Related papers: Biases in Data Science Lifecycle
In the era of Industry 4.0, artificial intelligence (AI) is assuming an increasingly pivotal role within industrial systems. Despite the recent trend within various industries to adopt AI, the actual adoption of AI is not as developed as…
Increasingly larger number of software systems today are including data science components for descriptive, predictive, and prescriptive analytics. The collection of data science stages from acquisition, to cleaning/curation, to modeling,…
Modern tools for biological research, especially microscopy, have rapidly advanced in recent years, which has led to the generation of increasingly large amounts of data on a regular basis. The result is that scientists desperately need…
This article offers a short guide to the steps scientists can take to ensure that their data and associated analyses continue to be of value and to be recognized. In just the past few years, hundreds of scholarly papers and reports have…
There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to make their algorithms more understandable. Much of this research is focused on explicitly explaining decisions or…
Nowadays, we delegate many of our decisions to Artificial Intelligence (AI) that acts either in solo or as a human companion in decisions made to support several sensitive domains, like healthcare, financial services and law enforcement. AI…
Since a significant time ago, although time runs very fast,nanotechnology transformed from one of the most promising scientific hopes in uncountable human domains into a marvelous certainty. Innumerable scientific studies in several areas…
It is tempting to think that machines are less prone to unfairness and prejudice. However, machine learning approaches compute their outputs based on data. While biases can enter at any stage of the development pipeline, models are…
In today's digital era, the Internet, especially social media platforms, plays a significant role in shaping public opinions, attitudes, and beliefs. Unfortunately, the credibility of scientific information sources is often undermined by…
Sharing data can often enable compelling applications and analytics. However, more often than not, valuable datasets contain information of a sensitive nature, and thus, sharing them can endanger the privacy of users and organizations. A…
The evolution of AI-based system and applications had pervaded everyday life to make decisions that have momentous impact on individuals and society. With the staggering growth of online data, often termed as the Online Infosphere it has…
We present a programmatic approach to incorporating ethics into an undergraduate major in statistical and data sciences. We discuss departmental-level initiatives designed to meet the National Academy of Sciences recommendation for…
Recognizing, assessing, countering, and mitigating the biases of different nature from heterogeneous sources is a critical problem in designing a cognitive Decision Support System (DSS). An example of such a system is a cognitive…
Data-driven algorithms are only as good as the data they work with, while data sets, especially social data, often fail to represent minorities adequately. Representation Bias in data can happen due to various reasons ranging from…
Data science is gaining more and more and widespread attention, but no consensus viewpoint on what data science is has emerged. As a new science, its objects of study and scientific issues should not be covered by established sciences. Data…
Artificial Intelligence (AI) is becoming the corner stone of many systems used in our daily lives such as autonomous vehicles, healthcare systems, and unmanned aircraft systems. Machine Learning is a field of AI that enables systems to…
Science has a data management problem, as well as a project management problem. While industrial-grade data science teams have embraced the agile mindset, and adopted or created all kind of tools to create reproducible workflows,…
Social media studies often collect data retrospectively to analyze public opinion. Social media data may decay over time and such decay may prevent the collection of the complete dataset. As a result, the collected dataset may differ from…
Machine learning applications are becoming increasingly pervasive in our society. Since these decision-making systems rely on data-driven learning, risk is that they will systematically spread the bias embedded in data. In this paper, we…
From smart sensors that infringe on our privacy to neural nets that portray realistic imposter deepfakes, our society increasingly bears the burden of negative, if unintended, consequences of computing innovations. As the experts in the…