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Why Is Building a Trust Bridge Critical at Technology Inflection Points?

A technology inflection point is where users move from an existing technology to a new technology. It takes a leap of faith for early adopters to make this transition and as the number of users adopting this new technology increases, there is a tipping point at which the new technology completely overwhelms the old one. A trust bridge is a mechanism that makes it easier for early users to adopt new technology. Understanding the underpinnings of a trust bridge will help you navigate an inflection point and drive adoption of the new technology.

Technology Inflection Points & S-Curves

The automobile moving from gasoline power to electric power is an example of one such transition (Case A on the framework mindmap). We take it for granted that we will not run out of fuel and be stranded with gasoline based cars, because of the numerous gas stations all over; the same is not true for electric cars which have a limited range and electric charging stations are not as common as gas stations. But the availability of accurate maps with locations for these electric charging stations, the accurate determination of how much charge is left in the car and the confidence that we can make it to the next charging station before the battery runs out, helps us make this transition (this is the trust bridge).

Ownership of cars is also undergoing a major transition (Case B). Earlier it was necessary for a person to own a car to use it regularly. Now with the availability of ride hailing services, it isn’t necessary to own a car. I have seen this with my own aging parents. My father has reached a stage of life where his ability to drive a car has deteriorated; So the transition to a ride share platform was great – available when he needed it without the fixed costs of maintaining a car and a driver. The trust bridge for him was the ability to see in real time the cars available near him and the confidence that he would have a car when he needed one; and for me it was to see in real time where he was on his ride, since he lives half a world away.

Another trust boundary on our horizon is the transition to self driving cars (Case C). Imagine how difficult it is for a majority of people to let go of control while driving, to a machine that will have your life and safety in its power while driving you from point A to B. The trust bridge in this case will be objective data on how much better automomous interconnected machines are at avoiding accidents than humans and the tipping point will be when it will be difficult for the die hard drivers who have refused to adopt autonomous cars to get automobile insurance. 

Think about our transition from records, to cassettes, to CDs and DVDs and finally to streaming content (Case D). The last transition was the most difficult because it has meant that we do not have physical possession of the copyright content that we bought – but rely on a belief (trust bridge) that we could stream it when ever we want from the cloud and we can trust an Apple iTunes Store or an Amazon store to remember that we have purchased it and can access it anytime. It also implies that we trust these cloud stores to be available when we need them.

Companies  moving from owned and managed data centers to shared cloud providers (Case E) is another example. The advantage of using shared cloud providers is that we have the ability to elastically expand and contract capacity, without a huge capital investment and only pay for what we use; Secondly cloud adoption has allowed for rapid evolution in container based application deployment where we have become completely oblivious of underlying server architectures, operating systems and technology stacks using containers like Docker and container orchestration options like Kubernetes; Thirdly with the rapid evolution in managing large amounts of data, a number of ML and AI frameworks are available as plug and play options. In the owned datacenter paradigm, bringing in such frameworks would have resulted in a whole new integration and onboarding project. The trust bridge in this case has been the rapid strides in cloud security infrastructure and capabilities for public, private and hybrid cloud offerings and the assurance of network, container, process and data isolation between clients.

In the Healthcare domain, introducing new products/therapies is also an example of an inflection point (Case F). The trust challenge is knowing the benefits and risks of a new treatment or therapy in the long term. Governments put in regulations to slow down the approval and adoption of any new therapy or drug until they make it through Stage I – IV trials. Once the new therapy has cleared these hurdles, adoption is a lot easier because people have faith in the regulatory process (trust bridge) to ensure that the new product is safe and effective before entering the market. 

Similar is the case for abstract machine learning models and autonomous systems (Case G on the map). Some of the esoteric ML models may not be well understood or tested for all permutations and combinations. This leap becomes even more difficult in my experience when we deal with self learning or autonomous machine learning systems. Rather than letting adoption be a leap of faith for your users, it is critical that we understand user psychology – their fears & beliefs and  build the trust bridge to facilitate adoption. I suggest the following process for evolving ML models (the model validation step will be the trust bridge here): 

Trust Bridge when developing ML Models

I have used the following framework to build my case for change. My experience has been whenever my team has focused on building a trust bridge in technology inflection cases or projects with significant change, the results have been very positive. I would love to have feedback on what folks have found useful when driving change.

Explore this Framework

PostScript:

Given the momentous changes we as a society are staring at in the near future like climate change, evolution of AI and genetic engineering like CRISPR, we need to make taking these trust leaps easier to keep up with the rapid changes in our environment, ideas, processes, products and services. And this is where a Change Management Practice that focusses on understanding user psychology – beliefs, desires and fears and being able to build the “Trust Bridge” will be critically important. 

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Evolution Of AI And The Trust Frameworks We Need to Support It

We have seen evolution of AI systems from the simple to the more complex: Going from simple correlations and causations, to model creation, training and advanced prediction to finally unsupervised learning & autonomous systems.

