
- Conclusion
- Index
12 - Algorithms
Published online by Cambridge University Press: 14 November 2024
- Ethics in Econometrics
- Ethics in Econometrics
- Copyright page
- Contents
- Figures
- Tables
- Preface
- Acknowledgments
- Recommended Reading
- Introduction
- 1 Ethical Guidelines
- 2 Scientific Misconduct
- 3 Influential Observations
- 4 Model Selection
- 5 Estimation and Interpretation
- 6 Missing Data
- 7 Spurious Relations
- 8 Blinded by the Data
- 9 Predictability
- 10 Adjustment of Forecasts
- 11 Big Data
- 12 Algorithms
- Conclusion
- Index
Summary
This chapter uses a range of quotes and findings from the internet and the literature. The key premises of this chapter, which is illustrated with examples, are as follows. First, Big Data requires the use of algorithms. Second, algorithms can create misleading information. Third, algorithms can lead to destructive outcomes. But we should not forget that humans program algorithms. With Big Data come algorithms to run many and involved computations. We cannot oversee all these data ourselves, so we need the help of algorithms to make computations for us. We might label these algorithms as Artificial Intelligence, but this might suggest that they can do things on their own. They can run massive computations, but they need to be fed with data. And this feeding is usually done by us, by humans, and we also choose the algorithms to be used.
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- Ethics in EconometricsA Guide to Research Practice, pp. 269 - 280Publisher: Cambridge University PressPrint publication year: 2024