【Congratulation】One accepted journal paper in NMI
Barriers to academic data science research in the new realm of algorithmic behaviour modification by digital platforms
數據科學學術研究在新領域中的挑戰：研究數字平台上行為改變技術的阻礙
Travis Greene (National Tsing Hua University, Institute of Service Science, Hsinchu, Taiwan)
David Martens (University of Antwerp, Department of Engineering Management, Antwerp, Belgium)
Galit Shmueli (National Tsing Hua University, Institute of Service Science, Hsinchu, Taiwan)
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 has 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.
行為大數據時代為數據科學研究創造了新的途徑，許多新的貢獻也來自學術研究人員的努力。然而，學術界越來越難以訪問/取得由平台所控制的數據。平台現在通常使用行為改變的演算法技術來操縱用戶的行為，這使得學術研究人員在進行重要的數據科學和計算社會科學研究時更加孤立無援。這種孤立是由於研究人員無法訪問人類行為數據，且其中更重要的是：研究人員無法訪問從人類數據中學習與觸發的機器行為數據以及平台的行為修改機制。鑑於行為改變技術對個人和社會福祉的影響，我們討論了數據科學知識創造的結果，並鼓勵學術數據科學家在開展研究中扮演新角色，以促進 (1) 平台透明度和 (2) 知情公眾圍繞數字平台的社會目的和功能展開辯論。
Editor's Summary
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.
學術研究人員難以接觸由商業平台所控制的行為大數據和行為改變的演算法技術。 格林等人描述了學術研究在此類數據和算法遇到的阻礙，並為增強平台的訪問與透明度提供案例。
