Link recommendation algorithms contribute to shaping human relations of billions of users worldwide in social networks. To maximize relevance, they typically propose connecting users that are similar to each other. This has been found to create information silos, exacerbating the isolation suffered by vulnerable salient groups and perpetuating societal stereotypes. To mitigate these limitations, a significant body of work has been devoted to the implementation of fair link recommendation methods. However, most approaches do not question the ultimate goal of link recommendation algorithms, namely the monetization of users' engagement in intricate business models of data trade. This paper advocates for a diversification of players and purposes of social network platforms, aligned with the pursue of social justice. To illustrate this conceptual goal, we present ERA-Link, a novel link recommendation algorithm based on spectral graph theory that counteracts the systemic societal discrimination suffered by vulnerable groups by explicitly implementing affirmative action. We propose four principled evaluation measures, derived from effective resistance, to quantitatively analyze the behavior of the proposed method and compare it to three alternative approaches. Experiments with synthetic and real-world networks illustrate how ERA-Link generates better outcomes according to all evaluation measures, not only for the vulnerable group but for the whole network. In other words, ERA-Link recommends connections that mitigate the structural discrimination of a vulnerable group, improves social cohesion and increases the social capital of all network users. Online social networks have a paramount impact on the social fabric of human communities.

When I think about "managing information" and using "information of many types and from many sources" I think about the different levels of interoperability of that information and the different types of AI and automation that occurs at different levels of interoperability. In this article, I introduce 4 levels of interoperability used in industries like Healthcare and the associated AI and automation that aligns with or is enabled by increasing levels of interoperability. These 4 levels of interoperability are critical to managing information and realizing the full potential of AI and automation for enabling a "holistic cyber defense machine". Foundational Interoperability (Level 1) – establishes the inter-connectivity requirements needed for one system or application to securely communicate data to and receive data from another. Foundational Interoperability lets the data transmitted by one system to be received by another.

AI is helping companies coordinate their workflows to achieve great efficiency and more synchronization. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Anyone who has ever worked for a large organization knows that information silos are a challenging fact of life. They're evident internally: The left hand doesn't always know what the right hand is doing, and employees who are supposed to be working in concert are out of sync. Companies that are in business together often don't have full information or a clear picture of their partnership.

There are multiple applications: We're not talking about just one small part of the whole that machine learning (ML) and AI have the opportunity to affect - in fact, there is opportunity throughout the chain, from more efficient delivery routes to price optimization and everything in between. The data is so siloed today: Across the supply chain, data is stored in many different places with different systems, and it's rare to consider it all together as a whole. That's why when enterprises do break down information silos there is so much to uncover that was previously unseen. The opportunity for automation is huge: When it comes to ML and AI in the supply chain, it isn't just about optimization, but also automation - removing steps from the process that humans previously had to do manually. There are multiple applications: We're not talking about just one small part of the whole that machine learning (ML) and AI have the opportunity to affect - in fact, there is opportunity throughout the chain, from more efficient delivery routes to price optimization and everything in between.

Advances in cloud computing, along with the big data movement, have transformed the business IT landscape. Leveraging the cloud, companies are now afforded on demand capacity and mobile accessibility to their business-critical systems and information. At the same time, the amount of structured and unstructured data created by, and available to, organizational users is a constantly moving target, with IDC estimating that the digital universe will grow by a factor of 10 between 2013 and 2020. But while both of these IT megatrends can be the catalysts for innovation and growth, organizations are facing significant new challenges when trying to seize upon their opportunities. Corporate datasets are growing more diverse, complex and massive in size.