Comparing what has profoundly changed since before and after the Web 2.0, it’s very interesting – for not saying disappointing – to notice how we have shifted from a digital utopia where sharing, collaborating and free exchange reigned supreme, to an era where we start to doubt about the benevolence of computers and Internet. Web 2.0 Golden Age is indeed far behind us. While reading some articles, the word ‘algorithm’ seems to be used in the same way as if we were talking about a virus and at the other side, companies are entangled in a jungle of regulations and constraints. If we want to stand out the Before and After, we can only agree that social networks have totally disrupted the digital landscape – irreversibly. We don’t have to forget how and why Social Networks have been so popular since the day they came into our lives – People finally had the opportunity to be listened to. The tool is not criticizable in itself but what netizens do with it is far more worrying. A few years later the rise, we also have to accept that the best tool for promoting democracy is also the best way to spread misinformation and to manipulate public opinion.

Predicts the most common race of a surname based on U.S. Census data.

The paper describes how the new technologies and data they generate are transforming medicine. It stresses the uniqueness of heterogeneous medical data and the ways of dealing with them. It lists different sources that generate big medical data, their security, legal and ethical issues, as well as machine learning/AI methods of dealing with them. A unique feature of the paper is use of case studies to illustrate how the new technologies influence medical practice.

The new book by philosopher Deborah Mayo is relevant to data science for topical reasons, as she takes various controversial positions regarding hypothesis testing and statistical practice, and also as an entry point to thinking about the philosophy of statistics. The present article is a slightly expanded version of a series of informal reviews and comments on Mayo’s book. We hope this discussion will introduce people to Mayo’s ideas along with other perspectives on the topics she addresses.

Hiring robots for the workplaces is a challenging task as robots have to cater to customer demands, follow organizational protocols and behave with social etiquette. In this study, we propose to have a humanoid social robot, Nadine, as a customer service agent in an open social work environment. The objective of this study is to analyze the effects of humanoid robots on customers at work environment, and see if it can handle social scenarios. We propose to evaluate these objectives through two modes, namely, survey questionnaire and customer feedback. We also propose a novel approach to analyze customer feedback data (text) using sentic computing methods. Specifically, we employ aspect extraction and sentiment analysis to analyze the data. From our framework, we detect sentiment associated to the aspects that mainly concerned the customers during their interaction. This allows us to understand customers expectations and current limitations of robots as employees.

Data cooperatives with fiduciary obligations to members provide a promising direction for the empowerment of individuals through their own personal data. A data cooperative can manage, curate and protect access to the personal data of citizen members. Furthermore, the data cooperative can run internal analytics in order to obtain insights regarding the well-being of its members. Armed with these insights, the data cooperative would be in a good position to negotiate better services and discounts for its members. Credit Unions and similar institutions can provide a suitable realization of data cooperatives.

High-resolution individual geolocation data passively collected from mobile phones is increasingly sold in private markets and shared with researchers. This data poses significant security, privacy, and ethical risks: it’s been shown that users can be re-identified in such datasets, and its collection rarely involves their full consent or knowledge. This data is valuable to private firms (e.g. targeted marketing) but also presents clear value as a public good. Recent public interest research has demonstrated that high-resolution location data can more accurately measure segregation in cities and provide inexpensive transit modeling. But as data is aggregated to mitigate its re-identifiability risk, its value as a good diminishes. How do we rectify the clear security and safety risks of this data, its high market value, and its potential as a resource for public good? We extend the recently proposed concept of a tradeoff curve that illustrates the relationship between dataset utility and privacy. We then hypothesize how this tradeoff differs between private market use and its potential use for public good. We further provide real-world examples of how high resolution location data, aggregated to varying degrees of privacy protection, can be used in the public sphere and how it is currently used by private firms.

Over the past decade, artificial intelligence has demonstrated its efficiency in many different applications and a huge number of algorithms have become central and ubiquitous in our life. Their growing interest is essentially based on their capability to synthesize and process large amounts of data, and to help humans making decisions in a world of increasing complexity. Yet, the effectiveness of algorithms in bringing more and more relevant recommendations to humans may start to compete with human-alone decisions based on values other than pure efficacy. Here, we examine this tension in light of the emergence of several forms of digital normativity, and analyze how this normative role of AI may influence the ability of humans to remain subject of their life. The advent of AI technology imposes a need to achieve a balance between concrete material progress and progress of the mind to avoid any form of servitude. It has become essential that an ethical reflection accompany the current developments of intelligent algorithms beyond the sole question of their social acceptability. Such reflection should be anchored where AI technologies are being developed as well as in educational programs where their implications can be explained.