Throughout our history as a species, we have grappled in the quagmire of complexity around the decisions we make. There are ethical, moral, and social impacts to every new development and we continue to navigate this confusing landscape in a process where things constantly shift and no end state is available.
This article, or perhaps essay, is long. This is necessary to capture the thought process, rather than just the outcome. Just as a student may gain bonus points for a wrong answer that uses the correct method of calculation, I hope we can debate where in this mental exercise the two of us diverge in agreement, rather than having a binary debate about it being entirely right or wrong.
My expectation isn't that this is the final version or even that such a version is possible. But as a way to agree on the terms of debate, I believe it will be highly constructive.
Regulating the motorwagen
The motor car has been around for over 140 years and we've developed a suite of rules and practices to tackle conflicting interrelated concerns. A set of rules governs almost every aspect of a vehicle's design and use. Some of these are imposed on the manufacturer, others on the driver, and still more on the interaction of vehicles, pedestrians, and the spaces they operate in.
Source: Wikimedia Commons.
One thing is very clear. 150 years has not been enough to perfect the system and we are still making adjustments as we balance a system that's subject to shifting concerns. There are vast differences in how these systems are governed around the world based on social views, like the priority of vehicles, bicycles, or pedestrians, or balancing personal autonomy with congestion. These social attitudes also shift over time in response to factors like environmental impact, or evolving views of fairness.
At the time of writing, we seem close to proving that well-designed subsidized public transport is superior to personal transport in many cases. Congested cities are using freeway removal to introduce (or re-introduce) public spaces. This is a complete reversal of policies enacted in the second half of the 20th century, where freeway expansion proliferated.
If the motorwagen had never been invented, what might our towns and cities look like? It's almost impossible to imagine as so many of us have grown up surrounded by roads, even if we don't own a vehicle. This is the question we must now ask in order to challenge the current state of things and create something better. And it would have been equally challenging for people in 1885 to imagine what the motorwagen would do to their towns, cities, and lifestyle.
Source: Wikimedia Commons.
Reading the road ahead
With the historical lesson of the motorwagen firmly in mind, we must now look ahead to imagine what our towns and cities might look like with generative AI. The analogy is apt because, just as with personal transport, the speed and convenience are much celebrated and the harms (emissions, road deaths, environmental impact, congestion, centralization) are far less clear.
We might find it instructive to look at how the debate took shape in the 1920s, when the harms of motor vehicles were becoming more apparent as motorists began to compete in earnest with the space formerly occupied by people. While both public opinion and that expressed by judges was vastly in favour of the rights of pedestrians, the wealth at the disposal of the motoring minority allowed them to build the rhetorical force to petition for rights against the majority view.
The transformation was not a natural evolution, a side effect of technological progress, the choice of a democratic majority, or the product of a free market.
Peter Norton, MIT Press.
And now we must transfer these lessons to the subject of generative AI, a term we use to refer to the class of tools that build models based on training data, and generate text, image, audio, and video output based on these models in response to prompts that do not precisely determine the output.
A model concerning utility and harms
Much of the conversation around generative AI has been polarized into extremes labelled hype and hate to describe those with a positive or negative view of generative AI. We can certainly draw a comparison between pedestrians and motorists in this respect, though how large the minority is when compared to the majority is less clear.
In fact, we might imagine the situation before the 1920s, when the motorwagen was a novelty and the true picture of the utility and harms were not yet clear. What model could have been used to integrate this new form of transport into society with the least harm?
This is what I have grappled with as a technologist and creator trying to navigate the utility and harms of generative AI. It becomes impossible to think clearly of the utility when the harms are so obvious. This is why so many people have chosen to simply ignore the harms, or reject the technology.
There may be a middle path, which is the assumption I am working with. It might also be the case that there is no middle path and we must choose to embrace without remorse or reject entirely the whole concept of generative AI (a choice many have already made, which is either premature or correct depending on which polar opposite was selected).
The middle-ground solution I propose involves building around some questions I believe to be central to the discussion:
- What harms do we see now, or predict in the future, that we could avoid?
- Who is responsible for each of these harms and could they take action now rather than in response to controversy and backlash?
