This page is meant to be an accessible entry point to what is meant by “Verifiable AI”, and to the resources on Verifiable AI that are available in the AI4EU AI on-demand platform. This guide is part of the broader AI4EU scientific vision on “Human-centered AI”, available here.
A question frequently asked is “Why are there no self-driving cars on our streets?”
This is mainly due to the fact that we are not yet able to verify the various AI components needed to autonomously drive a car in a reliable way, that is, as safely as an average human driver. A car is an example of a safety-critical system in which an error occurring in a component, be it software, computing hardware, physical sensor, physical actuator or energy source, can lead to an erroneous system behaviour that can in turn injure or kill a person.
This is the case for all transportation systems (airplanes, trains, boats, etc) as well as medical systems, energy production and distribution systems, heavy industry control systems, defense and security systems. In these industries, which currently use very little AI, dependability is ensured by applying a completely different, far more cautious and costly engineering method and lifecycle than the one used for developing non-critical AI applications such as e-commerce voice ordering assistants and product recommender systems. State of the art dependability engineering generally starts by explicitly specifying, as systematically as possible, with the rigor of mathematical formalisms allowing automated verification, all the expected behaviors and properties of the overall system in all the situations that it may encounter in operations. These behaviors are then decomposed into sub-behaviors, and at each refinement step, systematic, often fully automated verification of the expected behavior and properties is carried out. Those verification steps are also thoroughly documented so that they can be audited by a third-party dependability certification agency. Critical systems cannot be deployed until they are certified dependable.
Verifiable AI primarily investigates how to extend, and in some cases rethink, dependability engineering to be able to certify AI-powered critical systems.
Consider again the task of driving a car. It is challenging for an AI because it involves integrating many sub-tasks such as multi-sensor perception, navigation, avoidance of simultaneously moving obstacle in directions and speeds that must be correctly anticipated, integrating reflex and deliberative action choice and doing all that under real-time constraint (e.g., taking two more seconds to choose between braking and swerving can make both actions ineffective to avoid a fatal accident).
How can we formally specify what the output of a perception component for a self-driving car should be for each possible given set of pixels that the car might receive as input during its lifetime? The current approach is to specify those tasks informally by a large amount of example images, each one with its classification label, and use algorithms to let the AI machine learn an image classifier from the examples. The challenge is to obtain a set of images that are representative of the ones the driving AI will encounter in its lifetime. How to label such an image dataset with the correct classification without massive human labor? How to guarantee that a small detail in the image will not result in a dangerously wrong classification. The current approach has been shown to misclassify street signs when even a very small part of it has been spray painted or lost its paint.
How should the concept of “a pedestrian” be defined from pixels? How should the action of “crossing” be defined? Or rather, how can they be defined in a way that it is sufficiently precise and universal to support correct interpretation verification up to some probability threshold prescribed by a dependability certification standard? The question is how to recognize that despite widely different contours, a walking pedestrian and a wheelchair pedestrian are both pedestrians, as well as how to also recognize as pedestrians a running child, an electric unicyclist or a skater in a low squatting position?
Can a driver’s perception be dependably specified as the task of detecting an object in an image or is there more to it? Should not the perception component learn to interpret videos rather than static images? How to recognize for example that a pedestrian is currently walking briskly on the sideway, with the eyes fully focused on a cell phone and with headphones on, and (s)he might very well start crossing the street automatically before noticing that the light is green for cars and will not hear a horn anyway? Note the jump in the complexity in visual detail perception and human behavior modeling required to perform this kind of anticipation that is crucial to prevent accidents. How to verify that a self-driving car will behave as safely as an average driver who performs such detailed analysis and anticipation routinely without thinking about it. These are examples of the difficult open research questions that verifiable AI currently investigates.
Below is a list of useful resources on Verifiable AI currently available on the AI4EU platform. The list is by no means complete, but it can be a good starting point: any contribution or suggestion for further content is well accepted!
Background knowledge on Verifiable AI
- K1: We maintain an extensive survey on the current research in the field of Verifiable AI. This is a living document, updated by the researchers in AI4EU every six months.
- Link to the current version: [coming soon]
- K2: You can read a quicker, two-page overview of what we mean by Verifiable AI here.
Tools for Verifiable AI
- T1: VERIFAI (Verifiable and Explainable RIsk Forecasting Artificial Intelligence): A framework that contemplates verification steps during both design and run time, for a machine learnable model. In specific, at design-time, it is able to check for invalid end states, non-determinism and accordance with a priori knowledge. Online verification focuses on verifying the confidence of a classification (forecasting) for a specific instance, based on a tailored vicinity specification that is explored by the model checker to verify the closeness to model decision boundaries. This last phase of verification is also considered as ensemble strategy, in a scenario of combining more than one classifier.
Data sets for Verifiable AI
The following data sets, currently on the AI4EU platform, are used in research on artificial intelligence and machine learning for health, and provide suitable case studies for Verifiable AI.
- D1: EEG P300 Dataset: Data from 15 participants, with 7 sessions each containing complete EEG recordings of a feasibility clinical trial that tested a P300-based Brain Computer Interface to train youngsters with Autism Spectrum Disorder to follow social cues.
- Link to the dataset: https://ai4eu.dei.uc.pt/eeg-p300-dataset/
- D2: Respiratory Sound Dataset: 920 annotated audio samples from 126 subjects containing respiratory cycles with crackles, wheezes and both crackles and wheezes. The respiratory sounds presented in this dataset contain relevant information about the structure and function of the respiratory system and are generally classified as normal or adventitious.
- Link to the dataset: https://ai4eu.dei.uc.pt/respiratory-sounds-dataset/
- D3: Cardiac Function Dataset: Data collected from 68 volunteers (healthy or suffering from various cardiovascular diseases) consisting of bio-signals (ECG, PPG and PPG) and echocardiography images that can be used to assess cardiac function indexes such as systolic and diastolic time intervals, stroke volume and cardiac contractility.
Link to the dataset: https://ai4eu.dei.uc.pt/cardiac-function-dataset/
Case studies of Verifiable AI
Groups related to Verifiable AI
- G1: The main group for Verifiable AI on the AI4EU platform:https://www.ai4eu.eu/group/verifiable-ai
Researchers working on Verifiable AI
- R1: Salvador Abreu, University of Evora, Portugal and Université Paris Panthéon-Sorbonne, France.
- R2: Raul Barbosa, University of Coimbra, Portugal
- R3: Henrique Madeira, University of Coimbra, Portugal
- R4: João Martins, University of Coimbra, Portugal
- R5: Annika Mütze, Fraunhofer IAIS, Germany
- R6: Jacques Robin, Université Paris Panthéon-Sorbonne, France.
- R7: Florian Zimmermann, Fraunhofer IAIS, Germany.
Note: if you want to add a software resource, data set or researcher to this document, you first need to make sure that they are available in the AI4EU platform, e.g., by publishing the software.
This document is published under the Creative Commons License Attribution 4.0 International (CC BY 4.0). It should be cited as:
- Jacques Robin and Florian Zimmermann (editors), “A simple guide to Verifiable AI”. Published on the AI4EU platform: http://ai4eu.eu. June 24, 2020.