There are increasing concerns about the bias and fairness of artificial intelligence (AI)
models as they are put into clinical practice. Among the steps for implementing machine …

There is an increasing number of big data science projects aiming to create value for
organizations by improving decision making, streamlining costs or enhancing business …

Algorithms in the age of artificial intelligence (AI) constantly transform customer behaviour,
marketing programs, and marketing strategies in industrial markets. However, algorithms …

This paper explores the strengths and weaknesses of CRISP-DM when used for data
science projects. The paper then explores what key actions data science teams using …

Researchers and practitioners in the fairness community have highlighted the ethical and
legal challenges of using biased datasets in data-driven systems, with algorithmic bias …

Artificial Intelligence (AI) models are now being utilized in all facets of our lives such as
healthcare, education and employment. Since they are used in numerous sensitive …

Problem statement: Standardisation of AI fairness rules and benchmarks is challenging
because AI fairness and other ethical requirements depend on multiple factors, such as …

Integrating artificial intelligence (AI) has transformed living standards. However, AI's efforts
are being thwarted by concerns about the rise of biases and unfairness. The problem …

Recommender Systems (RSs) are used to provide users with personalized item
recommendations and help them overcome the problem of information overload. Currently …

This paper presents a critical, qualitative study of the social role of algorithmic bias in the
context of the Chicago crime prediction algorithm, a predictive policing tool that forecasts …