Trust Models Required At Each Stage Of Evolution

Human society has been seen to be comfortable with assigning accountability to one of its own i.e. a human actor who creates, authors or mentors these models and can assume accountability & responsibility. If you trace the evolution of our justice systems from the time of Hammurabi (sixth king of the First Babylonian Dynasty, reigning from 1792 BC to 1750 BC), to the modern ones in nation states today, we seem to accept good behavior within a well defined system of laws and rules, and digression from these attract punishment which is meant to drive compliance. 

Unknown author Mbzt, P1050763 Louvre code Hammurabi face rwk, CC BY 3.0

But will this always be true? Do we need new trust models and enforcement mechanisms?

Some Questions To Answer Before Advent Of Completely Autonomous Systems:

How do you program ethics?

Morals are the objective transcendent ideals we base our ethics upon. Jonathon Haidt in his exploration of the conservative and liberal morality describes 5 key traits – harm, fairness, authority, in-group and purity. Per his TED talk, liberals value the first two and score low on the other three, while conservatives value the latter 3 more than liberals.

Ethics are the subjective rules by which we govern our behavior and relate to each other in an acceptable manner. So which of these moral principles and in what measure should our ethical rules be based upon? And who chooses?

These ethics rules determine the system’s behavior in any situation and thus form the basis for the trust system we will operate upon with the autonomous system. (See the definitions of trust in my earlier post here)

Would the creator of a model be held responsible for all its future actions?

Think about an infant that is born. He/she usually has a base set of moral frameworks hard wired into the brain and it is life experiences that shape how that model further develops, what behaviors are acceptable in society, which ones are not, what’s considered good vs. evil etc. The only thing that a creator can be held responsible for is the base template that he inputs into creating the autonomous AI system. Anything that is learnt post birth would be a part of the nurture argument that would be very difficult to assign accountability for. 

Can you set up a reward and punishment system for AI models?

If we consider an AI system to be similar, how do you provide a moral compass to it? Would you expose it to religion (and which one?) to teach it the basics of right or wrong or set up reward and punishment systems to train it to distinguish desirable vs. undesirable behavior. And again who determines what is desired and what is not – is it us humans or do we leave this up to the autonomous AI system.

Who decides on when and how we go to Autonomous AI?

When would we as a society be ready to take the leap? There are a number of thought leaders who have warned us about this including Stephen Hawking and Elon Musk. Are we ready to heed those warnings and muzzle our explorations into truly autonomous systems or is this an arms race that even if we bore restraint, someone somewhere may not act with the same constraints that we did…and finally was the purpose for us as a species was to develop something more intelligent than us that is able to outpace, out compete and eventually sunset our civilization?

I guess only time will tell, but meanwhile it is important to at least model ethical rules as we know it (similar to Isaac Assimov’s three Laws of Robotics – A robot may not injure a human being or, through inaction, allow a human being to come to harm. A robot must obey orders given it by human beings except where such orders would conflict with the First Law. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law) into systems and autonomous programs that we create but realize that our biases, desires and ambitions will always be a part of our creation…

A Possible Way Forward…

We need a framework to establish certain base criteria for evolution of AI – something that will be the basis of all decision making capabilities. This core ROM which cannot be modified should form the basis of trust between humans and autonomous AI systems.

This basic contract is enforced as a price of entry to the human world and becomes a fundamental tenet for trust between us humans and autonomous systems allowed to operate in our realm.  

As long as humans trust the basis of decision making upon these core principles (like Assimov’s three laws of robotics described above) we will operate from a position of mutual trust where we should be able to achieve a mutually beneficial equilibrium that maximizes benefits all around.

Given the CRISPR announcement today about two babies being born with their genes edited using CRISPR Cas9, its all the more urgent for us to establish this common framework before the genie is out of the bottle… 

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Trust In The Digital World

With the recent news of numerous data breaches and companies caught with questionable business/technology practices for managing customer data (which may seem to be in breach of public trust); the question that comes to one’s mind is how important is “Trust”? How much should you invest in maintaining trust in a proactive manner and what is the cost of “breach of trust”? How do you recover from a breach? What foundational elements of trust are damaged from such a breach? And borrowing a marketing slogan from the MasterCard Priceless campaign, is it fair to say – “Not having your company’s data breach on the front page of the Wall Street Journal: Priceless”.

Let’s look at some basics…

What is Trust?

A few definitions that I have found most relevant –

Paraphrasing, social psychologist Morton Deutsch: 

Trust involves some level of risk, and risk has consequences with payoffs being either beneficial or harmful. These consequences are dependent on the actions of another person and trust is the confidence that you have in the other person, to behave in a manner that is beneficial to you. 

