Name
Title | ||
|---|---|---|
Barriers to academic data science research in the new realm of algorithmic behaviour modification by digital platforms |
Abstract | ||
|---|---|---|
The era of behavioural big data has created new avenues for data science research, with many new contributions stemming from academic researchers. Yet data controlled by platforms have become increasingly difficult for academics to access. Platforms now routinely use algorithmic behaviour modification techniques to manipulate users' behaviour, leaving academic researchers further isolated in conducting important data science and computational social science research. This isolation results from researchers' lack of access to human behavioural data and, crucially, to both the data on machine behaviour that triggers and learns from the human data and the platform's behaviour modification mechanisms. Given the impact of behaviour modification on individual and societal well-being, we discuss the consequences for data science knowledge creation, and encourage academic data scientists to take on new roles in producing research to promote (1) platform transparency and (2) informed public debate around the social purpose and function of digital platforms. Behavioural big data and algorithmic behaviour modification technologies controlled by commercial platforms have become difficult for academic researchers to access. Greene et al. describe barriers to academic research on such data and algorithms, and make a case for enhancing platform access and transparency. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1038/s42256-022-00475-7 | NATURE MACHINE INTELLIGENCE |
DocType | Volume | Issue |
Journal | 4 | 4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Travis Greene | 1 | 0 | 0.34 |
| David Martens | 2 | 0 | 0.34 |
| Galit Shmueli | 3 | 265 | 23.00 |