Unsupervised machine learning to analyse city logistics through Twitter

City Logistics is characterized by multiple stakeholders that often have different views of such a complex system. From a public policy perspective, identifying stakeholders, issues and trends is a daunting challenge, only partially addressed by traditional observation systems. Nowadays, social media is one of the biggest channels of public expression and is often used to communicate opinions and content related to City Logistics. The idea of this research is that analysing social media content could help in understanding the public perception of City logistics. This paper offers a methodology for collecting content from Twitter and implementing Machine Learning techniques (Unsupervised Learning and Natural Language Processing), to perform content and sentiment analysis. The proposed methodology is applied to more than 110 000 tweets containing City Logistics key-terms. Results allowed the building of an Interest Map of concepts and a Sentiment Analysis to determine if City Logistics entries are positive, negative or neutral.

Trial of an AI: Empowering people to explore law and science challenges

Artificial Intelligence represents many things: a new market to conquer or a quality label for tech companies, a threat for traditional industries, a menace for democracy, or a blessing for our busy everyday life. The press abounds in examples illustrating these aspects, but one should draw not hasty and premature conclusions. The first successes in AI have been a surprise for society at large-including researchers in the field. Today, after the initial stupefaction, we have examples of the system reactions: traditional companies are heavily investing in AI, social platforms are monitored during elections, data collection is more and more regulated, etc. The resilience of an organization (i.e. its capacity to resist to a shock) relies deeply on the perception of its environment. Future problems have to be anticipated, while unforeseen events occurring have to be quickly identified in order to be mitigated as fast as possible. The author states that this clear perception starts with a common definition of AI in terms of capacities and limits. AI practitioners should make notions and concepts accessible to the general public and the impacted fields (e.g. industries, law, education). It is a truism that only law experts would have the potential to estimate IA impacts on judicial system. However, questions remain on how to connect different kind of expertise and what is the appropriate level of detail required for the knowledge exchanges. And the same consideration is true for dissemination towards society. Ultimately, society will live with decisions made by the "experts". It sounds wise to involve society in the decision process rather than risking to pay consequences later. Therefore, society also needs the key concepts to understand AI impact on their life. This was the purpose of the trial of an IA that took place in October 2018 at the Court of Appeal of Paris: gathering experts from various fields to expose challenges in law and science towards a general public.