Recommender systems (RS), which are widely deployed across high-stakes domains, are susceptible to biases that can cause large-scale societal impacts. Researchers have proposed methods to measure and mitigate such biases -- but translating academic theory into practice is inherently challenging. RS practitioners must balance the competing interests of diverse stakeholders, including providers and users, and operate in dynamic environments. Through a semi-structured interview study (N=11), we map the RS practitioner workflow within large technology companies, focusing on how technical teams consider fairness internally and in collaboration with other (legal, data, and fairness) teams. We identify key challenges to incorporating fairness into existing RS workflows: defining fairness in RS contexts, particularly when navigating multi-stakeholder and dynamic fairness considerations. We also identify key organization-wide challenges: making time for fairness work and facilitating cross-team communication. Finally, we offer actionable recommendations for the RS community, including HCI researchers and practitioners.

Recent years have seen the development of many open-source ML fairness toolkits aimed at helping ML practitioners assess and address unfairness in their systems. However, there has been little research investigating how ML practitioners actually use these toolkits in practice. In this paper, we conducted the first in-depth empirical exploration of how industry practitioners (try to) work with existing fairness toolkits. In particular, we conducted think-aloud interviews to understand how participants learn about and use fairness toolkits, and explored the generality of our findings through an anonymous online survey. We identified several opportunities for fairness toolkits to better address practitioner needs and scaffold them in using toolkits effectively and responsibly. Based on these findings, we highlight implications for the design of future open-source fairness toolkits that can support practitioners in better contextualizing, communicating, and collaborating around ML fairness efforts.

Data Science as a discipline and profession demands its practitioners possess various skills, ranging from soft skills such as communication, leadership to hard skills such as deductive reasoning, algorithmic thinking, programming, and so on. But there's a crucial skill that should be attained by Data Scientists, irrespective of their experience, and that is writing. Even Data Scientists working in technical fields such as quantum computing, or healthcare research need to write. It takes time to develop a strong writing ability, and there are challenges that Data Scientists confront that might prevent them from expressing their thoughts easily. That's why this article contains a variety of writing strategies and explanations of how they benefit Data Science and Machine Learning professionals.

Data Science as a discipline and profession demands its practitioners possess various skills, ranging from soft skills such as communication, leadership to hard skills such as deductive reasoning, algorithmic thinking, programming, and so on. But there's a crucial skill that should be attained by Data Scientists, irrespective of their experience, and that is writing. Even Data Scientists working in technical fields such as quantum computing, or healthcare research need to write. It takes time to develop a strong writing ability, and there are challenges that Data Scientists confront that might prevent them from expressing their thoughts easily. That's why this article contains a variety of writing strategies and explanations of how they benefit Data Science and Machine Learning professionals.

You will start from the fundamental concepts and slowly build your knowledge of the subject.

This issue focuses on cool math problems that come with data sets, source code, and algorithms. Many have a statistical, probabilistic or experimental flavor, and some are dealing with dynamical systems. They can be used to extend your math knowledge, practice your machine learning skills on original problems, or for curiosity. My articles, posted on Data Science Central, are always written in simple English and accessible to professionals with typically one year of calculus or statistical training, at the undergraduate level. They are geared towards people who use data but are interesting in gaining more practical analytical experience.

My opinions are my own. An introduction to the harm that ML systems cause and to the power imbalance that exists between ML system developers and ML system participants …and 10 concrete ways for machine learning practitioners to help build fairer ML systems. Image description: Photo of Black Lives Matter protesters in Washington, D.C. -- 2 signs say "Black Lives Matter" and "White Silence is Violence." Machine learning systems are increasingly used as tools of oppression. All too often, they're used in high-stakes processes without participants' consent and with no reasonable opportunity for participants to contest the system's decisions -- like when risk assessment systems are used by child welfare services to identify at-risk children; when a machine learning (or "ML") model decides who sees which online ads for employment, housing, or credit opportunities; or when facial recognition systems are used to surveil neighborhoods where Black and Brown people live. In reality though, machine learning systems reflect the beliefs and biases of those who design and develop them.

Machine learning is a vast field. Thanks to the internet, there are plenty of resources available to get your hands on it -- from books to blogs to vlogs. Analytics India Magazine has been compiling learning resources for the ML community for quite some time now. In this article, we list down top machine learning books for those who want to get practical with algorithms. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.

Many individuals picture a robot or a terminator when they catch wind of Machine Learning (ML) or Artificial Intelligence (AI). However, they aren't something out of motion pictures, it is anything but a cutting edge dream. We are living in a situation with numerous cutting edge applications developed using machine learning, despite that there are certain challenges an ML practitioner might face while developing an application from zero to bringing them to production. Data plays a key role in any use case. For beginners to experiment with machine learning, they can easily find data from Kaggle, UCI ML Repository etc.

"Deepfake" techniques today are capable of producing artificial intelligence-generated videos of real people doing fictional things or fictional people doing real things. Their applications are being invented along the way ever since the success of GANs. The deep learning community is still partially clueless about the outcomes of the existing malicious content. That is why, industry experts have been collaborating to create awareness amongst the community. As a machine learning practitioner, one can do their own part by availing the resources on deep fake.