Most literature in fairness has focused on improving fairness with respect to one single model or one single objective. However, real-world machine learning systems are usually composed of many different components. Unfortunately, recent research has shown that even if each component is "fair", the overall system can still be "unfair". In this paper, we focus on how well fairness composes over multiple components in real systems. We consider two recently proposed fairness metrics for rankings: exposure and pairwise ranking accuracy gap. We provide theory that demonstrates a set of conditions under which fairness of individual models does compose. We then present an analytical framework for both understanding whether a system's signals can achieve compositional fairness, and diagnosing which of these signals lowers the overall system's end-to-end fairness the most. Despite previously bleak theoretical results, on multiple data-sets -- including a large-scale real-world recommender system -- we find that the overall system's end-to-end fairness is largely achievable by improving fairness in individual components.

A group of researchers have published results from a shocking experiment that shows how voice controlled smart devices can be operated remotely using targeted laser beams to simulate human speech. The researchers announced Monday that they were able to control a Google Home and command it to remotely open the garage door from a separate building 230 feet away. Also susceptible were Amazon's Echo, Facebook Portal, a range of Android smartphones and tablets, and both iPhones and iPads. The experiments were conducted by a group of scientists from the University of Michigan and The University of Electro-Communications in Tokyo. 'It's possible to make microphones respond to light as if it were sound,' Takeshi Sugarawa, of University of Electro-Communications in Tokyo, told Wired.

The value of machine learning in finance is becoming more apparent by the day. As banks and other financial institutions strive to beef up security, streamline processes, and improve financial analysis, ML is becoming the technology of choice. Unlike so many hyped technologies and overrated buzzwords, machine learning is not going away -- probably ever. The ability of computer programs to learn on their own and improve over time creates new opportunities for industries across the board. While it is true that the naturally conservative financial industry was not at the front of the line for ML adoption, machine learning in fintech is now a common phrase.

Artificial Intelligence (AI) has become part of our everyday lives, from smart personal assistants such as Siri and Alexa, to security surveillance, and automatic parking systems. In education, particularly in higher education, Artificial Intelligence is transforming traditional methods of teaching for both students and teachers, making the whole learning experience more customised, efficient and engaging. The world has changed due to the momentum of technology, and teaching must reflect the fact that information is available at all times and in all places through smartphones and tablets. Today, students do not necessarily need to attend physical classes as long as they have computers and internet connection, and although challenging, AI has and will continue to benefit education in various ways. Firstly, from a Business School perspective, new technology can be used to enhance the efficiency of the school itself.

A few years back, the thought of a fully smart home might have seemed far-fetched. With everything around us becoming increasingly interconnected, home appliances and devices are now becoming ever more combined into the internet. The main goal of a smart home is to provide comfort, efficiency, and security to the homeowner. This trend is quickly shifting from a luxury to a necessity for a majority of homeowners, not only in the UK but worldwide. A smart home has a collection of interconnected home devices powered by intelligent applications.

A new report suggests consumers fully expect more automation and technology to be involved in their interactions with brands over the next ten years, but that presents a fine line between effectiveness and privacy for brands to walk. The report is based on a survey of 4,000 respondents across three dozen countries, conducted by Futurum Research on behalf of analytics company SAS. The respondents were a mix of consumers and marketing professionals and were asked about their anticipated use of technology in the year 2030. The main finding of the report is that there will be an increasing openness to automation when it comes to interactions between brands and consumers. According to SAS' analysis of the data, 67% of engagements done through digital channels in 2030 will be completed by smart systems, while 69% of business decisions made during that engagement will be completed by smart machines.

Apple's Siri Drew everyone's attention once it had been initial free in Oct 2011. Since then, quite a few virtual assistants have surfaced within the market, serving as instruments of automation that improve productivity. Similarly, Google currently and Amazon Alexa have even surpassed the expectation of users by Natural Language Processing(NLP) to form conversations additional interactive and fewer machine-like. From setting an easy reminder to narrating hour stories, these virtual assistants deliver varied capabilities by leverage computer science. Such Associate in the Nursing ecosystem -- on any platform -- can prove to be extraordinarily useful for entrepreneurs World Health Organization perpetually move with multiple knowledge sets and access data from totally different sources.

Artificial Intelligence (AI) and Machine Learning (ML) technologies have changed the way both offline and e-retailers interact with or approach the customer and the way they offer products, particularly in fashion eretail. AI and ML are allowing for critical insights into more accurate personalisation of products and services. Customer experiences can now be data-driven all thanks to insights that we receive through the implementation of AI and ML. These technologies allow pattern recognition of each consumer to match their preferences to the product offerings. Robust recommendation engines based on ML can boost up-sell and cross-selling opportunities – directly affecting the top and bottom line.

Having recently published our Top 20 Global Thought Leaders and Influencers on Artificial Intelligence (September 2019), we asked a selection of our Thinkers360 global influencers about their predictions for AI in 2020. Here's what they told us… When data and algorithms are king, then moral and ethical integrity is queen. In light of the Cambridge Analytica scandal, the New York Times ran a headline: "Don't Fix Facebook. In the intelligent age, organizations will win in the long-term when their leadership is based on a moral and ethical foundation – something only humans can provide. The European Commission's ‚ethics for trustworthy AI'-initiative marks a milestone on the road towards the responsible development and deployment of human-centric AI.

In the last decade, the digital age has sharply redefined the way we study human behavior. With the advancement of data storage and sensing technologies, electronic records now encompass a diverse spectrum of human activity, ranging from location data, phone and email communication to Twitter activity and open-source contributions on Wikipedia and OpenStreetMap. In particular, the study of the shopping and mobility patterns of individual consumers has the potential to give deeper insight into the lifestyles and infrastructure of the region. Credit card records (CCRs) provide detailed insight into purchase behavior and have been found to have inherent regularity in consumer shopping patterns; call detail records (CDRs) present new opportunities to understand human mobility, analyze wealth, and model social network dynamics. In this chapter, we jointly model the lifestyles of individuals, a more challenging problem with higher variability when compared to the aggregated behavior of city regions. Using collective matrix factorization, we propose a unified dual view of lifestyles. Understanding these lifestyles will not only inform commercial opportunities, but also help policymakers and nonprofit organizations understand the characteristics and needs of the entire region, as well as of the individuals within that region. The applications of this range from targeted advertisements and promotions to the diffusion of digital financial services among low-income groups.