Businesses are witnessing a tipping point that can dramatically change the trajectory of their growth. Shifting attention from Big Data collection to real-time data consumption is setting the stage for a level of intelligence that requires a new way of thinking about processes and the applications that enable them. Fortunately, technologies such as artificial intelligence, machine learning, and blockchain are helping companies find a path forward. For many organizations, pulling together large volumes of data – which comprise diverse formats and originate from numerous sources – is creating a wave of pressure-tested innovations, data security and privacy, and a seismic shift in business value. And this outcome is changing not only the definition of industry leadership but also people's lives for the better.

CSR is gaining more and more importance in the business world. The companies with a good CSR strategy in place have a distinct competitive advantage. Consumers prefer products and services by socially responsible organisations even at a higher price. The companies have started to have CSR budgets to leverage this trend. However, many times the impact created by CSR programs does not reach its potential because of various glitches.

The media plays a central role in monitoring powerful institutions and identifying any activities harmful to the public interest. In the investing sphere constituted of 46,583 officially listed domestic firms on the stock exchanges worldwide, there is a growing interest `to do the right thing', i.e., to put pressure on companies to improve their environmental, social and government (ESG) practices. However, how to overcome the sparsity of ESG data from non-reporting firms, and how to identify the relevant information in the annual reports of this large universe? Here, we construct a vast heterogeneous information network that covers the necessary information surrounding each firm, which is assembled using seven professionally curated datasets and two open datasets, resulting in about 50 million nodes and 400 million edges in total. Exploiting this heterogeneous information network, we propose a model that can learn from past adverse media coverage patterns and predict the occurrence of future adverse media coverage events on the whole universe of firms. Our approach is tested using the adverse media coverage data of more than 35,000 firms worldwide from January 2012 to May 2018. Comparing with state-of-the-art methods with and without the network, we show that the predictive accuracy is substantially improved when using the heterogeneous information network. This work suggests new ways to consolidate the diffuse information contained in big data in order to monitor dominant institutions on a global scale for more socially responsible investment, better risk management, and the surveillance of powerful institutions.

When you think of artificial intelligence (AI), what do you envision? Depending on what generation you were born into, you might think of HAL 9000's glowing red eye from 2001: A Space Odyssey, or you might think of the surprisingly humanlike Ava from Ex Machina. Or, if you're knee-deep in AI programming, you might envision AI as nothing more than a complex web of computational frameworks. For decades, pop culture and science fiction have illustrated the possibilities of AI--but these depictions aren't always accurate, and they aren't always in ways that positively encourage its development. By that same token, these depictions have inspired thousands, if not millions of curious minds to push the boundaries of what AI can accomplish (and even take efforts to improve our safety).

All tools can be used for good or ill. Even a broom can be used to sweep the floor or hit someone over the head. The more powerful the tool, the greater the benefit or damage it can cause. The last few months have brought this into stark relief when it comes to computer-assisted facial recognition – the ability of a computer to recognize people's faces from a photo or through a camera. This technology can catalog your photos, help reunite families or potentially be misused and abused by private companies and public authorities alike. Facial recognition technology raises issues that go to the heart of fundamental human rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products.

Kriti Sharma, vice president of AI and Ethics at Sage, shares what she wishes she knew as a teenager growing up in India and how tech can help underserved communities. You just built your first computer from scratch after reading a few books about them. But first, you need to endure a few more years of high school in India. I know you don't like school. But, I'm asking you to embrace your love for learning.

The development of new artificial intelligence (AI) technology is often subject to bias, and the resulting systems can be discriminatory, meaning more should be done by policymakers to ensure its development is democratic and socially responsible. This is according to Dr. Barbara Ribeiro of Manchester Institute of Innovation Research at The University of Manchester, in On AI and Robotics: Developing policy for the Fourth Industrial Revolution, a new policy report on the role of AI and Robotics in society, being published today. Dr. Ribeiro adds because investment into AI will essentially be paid for by tax-payers in the long-term, policymakers need to make sure that the benefits of such technologies are fairly distributed throughout society. She says: "Ensuring social justice in AI development is essential. AI technologies rely on big data and the use of algorithms, which influence decision-making in public life and on matters such as social welfare, public safety and urban planning."

In his 2011 book charting the history of corporate social responsibility, the academic and futurist Wayne Visser divided CSR into five ages: greed, philanthropy, marketing, management, and responsibility. Each age related to a particular stage for companies: defensive, charitable, promotional, strategic, and systemic. As we enter 2018 I am convinced that a new age of CSR has begun. It is known as the intelligence age and the corresponding stage is the cognitive era, when machines are deployed to radically transform and improve CSR processes. While ethics, sustainability, the multi-stakeholder model, and CSR have gained in popularity, problems with the management, effectiveness, need, and efficacy impede progress.

London could lose all of its Ubers, courtesy of the city's transportation agency. On Friday, Transport for London announced it would not renew the ridehailing giant's license to operate in the city, citing the company's "lack of corporate responsibility." The license expires September 30, though, unsurprisingly, Uber has declared it will exercise its right to an appeal. The company is able to continue operating in the city as long as the legal process drags on, but it didn't wait for its lawyers to prepare their case before dusting off the weapon that has carried it through many a battle: public fervor. Right after TfL dropped its news, Uber posted a petition on Change.org.

Acorns is the newest app capturing the imagination of the Australian market. Aiming to streamline the saving process while making it easier than ever to enter the investing sphere, Acorns is the hyped-up US micro-investing app which launched in Australia last year. A Techly Guest Post by venture capitalist, Omar Khan, had a look at the unique functions of the Acorns app. The app's investment options are broken down like so: "The app gives investors several options. The other, a voluntary contribution whenever I have some spare money I'd like to save.