Differential Technology Development is originally defined as an innovation framework that calls for preferentially advancing risk-reducing technologies over any alternatives (source). The principles of DTD can be applied in two ways: before or after the proliferation of a risky technology.
The values and ideas at the core of DTD are not new, but it can be thought of as a useful framework that groups together existing and new actions taken toward risk reduction. One example where risk-reducing actions were implemented before the proliferation of a risky technology occurred within the energy portfolio of the province of Ontario. Ontario converted what was once the largest coal power station in the world into a solar power station in 2019. The Ontario government, simultaneously, subsidized nuclear energy and renewables through a concerted effort with private companies. As of 2019, 92% of Ontario’s energy originated from zero-carbon sources, while still being able to export a net amount of 13.2 TWh that same year.
Proactive examples include post-quantum cryptography (PQC). Since quantum computers have been proven to, in theory, have the capacity to break current encryption methods, efforts like PQC have been in development to create encryption methods that are more resistant to attacks by quantum technologies (source). DTD of this form comes with its own challenges like anticipation, which I will discuss in a later section.
Traditional approaches to innovation frameworks taken by, for example, a venture capital firm, tend to focus mainly on the commercial viability of an idea with little consideration of risk-reducing potential. If that project happens to be risk-reducing it is seen as good, but it is usually neither a prerequisite nor a goal.
An approach to innovation grounded in DTD places risk reduction as an explicit goal. Since not all of these efforts may yield profits, it sometimes becomes necessary to engage actors who do not require commercial viability to aid in realising a project (e.g. philanthropists). However, the creation of commercial viability itself could also be used to incentivize risk-reducing tech (example).
This is not to say that existing actors are falling short. Many of the institutions I will be mentioning have made great strides in supporting risk-reducing technologies — several of these examples would not exist otherwise! DTD builds upon much of that previous work and aims to combine it into a new tool that such stakeholders can utilize.
DTD is also relevant for potentially dual-use technologies, which are technologies that may be used in both civilian and military applications (sources 1 and 2). An example of dual-use is the development of improved methods of drug delivery to the lungs — this advancement could provide better care for people with asthma, but may also allow for more efficient delivery of anthrax (source). However, it is important to bear in mind that biasing such technologies in one direction can be difficult in practice.
What parties may partake in DTD?
My guess is there are two distinct stakeholders which I will refer to as Regulators and Operators.
Institutions or groups. (Government agencies (e.g., NIH), international groups (e.g., UN), philanthropies)
People who build technology or contribute to research and development on a technical basis (e.g., Engineers, researchers, etc.)
Coordination between these groups is key to best implementing DTD (e.g. not funding risky research only works if everyone decides not to do it) but is not necessary in all cases.
DTD also requires significant domain expertise. Understanding what may be risky in drug development vs. transportation vs. AI vs. energy is vastly different, let alone implementing what the solutions might be. As such, the best Regulators are probably going to also be previous Operator types for this reason, or are deeply informed by them. A good example of this is Kevin Esvelt, a professor at MIT who is working on risk-reducing solutions specific to his domain, but also generates papers like these that could be relevant to regulators.
What does risk-reducing tech look like?
Operators and Regulators have historically taken many steps to decrease risks and advance risk-reducing technologies. However, balancing trade-offs and identifying concrete deliverables can be hard to do. Facing these tough questions through a DTD lens could be helpful and serve as a starting point (Sanbrink et al).
These technologies mitigate negative societal impacts by modifying existent risk-increasing technologies. Some examples: PALs for nuclear weapons, catalytic converters in car exhausts.
These are technologies that aim to directly decrease risk propagated by risk-increasing technologies. (e.g. Far-UVC Light Sterilisation).
Alternatives to risk-increasing technologies that produce less risk with a similar benefit. (e.g. nuclear power plants as an alternative to coal).
Risk reduction may also necessitate governance, protocols, conventions, and even workshops. More on what non-technical solutions may look like can be found in the DTD Toolkit.
On Anticipation and Windows of Vulnerability
One way I like to think of DTD involves a focus on decreasing or completely eliminating windows of vulnerability. In other words, DTD is about “getting ahead of the curve” and setting the path straight. This is easy to know what to do in some cases (i.e. stockpiling mRNA vaccines that can adapt to new viruses quickly) but difficult in others (i.e. space governance).
Such a challenge could come in two forms. The first is when there are risks that are well understood. Climate change is a good example of this, in which DTD calls for the prioritization of proportional interventions. Implementation of such solutions usually involves providing incentives (e.g. carbon taxes, public procurement). This relates back to DTD being implemented after the proliferation of a technology.