- If there were no harms, what use could we make of this technology and it is fit for such purposes?
It appears on the surface that this lands closer to the "reject entirely" extreme position, but that's a deliberate choice. My perspective is that if we could find a path of minimal harm, we could focus on what utility generative AI has. While the harms are glaringly obvious, it is hard to imagine a level of utility that justifies the level of harm.
Model of harms for the motorwagen
We can illustrate this with the motor vehicle example, then build a model for generative AI. We are a century beyond the flashpoint with motor vehicles, so we can be more circumspect in our treatment of the model. This will help us keep a clear head as we continue.
Source: How are vehicles built and what materials does this process consume? What are the benefits and drawbacks of building a factory in a town? Are innovative design elements created by competitors appropriately licensed?
Preparation: What design elements could make the vehicle safe for motorists and other people occupying the same space as motor vehicles, like pedestrians and cyclists? How might consumable parts of the vehicle, like lightbulbs, lubricant, and brake pads, be replaced by the motorist?
Application: How will the vehicle be used and how do other members of society respond to this use? Commuting and leisure travel is socially accepted while street cruising and causing an obstruction are considered anti-social. In some cases, the intent behind the application can be as important as the application itself.
Outcome: What do we achieve with the motor vehicle and what unanticipated outcomes result from this use? For example, we discovered vehicle emissions degrade local air quality and cause environmental damage. We also created a dependence on oil prices for everything that must be transported from farms and factories to stores and customers.
The classification of harms allows us to assign responsibility, with regulators and government stepping up where required to create the rules and laws to ensure fairness.
Source and preparation are the responsibility of the manufacturer and their suppliers. The joint enterprise of this supply chain must be considered to prevent an end-supplier burying harms upstream in the production process.
The application is the responsibility of the motorist. They must choose to travel at appropriate speeds based on signage and driving conditions. They decide how they drive and where they park their car. Motorists negotiate their use with society at various distances. They may decide they don't want to use their motor vehicle for short journeys, they might negotiate with neighbors to agree how they each park in a shared area, or the negotiations may be the result of a collective road safety campaign to reduce a speed limit near a school.
The outcome is collectively owned. This is a process whereby we discover harms and seek their resolution. This will often bring an individual with little power up against a larger more powerful entity. Unanticipated harms often require city or state intervention to ensure a resolution, such as the introduction of pedestrianized high streets, or the ban on lead-based petroleum.
| Responsibility | Manufacturers | Motorists | Society |
|---|---|---|---|
| Source | ✅ | ||
| Preparation | ✅ | ||
| Application | ✅ | ✅ | |
| Outcome | ✅ | ✅ |
Removal of harm enables participation
Removing harms invites broader participation and reduces the need for city and state authorities to regulate. With this model, you could say:
If the manufacturers resolved all major harms in the source and preparation of a vehicle:
- I will try being a motorist to see what utility a motor vehicle brings
- I will negotiate with others how motorists and non-motorists co-exist
- We will collectively look for new harms and pressure those responsible to remove them, and pressure authorities to remove them if those responsible neglect their duty
Specifically, if we can now begin to move to the topic of generative AI, as a technologist who has always enjoyed trying new ideas, techniques, and technologies, I would be able to commit to experimenting with generative AI to discover its utility in depth, and could participate in the social process to determine where we accept its use and where we don't.
My gut feeling is that generative AI has utility if the harms were to be removed, including those we haven't uncovered yet. For example, I have seen positive examples of generative AI being used to assist people through assistive technology. Even so, I'm convinced that I don't want to read books or articles written using generative AI.
Model of harms for generative AI
| Responsibility | Model providers | Model users | Consumers |
|---|---|---|---|
| Source | ✅ | ||
| Preparation | ✅ | ||
| Application | ✅ | ✅ | |
| Outcome | ✅ | ✅ |
Source: Where does the data come from? Does it contain bias or inaccuracy? Do we have permission to use the data?
