Related papers: Approaching Ethical Guidelines for Data Scientists
Data cooperatives offer a new model for fair data governance, enabling individuals to collectively control, manage, and benefit from their information while adhering to cooperative principles such as democratic member control, economic…
Datasets play a key role in imparting advanced capabilities to artificial intelligence (AI) foundation models that can be adapted to various downstream tasks. These downstream applications can introduce both beneficial and harmful…
Background: Despite a long history, numerous laws and regulations, ethics remains an unnatural topic for many software engineering researchers. Poor research ethics may lead to mistrust of research results, lost funding and retraction of…
Mathematical modelling and ethics have more touching points than most of us would like to admit. Everyday decisions are often reasoned by mathematical arguments. Mathematics teachers belong to those mathematically literate, who must point…
Visualizations have a potentially enormous influence on how data are used to make decisions across all areas of human endeavor. However, it is not clear how this power connects to ethical duties: what obligations do we have when it comes to…
Nowadays technology is being adopted on every aspect of our lives and it is one of most important transformation driver in industry. Moreover, many of the systems and digital services that we use daily rely on artificial intelligent…
This special volume of Statistical Sciences presents some innovative, if not provocative, ideas in the area of reliability, or perhaps more appropriately named, integrated system assessment. In this age of exponential growth in science,…
With humans increasingly serving as computational elements in distributed information processing systems and in consideration of the profit-driven motives and potential inequities that might accompany the emerging thinking economy[1], we…
The past decade has observed a significant advancement in AI with deep learning-based models being deployed in diverse scenarios, including safety-critical applications. As these AI systems become deeply embedded in our societal…
The 175th anniversary of the ASA provides an opportunity to look back into the past and peer into the future. What led our forebears to found the association? What commonalities do we still see? What insights might we glean from their…
Statistics experiences a storm around the perceived misuse and possible abuse of its methods in the context of the so-called reproducibility crisis. The methods and styles of quantification practiced in mathematical modelling rarely make it…
Current definitions of Information Science are inadequate to comprehensively describe the nature of its field of study and for addressing the problems that are arising from intelligent technologies. The ubiquitous rise of artificial…
Ethics has become a major concern to the information management community, as both algorithms and data should satisfy ethical rules that guarantee not to generate dishonourable behaviours when they are used. However, these ethical rules may…
The recent surge in interest in ethics in artificial intelligence may leave many educators wondering how to address moral, ethical, and philosophical issues in their AI courses. As instructors we want to develop curriculum that not only…
Across academia, government, and industry, data stewards are facing increasing pressure to make datasets more openly accessible for researchers while also protecting the privacy of data subjects. Differential privacy (DP) is one promising…
As artificial intelligence (AI) grows in popularity and importance-both as a domain within broader computing research and in society at large-increasing focus will need to be paid to the ethical governance of this emerging technology. The…
Data sharing partnerships are increasingly an imperative for research institutions and, at the same time, a challenge for established models of data governance and ethical research oversight. We analyse four cases of data partnership…
What do we teach and what should we teach? An honest answer to this question is painful, very painful--what we teach lags decades behind what we practice. How can we reduce this `gap' to prepare a data science workforce of trained…
Statistical science (as opposed to mathematical statistics) involves far more than probability theory, for it requires realistic causal models of data generators - even for purely descriptive goals. Statistical decision theory requires more…
This article is concerned with setting up practical guardrails within the research activities and environments of CSS. It aims to provide CSS scholars, as well as policymakers and other stakeholders who apply CSS methods, with the critical…