Ethics of AI - An Engineering Perspective

Ethics of AI
An engineering perspective.
Francisco Curado Teixeira
(francisco.curado.teixeira@gmail.com)
ECA/UN Youth Innovation Bootcamp on Emerging Technologies
23 Feb 2021
Acknowledgments: Project RETIOT, DETI-IEETA, Univ. of Aveiro
(http://wiki.ieeta.pt/wiki/index.php/Institute_of_Electronics_and_Informatics_Engineering_of_Aveiro)

Given that AI is a powerful technology, we have a
moral obligation to use it well, to promote the positive
aspects and avoid or mitigate the negative ones.
Russell, S. J. & Norvig, P., 2020. Artificial Intelligence - A Modern Approach. Pearson
-----------------
If the feedback is built into a machine that cannot be inspected until the final
goal is attained, the possibilities for catastrophe are greatly increased. I should
very much hate to ride on the first trial of an automobile regulated by
photoelectric feedback devices, unless there were somewhere a handle by
which I could take over control if I found myself driving smack into a tree.
Norbert Wiener, 1964. God & Golem, Inc.

AI Ethics by Design - a Case Study
Problem Motivation.

Detection & Tracking of moving subjects in indoor /
outdoor spaces.
Applications (Public Surveillance & Security not contemplated)
Hospitals and Healthcare
● Monitor space occupation and movement of people
inside the building
● Detect and Alert for falls and inanimate people
City traffic management
● Traffic assessment & route planning
● Safety of pedestrians & bicyclists: warn for imminent
run-overs and collisions
See Credits list: [A]
See Credits list: [B]
See Credits list: [D]

Ethical Considerations.

Detection & Tracking of moving subjects in indoor /
outdoor spaces.
Ethical and operational requirements
Inclusiveness
● Account for diversity of subjects and situations
○ Humans, animals, vehicles, robots...
○ Apply to normal daily activities and detect abnormal behaviour
○ Account for people with disabilities
Privacy compliance
● Avoid acquiring data that permits personal identification
Safety
● Technology must be safe for humans and animals

Detection & Tracking of moving subjects in indoor / outdoor spaces.
Conventional solution: cameras
Advantages
● Well-established technology
● Low-cost H/W & CV software tools
Problems
● Privacy violation
● Illegal in most countries See Credits list: [C]

Technical Solutions.

Nonintrusive solutions: RADAR
Advantages
● Nonintrusive: no ID information in radar data
● Automotive Radar: compact, affordable, versatile FMCW tech.
● Safe for humans: 77-81 GHz
● Robust against environmental disturbances
● Appearing in mobile phones
Limitations
● No direct ‘picture’ acquisition
● Feature extraction highly dependent on radar hardware

Detection & Tracking of moving subjects in
indoor / outdoor spaces.
Radar working Principles and Techniques
● Cloud point of reflecting objects
● Rich data: range, angle, velocity,
intensity...
Problems
● Noisy raw data: clutter, multi-path...
● Complex feature extraction
AI Ethics by Design - Case Study

Radar working Principles and Techniques
● Cloud point of reflecting objects
● Rich data: range, velocity, intensity...
Problems
● Noisy raw data: clutter, multi-path...
● Complex feature extraction

Motion detection Principles and Techniques
● Point cloud data: range, angle, Doppler,
Intensity…
● Cluster points: range & Doppler (velocity)
● Use Doppler patterns for classification
● Complementary: signal intensity (RCS)
Radar geometry in 2D.
Source: [4]
Illustration of a person walking and a robot moving
and the respective point clouds. Source: [5]

Complexity
● Advanced signal processing (raw data):
ADC, 3D FFT, Clustering, Data-
Association, Tracking…
● Novel feature extraction techniques
Advantages
● Diversity of available feature types (e.g.: RCS)
and dimensionality (e.g. multi-frame)
Doppler pattern of mannequin or robot. Source: [5]
Doppler pattern of person walking. Source: [5]

Alternative solution: Low-resolution Thermal camera
Advantages
● Nonintrusive: no personal ID information
● Well-established, affordable technology
● Compact
Limitations
● Sensitivity to environmental disturbances
● Controversial in public spaces: similarity to Video & 3D cameras

Adopted Solution.

