Augmenting Humans with Technology — Part 3: With Great Powers Comes Great Responsibilities
In Part 1 we saw that AI/ML is a necessity. In Part 2 we saw that AI/ML does not just help understand or decide, it can also create, learn…
In Part 1 we saw that AI/ML is a necessity. In Part 2 we saw that AI/ML does not just help understand or decide, it can also create, learn, control, see and when coupled with mechanical capabilities it can act intelligently in the physical world.
Now, data and AI/ML are still at a very early stage in terms of governance, ethics and regulation. How businesses and governments use public and private data and how they make decisions will require much more transparency and safe guards that we see today.
Here is a technologist point of view on understanding our responsibilities as we help develop and deploy such solutions.
With Great Powers Comes Great Responsibilities
Everywhere all the time
Ubiquitous data capture technology and open source AI models make it easier for anyone to collect data at large scale, identify unique insights and patterns and create intelligent automation. And although it is more obvious in the digital world by recording users’ clicks and behaviours; it’s also becoming also much easier to digitize the physical world; and largely without people’s knowledge or specific consent.
Every device is a sensor: a lamppost, a car, and even buildings.
CCTV cameras are everywhere in the name public safety or private security. China and the UK have the most surveillance camera per inhabitants according to Statista chart below:
These camera feeds are not always well secured and can be accessed by third parties. Last year a group of hackers say “they breached a massive trove of security-camera data collected by Silicon Valley startup Verkada Inc., gaining access to live feeds of 150,000 surveillance cameras inside hospitals, companies, police departments, prisons and schools” (more details here).
Modern car engines have anywhere from 15 to 30 sensors to keep everything running properly. These sensors control everything in the engine for optimal performance. In total, there are over 70 sensors in a modern vehicle throughout the whole car.
Your phone alone is packed with many sensors: proximity sensor, ambient light sensor, accelerometer, gyroscopic sensor, magnetometer, microphone, camera(s), fingerprint sensor and barometer.
Israeli firm NSO Group who created Pegasus, the zero click spy software used by a long list of countries supposedly to protect against “enemies”: it can read and extract all your phone data and use your location, camera and microphone remotely without your knowledge.
A few months ago, VIVO presented a new concept of phone including a drone with a 200MP camera: although it looks like a pretty cool gadget it means more data can be collected easily by anyone without anyone’s knowledge or consent. See the demo video here.
Think about it: 90% of all the world data was created in the past 2 years!
Systematic collection of data whether it belongs to me or not; is raising many questions in terms of regulation, jurisdiction, sovereignty, rights to privacy. Additionally, free powerful open source AI models are making such technology simpler and more affordable to implement every day. It means new innovations and benefits but also new ways it could be weaponized.
Data = Asset or Liability?
You have been told for years now that data is new the gold, data is the new oil. But in fact, for most companies, data is a huge liability.
Forrester found that between 60% and 73% of data in a company is never used strategically, and research by Carnegie Mellon University (source: Forbes) has found that 90% of the data in an organization is “dark data.” Dark data is data which is acquired through various computer network operations but not used in any manner to derive insights or for decision making
Data is a business asset only when it is consciously captured and deliberately managed; if not, data can become a huge liability that threatens the very existence of the firm (Prashanth Southekal)
The most successful companies of the past 20 years have made of business of capturing everything, from everyone in full with limited to no safe guard or consideration for privacy or ethics. In fact, Facebook, Google, Amazon, etc have engineered a model that systematically both generates and captures behavioural data with the goal of reselling to brands the right to influence consumers. There is a say that if the product is free it means you are the product!
The value of data is also become a challenge as data is always bias: Baeza-Yates [Ricardo Baeza-Yates, “Bias on the Web”. Communications of the ACM, June 2018, Vol. 61 №6, Pages 54–61.] provides several examples of bias on the web and its causes. He points out that:
7% of users produce 50% of the posts on Facebook.
4% of users produce 50% of the reviews on Amazon
0.04% of Wikipedia’s registered editors (about 2000 people) produced the first version of half the entries of English Wikipedia.
The economics of systematic data collection, storage, transformation and analysis for what cause and purpose are key: the more data owned the biggest target for cyber criminal the more exposure and risk of breach.
you can’t steal what does not exist
The moment the data is captured and stored, one has to assume someone will attempt to steal it. So it needs to be secured and exploited in full for the purpose or mandate of the business; or else it will become a huge liability. Increased risks for businesses and governments who collect blindly a lot of data: more data = more exposure = higher liability!
Finally the bias of AI/ML models from the data it was trained with could ultimately do more harm than good — and at scale!
Augmenting vs Replacing
Augmentation does not mean replacement; but it will displace tasks and jobs: managing such change and softening the impact across organization and society will require much explaining, support and time.
Importantly, the massive potential of AI/ML can be used to help us solve our biggest challenges ahead: a sustainable future for the generations to come: climate change, water scarcity, food supplies, peace, energy consumption, etc.
In short:
it is getting easier and cheaper to collect data in full (with or without knowledge or consent) and extract sophisticated insights
data is bias and unbalanced by design of the platform that generates them: so few people generates content vs consume it
accumulated is a huge liability for most businesses that don’t use it as it needs to be secured
AI/ML should be used to augment our capabilities vs replace; helps us solve our biggest problems ahead.
Not Just About Tech
To finish on a positive note; progress and innovation is not all about technology: it’s about looking at a problem in a very different manner:
Albert Einstein said: “If I had an hour to solve a problem I’d spend 55 minutes thinking about the problem and five minutes thinking about solutions.”.
That’s all folks!
Damien