Impact on organisations and funding

CONTENTS

Introduction: Why is AI such a big deal right now?
New ways of achieving a mission and AI terminology
Impact on organisations and funding
New challenges for civil society to address
Conclusion & recommendations

Robotic Process Automation (RPA) & automated call centres

Perhaps the most straightforward disruption will come from automation. This has become a focus of widespread attention and concern recently because of the awareness that AI brings the potential for automation extending far beyond the traditional blue collar, manual jobs that have long been under threat from the introduction of industrial machinery and robots. The advent of machine learning and cognitive computing means that it is now (or will soon be, at least) possible to automate even the kind of skilled or knowledge-based jobs that were long thought impervious to such a threat.

We will consider the wider social impact of mass automation, and the role that civil society will have to play in managing this transition, in the next section. Here, let us focus on the impact that automation will have on CSOs themselves as organisations. Two of the areas that we can already identify as low-hanging fruit are repetitive, data-heavy processes and services that require telephone operators. In terms of the former, there is already an entire field of Robotic Process Automation which focuses on finding ways of using software “robots” to automate and improve clerical processes. It is almost certain that CSOs could benefit from much of what is being developed within this field. For instance, a blog from Nesta’s Geoff Mulgan in 2017 outlined ways in which the grant application and selection process used by philanthropic foundations might be streamlined and rationalised using AI.

In terms of call-centres and telephone operators, we considered in the previous section the use of AI-powered chatbots by nonprofits to deliver advice services in furtherance of their mission. But some of these organisations may also operate quasi-commercial call centres that are focused on helping people to access products and services, and as such could also benefit from the application of customer-service chatbots.

RegTech

CSOs are subject to many different kinds of regulation by a wide range of agencies around the world. The adoption of AI by regulators, therefore, could have a significant impact. And we are already seeing examples of this as part of the wider ‘RegTech’ field. For example, the UK tax authority HMRC is trialling the use of software robots to check tax returns and chatbots for customer service. The possibilities go much further, however: for instance, machine learning could be applied to large volumes tax data or companies’ financial data to analyse patterns that could be used to develop ‘early-warning systems’. This could enable a shift towards more preventative regulation, where potential issues are identified early and dealt with before they come to pass, which would be both more effective and cheaper than traditional methods of enforcement after the fact.

The same principles could apply to charity regulation in the future. As with anything else to do with ML, this will put a huge emphasis on data. It will also raise many of the questions about fairness, transparency and accountability that we will come on to in section 4. 

Al & Philanthropy

Giving to charity is (according to classical economics, at least) an inherently irrational act. However, there have always been those who have sought to remedy this perceived failing, and to make philanthropy more rational so that it is a better tool for redistribution within our society. AI could offer new ways of redressing this balance, and could have a profound impact on the ways in which people are able to give to charity.           

One way in which this impact could be felt is through the use of AI to turn philanthropy advice into a mass-market product. There are already numerous examples of financial services companies developing “robo-advisors” to give advice to customers. One of the key benefits of doing this is often argued to be that it makes such service more cost-effective, so they can be offered to a wider base of clients. If AI could be applied to automate philanthropy advice in the same way that it has been used to automate financial advice, then it could make it a feasible mass market product, and this could have a massive impact on the ways in which people give.

There are various different ways in which AI could be applied to offer philanthropy advice. One is to use the same sort of tailored recommendations based on past behaviour or peer group activity that underpin the algorithms used by Facebook or Amazon to present you with new content or products (i.e. “if you liked X, why not try Y?”, or “your friends are all doing Z, why not join them?”) Facebook itself has enabled giving to charity via its Facebook Messenger Service.  And along similar lines, Salesforce has partnered with United Way in the US to add an advice function to its workplace giving platform based on its AI-powered “Einstein”.

The obvious appeal of this is firstly that it fits well with existing platforms; and also that social cues are an important part of philanthropy, so harnessing peer group effects is potentially a powerful way of getting people to give. However, there are also clear reasons to be wary. The main one being that algorithms based on prior behaviour or peer group activity simply tailor information to fit with existing biases. When it comes to charitable giving, this means that they are likely to result in well-understood causes and well-known organisations getting promoted at the expense of less well known ones. This should be a serious source of concern for a sector where there are already concerns about the balance between large and small organisations and the difficulty of fundraising for unpopular causes.

A more sophisticated way to offer philanthropy advice, and one that would go further in terms of the challenge of making philanthropy more rational, would be to apply ML to data on social and environmental needs (much of which is out there already, although probably sitting in siloes in the public and private sector) and to data on the social impact of CSOs and interventions. This would enable identification of where the most pressing needs were at any given time, as well as the most effective ways of addressing those needs through philanthropy, and thus allow a rational matching of supply and demand. We have previously coined the term “philgorithms” for algorithms of this kind.