Ethical challenges within IoT, AI and ML solutions

1. IoT & Ethics
Erling Hesselberg

2. Internet of Things
Hype
• Large Scale
• Intelligence
• Sensing
• Massive amount of data
• Limited Energy
• Context awareness

3. 21/11/2019 3
IoT, Data & AI – End to End
IoT Sensors
Data Archive
External Data
Data managed and
secured in Data Lakes
Data Analytics, AI, ML & Deep
Learning
IIoT, BIM, SCADA
Data Visualization, AR/VR/MR

4. IoT at the “Edge”
• Process massive amount of data with
limited network bandwidth
• Connected Factory/Building/Oil Rig/Device
• Smart & effective Data Collection
• Privacy, Security & Offline support

5. 21/11/2019 5
Location and Humidity monitoring
Recover Nordic Group
Nordic market leader in damage control services to insurance
companies.
Full national coverage in Sweden, Norway, Denmark and
Finland.
More than 1,800 employees
Damage Control
•Water damage
•Fire damage
•Reconstruction
•Light demolition
Environmental Services
•Asbestos removal
•Mould removal
•Decontamination
•Sludge management
•Waste removal
•Oil spills
•Pipe inspection

6. 21/11/2019 6
Location and Humidity monitoring
• 10.000 Dehumidifier a ~ USD 5.000,- across Nordics
• Lack of control, 20% lost / 3 years
• Client/insurance companies requires specific pricing
• More advanced humidity measurement in concrete &
wooden structures.
• Labor-intensive to monitor and measure progress

7. 21/11/2019 7
Location and Humidity monitoring
• 10.000 Dehumidifier a ~ USD 5.000,- across Nordics
• Lack of control, 20% lost / 3 years
• Client/insurance companies requires specific pricing
• More advanced humidity measurement in concrete &
wooden structures.
• Labor-intensive to monitor and measure progress
• Cost reduction (manpower & material)
• Improved Customer Satisfaction (rating)
• Improved Employee Satisfaction
• Reduced loss & misplacement of
expensive equipment
• Reduced pollution

8. Machine and Deep
Learning
• Machine learning models start out dumb and get
smart by being exposed to data
• ML is algorithms and statistical models to
perform a specific task without explicit
instructions, relying on patterns and inference
• Using data to make business decisions based on
predicted outcomes
• Deep Learning is ML on steroids, used by
autonomous vehicles, content creation

9. The Cloud
• AI and ML algorithms needs data, a lot of data
• Local data centers available
• Large vendors invests massively for the “Cloud
Consumption” market

11. Ethical Issues
• Access to Data
• Discrimination and Inequality
• Informed Consent
• Potential for malicious use
• Privacy
• Transparency and Trust

12. IoT Security
Security Threats in Smart Cities
- Smart building
- Smart Transportation
- Healthcare
- Energy

13. IoT Privacy Threats
• Identification
• Localization and Tracking
• Profiling
• Life-cycle Transitions
• Inventory Attack
• Linkage

14. IoT Privacy Threats
• Identification
• Localization and Tracking
• Profiling
• Life-cycle Transitions
• Inventory Attack
• Linkage

15. IoT Privacy Threats
• Identification
• Localization and Tracking
• Profiling
• Life-cycle Transitions
• Inventory Attack
• Linkage

16. The Ethics Framework
for AI in Australia
Developed in partnership with government and industry
8 core principles that need to be adhered to:
1. Generates net benefits
2. Do no harm
3. Regulatory and legal compliance
4. Privacy protection
5. Fairness
6. Transparency and explainability
7. Contestability
8. Accountability

17. Whatever journey your organisation is on
to an optimised cloud enabled future,
don't take on the task alone.
Talk to the technology transformation experts at
Crayon and make your ambitions a reality.
Thank You!