If we want to improve the lot of humans, we need "Craft AI", not "Industrial AI". And the data needs to be "warm", not "cold"
Very interesting column from Nesta (co-written by one of our friends George Richardson, at Utopia Dispatch) on the possibilities for a “Craft AI”. This is AI that could produce subtle forms of data that might serve social outcomes and communities, rather than what they call an “Industrial AI”. Applying Industrial AI to solve societal and cultural challenges is possibly the big mistake of the moment.
As they write:
One of the unofficial remits of AI is to “solve intelligence and then solve everything else”. We have to assume that “solving” would include reducing inequalities in education, tackling obesity and decarbonising our homes. Are we about to get AI systems that could help us solve these problems?
The dominant model for AI is an industrial one. It trains deep, artificial networks on large volumes of web and social media data. These networks learn predictive patterns and can be useful for perception jobs such as identifying a face in a photo. They are good for tasks that don’t have human input or where we are not interested in understanding why someone made a choice, such as liking a social media post.
The large technology companies developing these systems use them to predict relevant search engine results, what social media content is most engaging and to make recommendations that could result in a purchase. This helps these companies build more engaging and profitable websites and apps.
But when it comes to social impact sectors, data is scarce, explanation is more important than prediction and making mistakes could cost lives.
The post is very linked-packed and worth reading through, but here’s their ticklist of what a “Craft AI”, tailored more intimately to concrete human situations and needs, could be like:
Craft AI
It is able to work with smaller, messier data and is as focused on documenting, curating and exploring as it is in training models.
It uses, where necessary, models that are robust, interpretable and explainable. Given the social nature of many of the decisions it seeks to inform, it keeps human decision makers in the loop instead of automating their roles.
It seeks to democratise access to data and AI methods so that they can be deployed by a more diverse set of organisations and communities.
Its goals are not merely to improve the efficiency of social impact sectors as they exist today, but to potentially transform them in a way that improves outcomes and makes them more just.
It is humble, open and collaborative, recognising the value of other disciplines and perspectives and that data interventions are sometimes (often) not the right tool to tackle a societal challenge.
These ideas are inspired by the work of a wide range of communities including data scientists for social impact, researchers in areas such as causal and participatory machine learning, critical studies of AI, responsible and inclusive innovation and data justice.
Many of these ideas have been put forward in response to the ethical risks raised by industrialised AI models and as an opportunity to overcome technical barriers that might prevent us from developing truly general-purpose and trustworthy AI systems.
Craft AI is likely to be slower, more complex, localised and less scalable than industrialised AI. It also requires more human involvement. Perhaps it makes sense to think about it as an instance of intelligence augmentation, where we use machines to boost our capabilities instead of automating them, not shying away from the responsibility to thoughtfully and carefully tackle the biggest challenges of our time.
More here. We’d also add that “craft AI” sounds like it may fit with Nora Bateson’s concept of “warm data”, which we have profiled here, and which she defines thus:
Warm Data is information about the interrelationships that connect elements of a complex system. Put another way, Warm Data is transcontextual information. Warm Data captures the qualitative dynamics and offers another dimension of understanding to what is learned through quantitative data, (cold data).
The implications for the uses of Warm Data are staggering, and may offer a whole new dimension to the tools of information science we have to work with at present.
Warm Data is a specific kind of information about the way parts of a complex system, such as members of a family, organisms in the oceans, institutions in society, or departments of organisation, come together to give vitality to that system.
By contrast, other data will describe only the parts, while Warm Data describes their interplay in context. Warm Data illustrates vital relationships between many parts of a system.
For example, to understand a family, one must understand not only the family members, but also the relationships between them, that is, the warm data. In such cases, warm data is used to better understand and improve responses to issues that are located in the relational dynamics.
Examples include understanding the systemic risks in health, ecology, economic systems, education systems and many more. The typical approach to issues decontextualises specific information, which in turn can generate mistakes. On the other hand, warm data promotes coherent understanding of living systems.
More here. Craft AI handling Warm Data sounds… very interesting…