Spain

In the past few years, several large companies have published ethical principles of Artificial Intelligence (AI). National governments, the European Commission, and inter-governmental organizations have come up with requirements to ensure the good use of AI. However, individual organizations that want to join this effort, are faced with many unsolved questions. This paper proposes guidelines for organizations committed to the responsible use of AI, but lack the required knowledge and experience. The guidelines consist of two parts: i) helping organizations to decide what principles to adopt, and ii) a methodology for implementing the principles in organizational processes. In case of future AI regulation, organizations following this approach will be well- prepared.

ECAI 2020

Towards organizational guidelines for the responsible use of AI

ai guidelines

responsible ai

ai ethics

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

poster

Robot self/other distinction: active inference meets neural networks learning in a mirror

In this video, we introduce through 8 questions the Human Rights approach to AI regulation and comment its superior societal performance compared to the regulation outlined in the European Commision white paper on AI. https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf  

A wide Human-Rights approach to Artificial Intelligence regulation in Europe

The modeling of a new generation of agent-based simulation systems supporting pedestrian and crowd management taking into account affective states represents a new research frontier. As in the case of any study of pedestrian dynamics, adding an affective component implies the rigorous design of experimental protocols and data acquisition sets. The integration of multi-modal signal sources considering both data coming from physical activity and uncontrolled reactions related to affective responses provides new perspectives to study pedestrian dynamics and pedestrian interaction with traditional vehicles as well as with autonomic and autonomous transportation systems. The designed in-vivo experimental protocol devoted to the collection of movement and physiological data as reliable stress indicators during walking and road crossing, and the related analysis will be illustrated.

Experimental approach to study pedestrian dynamics towards affective agents modeling

Artificial Intelligence (AI) is omnipresent today, but, what is AI capable of doing and where are its boundaries? Benchmarks, competitions and challenges are behind much of the recent progress in AI, but the dynamics of rushing breakthroughs at the expense of massive data and compute has led to a more complex AI landscape, in terms of what can be achieved and how. As a result, policy makers and other stakeholders have no way of assessing what AI systems can do today and in the future. In this paper, we identify a series of problems to track and understand what AI is capable of. Then we present the AIcollaboratory, a  data-driven framework to collect and explore data about AI results, progress and ultimately capabilities, which is being developed in the context of AI Watch, the European Commission knowledge service to monitor the development, uptake and impact of AI in Europe.

Tracking the Impact and Evolution of AI: TheAIcollaboratory

Lung nodule detection and segmentation play a critical role in detecting and determining the stage of lung cancer. This paper proposes a two-stage segmentation method which is capable of improving the accuracy of detecting and segmentation of lung nodules from 2D CT images. The first stage of our approach proposes multiple regions, potentially containing the tumour, and the second stage performs the pixel-level segmentation from the resultant regions. Moreover, we propose an adaptive weighting loss to effectively address the issue of class imbalance in lung CT image segmentation. We evaluate our proposed solution on a widely adopted benchmark dataset of LIDC. We have achieved a promising result of 92.78% for average DCS that puts our method among the top lung nodule segmentation methods.

Region Proposal Network for Lung Nodule Detection and Segmentation 

Improved forecasting of blood glucose could aid in the management of diabetes. Recently proposed neural network architectures that use stacked fully connected layers with residual backcasting have achieved state-of-the-art performance on benchmark time-series forecasting tasks. Though promising, previous work ignores opportunities for additional supervision, and the use of fully connected layers fails to account for the temporal nature of the signal. Here, we propose a new architecture that builds on previous work by learning to forecast gradually in stages or blocks. Our updates include replacing the fully connected block structure with a recurrent neural network and adding additional losses to provide auxiliary supervision. In addition, we leverage important context in the form of additional input signals. Applied to the task of glucose forecasting, we find that each of these modifications offers an improvement in performance. On the task of predicting blood glucose values 30 minutes into the future, our proposed approach achieves a mean rMSE of 18.2 mg/dL, versus 21.2 mg/dL achieved by the baseline. These improvements get us closer to predictions that are reliable enough to be used in CGMs or insulin pumps to manage diabetes.

Deep Residual Time-Series Forecasting: Application to Blood Glucose Prediction

"Artificial intelligence-driven Chatbot for Astronauts by
 taking advantage of Natural Language processing with the support of healthcare professionals/ doctors 
These Chatbots created with the expert advice of doctors can learn and evolve over a period of time and answer  medical questions from a pre-trained Database which can be updated periodically
 Astronauts would be able to list down their symptoms and the Chatbots could give medical support by suggesting some medicines or other remedies which helps in diagnosis and treatment of simple medical conditions without much delay
"

Artificial intelligence assisted Chatbot for Astronauts

StaRAI - Semantics and Symmetries in Exact Lifted Inference

Onboard and ground-based automated scheduling for the Mars 2020 Rover Mission

"Sensors are multiplying on machines, networks and living things. Reasoning and extracting knowledge from this huge amount of data is among nowadays challenges. In data- based diagnostic applications, large amounts of data are often available but a key issue is that data remain unlabelled because labelling would require too much time and imply prohibitive costs. The different situations, e.g. normal or faulty, must hence be learned from the data. Clustering methods, also qualified as unsupervised classification methods, can then be used to create groups of samples according to some similarity criterion. The different groups can supposedly be associated to different situations.
Numerous algorithms have been developed in recent years for clustering numeric data but these methods are not applicable to qualitative (categorical) data. However, in many application domains, qualitative features are key to properly describe the different situations. This paper presents DyClee-C, an extension of the numeric feature based DyClee algorithm to qualitative data. DyClee-C is applied to two data sets: a soybean data set to diagnose the disease soybean plants and a breast cancer data set to assess the current diagnosis in terms of recurrence events and prognose possible relapse."

Downloads

Next from ECAI 2020

Robot self/other distinction: active inference meets neural networks learning in a mirror

Similar lecture

Principles to Practices for Responsible AI: Closing the Gap