Anonymized Detection & Tracking of moving subjects in indoor / outdoor
spaces.
Preferred solution: RADAR-only sensing
Advantages of Radar Data
● Subject classification based on velocity (Doppler) patterns
● Multi-class classification: pedestrians, bicyclists, cars, animals...
Problems:
● Limited availability of AI tools and scarcity of labeled data-sets
● Huge amount of data supplied by the radar
● The majority of radar frames consist of noise and requires human inspection
● Radar frames are NOT easily interpretation by humans
● Data labeling is a highly time-consuming, expensive and tedious process
??
*!* ?!

Anonymized Detection & Tracking of moving
subjects in indoor / outdoor spaces.
Proposed: Training with Radar + Thermal data
Data acquisition and Labeling: Initial setup
● Radar board + Raspberry Pi CPU + camera
● WiFi + PC
● Using camera images to select radar frames allows
for automatic or supervised labeling of subjects
Final setup
● Low-res, low-cost thermal camera replacing RPi
camera

Anonymized Detection & Tracking of moving
subjects in indoor / outdoor spaces.
Proposed: Training with Radar + Thermal data
Deployment in Laboratorial / Academic environment
● Nonintrusive: no personal ID information
● Inform the community about the Experiment
Advantages
● Favorable context for system deployment
● Radar and Camera can be left shooting without
supervision

Results.

Anonymized Detection & Tracking of
moving subjects in indoor / outdoor spaces.
Model Learning: RADAR-only deployed on a
mobile robot
Multi-class Classification
● People
● Mannequin
● Person in wheelchair
● Robots

Model Learning: RADAR-only deployed on a
mobile robot
Results with initial datasets (25431 samples)
after hyper-parameter optimization
● Accuracy: Artificial Neural Network (ANN)
○ Doppler only: 92%
○ Doppler + RCS: 96%
● Accuracy: Random Forest (RF)
○ Doppler only: 92%
○ Doppler + RCS: 95%

Model Learning: RADAR-only sensing
Increasing the dataset
● Start with a small initial labeled dataset and
trained model (classifier)
● Apply Active and Cooperative Learning
to enlarge the labeled dataset and improve
the model
● Use synthetic or augmented data: physical
models; stochastic data-augmentation
Active
Learning
loop

Conclusions.

Anonymized Detection & Tracking of moving subjects in indoor / outdoor
spaces.
Anonymized detection, classification, and tracking
● It is possible to implement this type of system without using vision
● It is crucial to address ethical issues in the design phase
● Ethical considerations may condition the choice of H/W and S/W Tools
● Alternative technologies and methods may present increased complexity
and cost but may reveal more robust and versatile

Thank you
Acknowledgments: Project RETIOT, DETI-IEETA, Univ. of Aveiro
(http://wiki.ieeta.pt/wiki/index.php/Institute_of_Electronics_and_Informatics_Engineering_of_Aveiro)

References
1. Russell, S. J. & Norvig, P., 2020. Artificial Intelligence. - A Modern Approach. Pearson
2. Dubber, M. D.; Pasquale, F. & Das, S. (Eds.), 2020. The Oxford Handbook of ETHICS OF AI. Oxford University
Press
3. Ng, Andrew, 2018. Machine Learning Yearning. www.dbooks.org
4. Livshitz, M. 2018. Tracking radar targets with multiple reflection points. https://e2e.ti.com/cfs-
file/__key/communityserver-discussions-components-files/1023/ [Online accessed 13-June-2019].
5. Castanheira, J., 2019. Machine Learning Methods for Radar-Based People Detection. Master thesis. Universidade
de Aveiro.
Figures’ credits
A. By Micov at English Wikipedia, CC BY-SA 3.0: https://commons.wikimedia.org/w/index.php?curid=47564675
B. Dllu: https://commons.wikimedia.org/wiki/User:Dllu
C. Paweł Zdziarski, CC BY-SA 3.0 via Wikimedia Commons <http://creativecommons.org/licenses/by-sa/3.0/>
D. US News - Health: https://health.usnews.com/health-news/patient-advice/slideshows/14-ways-to-protect-seniors-from-falls

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