De, S; Jangra, S; Agarwal, V; Johnson, J; Sastry, N; (2023) Biases and Ethical Considerations for Machine Learning Pipelines in the Computational Social Sciences. In: Ethics in Artificial Intelligence: Bias, Fairness and Beyond. (pp. 99-113). Springer Nature: Singapore.

Preview

Text SDe CSS ML Bias Revised 2.pdf - Submitted Version Download (445kB) | Preview

Abstract

Computational analyses driven by Artificial Intelligence (AI)/Machine Learning (ML) methods to generate patterns and inferences from big datasets in computational social science (CSS) studies can suffer from biases during the data construction, collection and analysis phases as well as encounter challenges of generalizability and ethics. Given the interdisciplinary nature of CSS, many factors such as the need for a comprehensive understanding of different facets such as the policy and rights landscape, the fast-evolving AI/ML paradigms and dataset-specific pitfalls influence the possibility of biases being introduced. This chapter identifies challenges faced by researchers in the CSS field and presents a taxonomy of biases that may arise in AI/ML approaches. The taxonomy mirrors the various stages of common AI/ML pipelines: dataset construction and collection, data analysis and evaluation. By detecting and mitigating bias in AI, an active area of research, this chapter seeks to highlight practices for incorporating responsible research and innovation into CSS practices.

Download activity - last month

Export as XMLCSVJSON

Download activity - last 12 months

Export as JSONCSVXML

Downloads by country - last 12 months

Export as JSONCSVXML

Archive Staff Only

View Item