Global Top100 Outstanding Project Announcement 8/10: INPS

Published on February 11, 2022

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Every year, a ton of emails are being sent to the INPS, the Social Security Administration of the Republic of Italy. To improve the efficiency of the work within the INPS agency ecosystem, INPS and Accenture developed an AI-based solution with the aim of improving the current manual process of classifying emails sent by citizens and dispatching them to the appropriate office. Thus, by decreasing the burden of public sector employees and improving the quality of services to citizens, the project addresses both SDG8 (Decent Work and Economic Growth) & SDG9 (Industry, Innovation, and Infrastructure).

As announced in the previous few articles, the IRCAI Scientific Program Committees and the IRCAI Scientific Journal Editorial Board have completed their review of the Global Top 100 project submissions. 10 solutions were deemed “outstanding projects” based on their centrality of AI, the potential impact on relevant SDG(s), demonstration of potential in completed work (either proof of concept or completed research paper), and ethical design. The aim of this article is to introduce the project to relevant stakeholders and the broader public and to make INPS’ voice heard on the world stage.

4 million emails are sent to the 450 territorial agencies linked to INPS on a yearly basis. The content of these emails is often fairly unstructured and can cover a wide range of complex topics. Thereby, many of these communications are not addressed to one specific receiver, prompting designated employees to sort these messages manually and forward them to the right person. This represents a “critical bottleneck in the Italian administration”, note the developers, as it substantially slows down the daily operations.

To provide the citizens with a faster response, INPS’ new email classification system is based on innovative AI techniques to analyze the emails’ content and context, and dispatch it to the appropriate employee. The developers made use of the Transformer architecture BERT (by Google), a set of Deep Learning models which are “drastically changing the Natural Language Processing (NLP) landscape”. The model was trained on more than 100.000 emails to generate the specific word embedding representation used for the classification of the email content – in compliance with GDPR policies.

With its rollout scheduled soon in several cities across the country, the team behind the solution estimates that the automatic classification of emails can save the INPS agencies more than 10,000 days of the year of work. The team behind the solution emphasizes that “INPS’ experience with AI/ML can be re-used in other public administrations across the globe to requalify the work of their employees and to further extend the solution such that additional use cases can be powered.

This project is a joint effort by INPS (the Social Security Administration of the Republic of Italy) and Accenture.


Presenting the Global Top 100 outstanding projects: Artificial Intelligence-Based Referral System for Patients With Diabetic Retinopathy in Jalisco

Presenting the Global Top 100 outstanding projects: Artificial Intelligence-Based Referral System for Patients With Diabetic Retinopathy in Jalisco

This project provides an inspiring example of an AI-based referral system for patients with diabetic retinopathy. Aware of the high prevalence of diabetes and a shortage of ophthalmologists and eye illness detections services in the Mexican State of Jalisco, the founding members of this
project developed an AI model based on deep convolutional neural networks (DCNN) to enable a timelier detection of Diabetic Retinopathy in people’s eyes. The trained models preprocess and evaluate the suitability and quality and classify relevant ocular documentation with an exceptional specificity and sensitivity on local data.

Presenting the Global Top 100 outstanding projects: MALENA

Presenting the Global Top 100 outstanding projects: MALENA

MALENA is an NLP-based ESG analyst tool enabling investors to quickly review ESG texts developed by the International Finance Corporation (IFC) to make SDG-integrated investments in emerging markets. The tool is able to identify more than 1,200 ESG risk terms, then performs a sentiment analysis to assign positive, negative or neutral sentiment to the risk terms, depending on the context.


International Research Centre
on Artificial Intelligence (IRCAI)
under the auspices of UNESCO 

Jožef Stefan Institute
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