Special issue of <i>Production and Operations Management</i> on “Responsible Data Science”
Bibliographic record
Abstract
Submissions open: August 1, 2022 Deadline: November 30, 2022 Our society is experiencing a rapid digital transformation, changing the way how different players in supply chains and technological systems interact with each other and exert their influences. For example, the way that businesses and customers interact has changed in the digital economy with the influence of computing technology and information sharing. Businesses now routinely collect large volumes of fine-grained data to analyze consumers’ behavior, and consumers can also track changes in firms’ strategies to make informed purchasing decisions. An iconic trend in the era of digital transformation is the increasingly extensive use of data analytics and machine learning tools in decision making as both strategic and operational levers. The use of rich and large data sets also raises critical societal concerns. For example, data sets often include personal sensitive information that can be exploited, without explicit knowledge and/or consent from the involved individuals, for various purposes including monitoring, discrimination, and illegal activities. On the one hand, data- and artificial intelligence (AI)-driven algorithms may have created a competitive advantage for firms that are using these algorithms. For example, large corporations can create unequal competition in the market against smaller players. Similarly, firms may attract customers with stronger financial records by offering personalized enticing incentives, leading to a social bias toward individuals who are offered fewer appealing opportunities. On the other hand, algorithms that do consider social inclusion and fairness considerations have a great potential to reduce the inequalities induced by social status, gender, and race, just to name a few. Responsible data science is defined as the utilization and exploitation of data via manual analysis or automated algorithms (such as machine learning) that aim at improving the terms of participation in society, particularly for individuals or corporate entities that are disadvantaged. Such societal participation improvements include, but are not limited to, enhanced opportunities, increased access to resources, and greater voice and respect for human rights. This special issue aims to attracting submissions that are closely connected to real-world operational problems and have the potential to impact practice from the lens of responsible data science. All submissions must have clear managerial or theoretical contributions, and must be built upon rigorous research methods that serve as an appropriate framework to analyze problems: including analytical modeling, econometric analysis, field experimentation, and behavioral theories. Papers should be submitted through the POM manuscript central website: https://mc.manuscriptcentral.com/poms. Specifically, please follow the prompts below: On the author tab, please choose “Special Issue Article” (see the image below) in Step 1 In the drop-down menu (see the image below) that then appears in Step 1, please select appropriate department editor: Special Issue on Responsible Data Science. For Step 6, please upload a cover letter that includes the title of the special issue and the specific article type you are submitting. Towards the end of Step 6, please indicate “yes” for the question “Is this submission for a special issue?” and enter the title of the special issue in the text box below: “Responsible Data Science.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".