Patricia Jenkinson, Professor of Communications at Sacramento City College defines the various overlapping elements of trust as follows –

♦ Intent to do well by others

♦ Character – being sincere, honest and behaving with integrity

♦ Transparency – open in communication with others and not operating with hidden agendas.

♦ Competence / Capacity – ability to do things

♦ Consistency / Reliability – keeping your promises, meeting your obligations

Trust is important for us to feel physically and emotionally safe. With more trust, we can effectively and collaboratively work together towards common goals by sharing resources and ideas. When trust is high, we openly express thoughts, feelings, reactions, opinions, information and ideas. When trust is low, we are evasive, dishonest and inconsiderate. 

There are two basic types of trust: Interpersonal with regards to one’s welfare with privileged information and relational commitment and task oriented with its dimensions of ability to do the task and the follow through to finish the task.

Evolution of Trust

Yuval Noah Harari in his book Sapiens, describes “cooperation in large numbers” to be one of the key factors for human success over other species (which were physically stronger and much more adept at surviving the extreme elements of the earth’s environment). Trust allowed us to cooperate in large numbers and collectively gave us the ability to accomplish tasks beyond the capacity of a single individual. Chimpanzees also cooperate, but not in large numbers like humans which limits the capability of the clique.

Trust Platforms

With the advent of the digital age and large virtually connected social networks, our paradigms of digital trust have changed substantially. Rachel Botsman of Oxford University in a series of TED talks describes the transition from hyperconsumption to collaborative consumption, the evolution of trust from local to institutional to distributed.

This evolving distributed trust platform has three foundational layers (described as the Trust Stack by Ms. Botsman) – which allows us to trust relatively unknown people –

♦ Trust the Idea

♦ Trust the Platform

♦ Trust the other user

When there is assurance of accountability for a users’ actions as enforced by the platform (which has the ability to restrict future transactions by that user for bad behavior), there is implicit trust that the platform lends to transactions between complete strangers such as a transaction on the Uber or Air BnB platforms. One of the illustrative examples is how people behave differently (say cleaning up their room) when staying at a hotel vs. with an Air BnB host. In the former the expectation is that the institution will not hold them accountable for bad behavior while in the later the platform enforces this through mutual feedback and social reputation for both the guest and the host enhancing trust and ensuring good behavior.

Per Ms. Botsman, this is just the beginning, because the real disruption happening isn’t technological. It is fundamental to the way we will transact in the future. Once a trust shift has happened around a behavior or an entire sector, you cannot reverse this change. The implications here are huge.

A Simple Experiment

Daniel Arielly, a professor at Duke, in his TedX talk at Jaffa, describes a very nice social experiment. Suppose in a model society, everyone is given $10 at the beginning of the day – if they put this money in the public goods pot, then at the end of the day everything in the pot is multiplied by 5 and equally divided.  So for example, if 10 people in a society were given $10 every morning and they put everything in the public goods pot, the pot would have $100, when multiplied by 5, would result in $500 at the end of the day and every one would get $50 back at the end of the day and everyone is happy. If the next day one person cheats, everyone except that person put in the $10 at the beginning of the day, there would be $90 in the pot. At the end of the day, the pot would have $450 and everyone would be returned $45 back. Everyone would notice that they did not get the full $50 back and the person who betrayed the public trust has $55. Dan’s next question was – what would happen the next day – no one would contribute to the public pot. His point being most trust games play out as a prisoners delima with a very unstable equilibrium where everyone contributes/cooperates and a stable equilibrium where no one contributes/cooperates. To maximize overall benefit, one has to ensure that everyone cooperates, and a single defection would ensure the overall benefit from cooperation going down. The moral of the story is that “Trust” is a public good, and an incredible lubricant for society.  When we trust, everyone is better off, and when people betray the public trust, the system collapses and we are all worse off. 

In Conclusion

A number of companies have used transparency and a persistent reputation as a mechanism to keep people from betraying the public trust for example eBay, Air BnB, Uber etc. The cost of betrayal on these platforms is that the betrayer would not be able to transact on the platform anymore because of a hit to his/her reputation. 

Also adding punsihment and revenge to the mix also changes the game. A reputation for being revengeful will prevent the first player defecting. The justice system and police are a common example of using punishment to keep the trust in society.  

For companies that build trust platforms that allows for even strangers to transact, a betrayal of trust by the platform is much more damaging than a transgression by a single user on the platform. With such a breach there is the real possibility of users moving to the extremely stable equilibrium of not cooperating & thus abandoning the platform (loosing network scale is an existential threat) and moving them back to cooperating and using the platform is a herculean task.

No longer can we rest on our laurels by just calling ourselves trust worthy without redesigning our systems, process and people to be transparent, inclusive and accountable.

So remember, protect the idea first, then the integrity of the platform and then individual issues or breaches that may impact trust. Once trust is broken, it’s very hard to rebuild or repair.