Examples of specific harms:
- Models trained on copyrighted work without license or permission, exacerbated by generative AI directly competing against the works contained in its training data
- Collecting data from users without their consent for use in training, for example by using their profile and conversation data with their explicit agreement
- Using data sources that contain hate speech, false information, or bias
Preparation: How do we detect and correct mistakes and bias? What resources do we consume during preparation? What are the local and global impacts of our operation?
Examples of specific harms:
- Use of power and water for datacenters used in the training process, affecting access to these resources for local populations
- The global environmental impact of the supply chain for datacenters
- Subsidizing the technology to artificially lower its cost to build a dependency prior to increasing costs or reducing what people get for their subscription level
- Exploiting low-paid workers during the training process, for example to label data
Application: What is our motivation for using generative AI? Is the use appropriate? Do we discover the use of generative AI? Does what we produce meet our existing quality standards? Are we misleading people? If a person did this, would we accept it? What value exists in the work beyond the generated output?
Examples of specific harms:
- Using generative AI to make decisions without disclosing it and without providing an easy way to appeal the decision
- Using generative AI to misrepresent skills and abilities
- Using generative AI to save yourself time at the expense of other people's time
Outcome: Is the output correct? What should we check before we call this done? What unintended consequences result from this?
Examples of specific harms:
- Lowering the value of works by real creators and artists by spreading suspicion that work was done by generative AI
- Reducing the ability for users of generative AI to develop skills and solve problems without the technology
- Lowering the bar for quality by normalizing poorly completed work tasks
Practical use of the model
The model isn't final and the examples are illustrative, but we can now build a path forward. Based on the current examples, I could create a generative AI model using my own data, or data I've obtained permission to use for this purpose. During the preparation, I would use ecologically sound infrastructure and perform checks to identify sources that may pollute my model with inaccuracy or bias.
When the model is put to use, I use it to crawl through my photographs and apply labels based on what is found in each image. The tags allow me to search my photographs when I need to find something, instead of scrolling through a huge collection of thumbnails.
In this example, we've taken care to limit the known harms across all categories and responsibilities. The source, preparation, and application of the generative AI deal with known harms and the outcome has high utility. There may still be unintended harms, and we will tackle them as we identify them.
Why it works
The generative AI debate is highly charged and has quickly polarized into the hypers and haters, with each extreme focusing entirely on a single element. The hypers ignore the harms, and the haters dismiss all utility. While some will remain pinned to the extreme, I'm interested in advancing the debate to enable us to tackle the harms and test the true utility.
The model is the mechanism to provide the path forward. It doesn't dismiss the harms, but actively surfaces them and offers them to those responsible to resolve, just as we asked motor vehicle manufacturers to make cars safer and motorists to drive responsibly. When manufacturers and motorists fail to meet the standards society demands, we ask authorities to intervene.
Meanwhile, we choose our own relationship with motor vehicles within the agreed legal and social contracts, using them where we decide they are useful.
Because our understanding and empathy shift all the time in society, we may one day decide that personal transportation isn't the appropriate solution to our transport needs. We might make a personal decision, or we might make structural changes to public transport and city design that affect everyone. These changes don't necessarily eliminate motor vehicles, but they can alter how we structure their use.
Get involved
You can get involved in this project in various ways. I'm keen to obtain feedback from hypers and haters. I understand these terms are loaded, but I'm not judging the pragmatism of either group. Your thoughts on this model would be incredibly valuable. I'm also keen to find people who, like me, would enjoy experimenting if only the harms could be removed.
For feedback on the model, I would appreciate understanding not only what you disagree with, but if there are any aspects you agree with. If you feel very strongly about what I've written, you may be tempted to aim for concision through bifurcation, but I'm seeking a more profound understanding of different perspectives and that will require a longer and more thoughtful explanation on your part. I'm grateful for your additional effort in this respect.
Depending on the feedback, I may adjust the model or we may all reach the conclusion that it must be discarded and something new must be created to answer these questions. We will follow the wisdom of George Box; "All models are wrong, but some are useful." If the model proves useful, we will continue the work. If it turns out to have limited utility, our efforts can be directed elsewhere.
The generative-ai-harms GitHub repository will allow us to collaborate using discussions and pull requests.
So, what do you think?
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