The second is concerned with unknown risks or anticipating otherwise unseen risks. It is inherently difficult to come up with an example here but foresight, anticipation techniques, and forecasting can help. This sort of DTD is implemented before a technology proliferates. However, incorrect predictions could lead to stalled progress and stunting of other technologies. For example, talent or funding may be diverted from fields that may turn out to have no negative effects or indeed may contain positive impacts long term. Fields do not need to be explicitly defunded for this to happen either. For example, it may be the case that increasing funding for one field implicitly diverts funding from another domain that may not have any negative impacts itself, or may have more positive impacts than the one with a funding increase.
I tend to believe employing differential treatment of technologies is worth doing despite these potential shortcomings of prediction, especially in the cases of well-defined risk (e.g. climate change). This comes down to considering how much larger an effect these catastrophes could have in comparison to attempts to mitigate them. This, though is also not a reason to avoid exploring ways near-term prediction could be done better, especially within the context of scientific breakthroughs. Below are some thoughts on exercises that could make this sort of “field strategizing” better.
Retroactive Scientific Roadmapping:
One way to develop concrete ideas of what interventions might have promise is to think retroactively from a specific scenario. For example, “We had an engineered pandemic kill around 1 billion people sometime in the next 10 years. What went wrong?” and work backwards from there using a predetermined taxonomy that is also mutually exclusive and collectively exhaustible (MECE).
In doing this retroactive exercise, the goal should be to:
Identify sources of risk from a field
Identify questions of who is working on what, and what is not being worked on in a field (i.e. is there anyone working on methods or tools that could make engineering pandemics easier/harder?)
Create a map of constraints or bottlenecks within a field and what implications their unblocking holds (i.e. why or why not would someone work on method x which is likely to reduce the likelihood of accidental pandemics?)
Work backwards to identify research directions needed to address the bottleneck
Combine insights and tools from multiple domains for potential novel approaches (i.e. what if we try this method, used in field x for reason y, instead?)
Determine if the timing is correct for such a goal, or if it is premature (i.e. is it clear this does reduce risks?)
Identify the team needed for such a project and what existing work may inform it or is complimentary (i.e. reports like this one could be incredibly helpful)
Determine the best funding mechanisms and or institutions that could support this effort, existing or new (i.e. is this defensive technology better developed within DARPA or as a Focused Research Organisation?)
Sources for these points can be found here and here.
Road mapping may enable the identification of several risk-reducing projects and can be crucial in informing what projects to support, curb back, or start if they are not already being worked on. This is an example of scientific road mapping, though without an explicit risk-reducing purpose. Note the funding opportunities section. I think this exercise is useful, informative, and has great ROI.
Types of Differential Tech Development
As noted, it is sometimes difficult to explicitly generate action items that will lead to advancing risk-reducing technology. Scientific Road mapping within a DTD context is one way to generate action items but is not always possible. Below are some strategies that could be seen as ways to generate action items that are DTD aligned. The following strategies were originally outlined here and I will attempt to summarise and build on top of them.
Roughly speaking, Ordering is the exercise that takes a select amount of initiatives or developments we already have in mind and orders them based on factors like urgency or importance. It is the most basic form of DTD and can act as a solid starting point. An example of Ordering is a philanthropic organization making it a priority to fund non-dual use defensive technologies first, especially those with little risk associated with them, like Far-UVC light sterilization. This way, the organization is signaling it would prefer this development before other specific developments.
By placing some of the most beneficial developments first on a “prioritization list”, and some of the least beneficial ones last, Ordering can help us come up with better questions or decisions without relying on explicit forecasting predictions.
Outside of non-dual, defensive technologies, Ordering is more difficult and, in practice, does necessitate some level of forecasting or anticipation. I think Categorising has the potential to provide robust frameworks for Ordering purposes. However, I think it highlights how DTD could be relevant for informing trade-offs in everyday decision-making for relevant organizations, not just catastrophic risks.
Gradualism assumes it is beneficial to develop technology gradually, as the effects of exponential growth may be uncontrollable but the technology itself is potentially beneficial.
Roughly speaking, I think this approach lends itself well to developing Substitute and Safety Technologies. The Collingridge dilemma illustrates the utility of a gradual approach well. As technology increases in adoption, our knowledge around it increases but the ability to act on that knowledge decreases; It is difficult to shape a technology if it is already widely adopted. Conversely, when a technology is new, the ability to act on the available knowledge is high, but often little is known about it. The figure below demonstrates the inverse relationship between control over technology and its predictability as it is adopted over time, also known as “the Collingridge curve”.