Machine learning is changing the landscape of political advertising. Consumers need greater transparency into how their personal data influences the political messages they see online.
The City of Boston faces new challenges as it seeks to harness the power of machine learning to improve performance, optimize processes, and explore frontiers in local governance.
Lemonade, a company offering home insurance policies, is a pioneer in the InsurTech world where its use of machine learning (ML) goes beyond satisfying customers and driving efficiencies to underwriting risks and managing claims
Despite its exponential growth, Lemonade’s scalability is questioned considering increasing customer demand and limitations around availability and precision of data.
In an era of decreased attention span, heightened competition and high content creation costs, Netflix is countering churn with innovative machine-learning algorithms which aim to provide subscribers with the most personalized entertainment experience but would this be enough to maintain a sustainable competitive position in the long run?
Glossier engages its customers on numerous platforms, each providing this beauty brand with a wealth of data. Glossier is feeding this data into machine learning algorithms to better direct the customer down a purchasing path and to automate the crowdsourcing of product development.
In an effort to come back from its multi-year slump, H&M is turning to machine-learning. Is machine-learning the answer to H&M’s problems and is it too late?
How to release the full potential of renewable energy? Energy internet and machine learning would the right combination to achieve the energy efficient and low carbon future for China.
Square’s use of alternate data sources combined with machine learning is transforming access to credit for millions of small businesses.
Airline loyalty programs (also called Frequent Flyer Programs) have become a new source of high margin income for traditional airline companies. Meanwhile, loyalty programs also generate massive amount of data on customer behaviour as people spend and collect their points. Applying machine learning tools to understand the preferences of those frequent flyers, most of whom have strong spending power, will enable an airline loyalty program to monetize on areas beyond travel. Qantas Australia is the leader in adopting a data-centric approach to manage their loyalty business.
This paper will focus on Facebook’s use of machine learning to manage political content on its site. Today, with platforms like Facebook, content is being generated by a wider range of sources, which has eroded the credibility of the political information on Facebook. Recently, we have seen this occur with the proliferation of “fake news”, specifically falsified political information. This development has significant implications for Facebook and risks alienating its user based which can impact its bottom line and user base. It is Facebook’s mission to create a constructive community that brings people together to create positive experiences. False news is “harmful to [their] community” and “makes the world less informed” which inherently “erodes trust” with its users. In this context, using machine learning and other statistical tools to identify inaccurate and manipulated information is paramount to Facebook’s efforts to combat the spread of such information.