ALGORITHM IS GONNA GET YOU; WHAT THE RISE OF ALGORITHMS MEANS FOR PHILANTHROPY

Above: Chart taken from ‘Making an Impact‘, a report by UK think-tank NPC

This demand amongst funders – and amongst individual donors in particular – is set to increase as a new generation of donors integrate their ideas and preferences into their philanthropy. The Millennial Impact report found that young people are more likely to associate themselves with causes rather than organisations, possibly suggesting less loyalty to specific CSOs and a greater willingness to see their money go to whichever organisations offers greatest impact. Furthermore, a study carried out in the US by Fidelity has shown that millennials are more than twice as likely to be influenced by new technologies and alternative forms of giving as baby boomers when it comes to their philanthropy. Taken together, this loyalty to causes and willingness to be guided in their giving by technological advances – on top of a growing demand for data on impact – represent an aligning of the stars to create the perfect environment for algorithmic philanthropy.

The most likely early adopters of algorithmic philanthropy will be proponents of Effective Altruism (EA). Effective Altruism (EA) is a movement based on the work of the Utilitarian moral philosopher Peter Singer, which has found favour among a growing group of academics, practitioners and donors. It has proved particularly popular with many of the new breed of donor coming out of the Silicon Valley tech industry, such as Facebook co-founder Dustin Moskovitz. EA asks donors to take themselves out of the decision making process and instead allow their giving to be driven entirely by evidence. Put simply, it is about doing the greatest amount of measurable good possible with the resources available and evaluating the outcome. In this way, for proponents of EA, the data should not merely guide how you pursue a charitable cause, but dictate the cause itself too.

Given the growth of EA, it is easy to imagine philanthropists coming together, collecting publicly available data on the ‘market’ and creating a fund or endowment. This could prove very attractive, particularly as it would have hardly any running costs once established. Imagine a direct cash transfer programme that simply give money to people who need it (such as Give Directly) that used big data to assess which recipients were likely to add maximum community value / spend money most wisely. This seems intuitively to be desirable doesn’t it? It fits all of our assumptions about who deserves our help and what effectiveness is. However, the use of algorithms in other areas of society have shown that whilst handing over decision making to algorithms might reduce human error, it doesn’t remove the injustices of the society in which the algorithms are created. Indeed in some cases, it can reinforce them.

The problem is that algorithms take data from the world as it is now. That world is one in which prejudice and injustice has resulted in a system in which some people are profoundly disadvantaged by societal instruments and pressures which are entirely out of their control. The effects of those forces are picked up in the data by algorithms and where they have predictive power, used in decision making. The result can be to amplify existing social injustices.

In her book, “Weapons of Math Destruction,” Cathy O’Neil describes how this is already happening. One particularly striking example O’Neil details is that of “recidivism models” in criminal sentencing. For years, there has been widespread concern in the US about inconsistencies in sentencing whereby minorities often seem to receive more punitive treatment. As a result, a number of states have resorted to using algorithms to calculate the likelihood that those convicted will reoffend, guiding sentencing with reason and challenging unconscious biases. Objectively, this seems fair and it certainly feels efficient. With prison populations and costs spiralling out of control in the US, it makes sense to lock up those who are likely to reoffend and to minimise jail time for those who are not. However, closer analysis of the data that might best predict the likelihood of recidivism reveals it is also a proxy for societal inequality. Alongside what you might expect, such as the nature of prior convictions, drug and alcohol use and previous police encounters are included. The former is strongly associated with poverty and the latter has been shown to be impacted by the racial profiling by law enforcers. Furthermore, factors such as where you live (which could be a proxy for both poverty and race) and the criminal convictions of family members suggest a model that reinforces the social structures of inequality.

So in our example of a direct giving algorithm, it may well be that people of a certain ethnic group, location, gender, religion or age are more likely to make good use of any aid provided; but to deliver aid on that basis would seem merely to reinforce societal inequality. Similarly, were microfinance providers to profile farmers in this way to favour only those most likely to repay, this would no doubt advantage those facing only economic barriers, whilst – relatively speaking – further marginalising those facing multiple barriers to prosperity.

The best place to see both the potential power and dangers of algorithm-led decision making is the financial trading industry. When we think of trading we tend to have images of trading floors full of smartly-dressed but sweaty looking people (probably men) shouting into multiple phones, waving pieces of paper around and looking at giant boards full of inscrutable numbers while whispering to one another about how the weather is likely to affect the price of sugar. However, the reality of modern trading is quite different.

Trading these days mostly takes place in server farms and is largely automated. Money here is made not by cleverly predicting some market shift by taking on board various macro economic and market specific variables, but by responding as fast as possible to price changes and trades logged by others. Trades are logged so quickly – this kind of trading is called High Frequency trading (HFT) – that it is impossible for any human to keep track of what is going on. When I say ‘as fast as possible’, I really do mean that. It is difficult to overstate the premium that is placed on the ability to respond the fastest. One way to illustrate this point is to reference the $2.8 billion paid by the first 200 companies in 2014 to use a new fibre optic cable spanning the 827 miles from the Chicago exchange to the New York Exchange (in New Jersey). By traveling slightly straighter this cable promised to cut milliseconds of the time it takes to deliver information between the two exchanges. Given the power of the algorithms being used in HFTs, even thousands of a second represent a significant advantage.

This system, in which humans take a back seat in the decision making in trading, represents an extreme example of what the trend for integrating big data and computer aided, rule based decision making could look like for philanthropy. We have already outline some of the potential applications of such a model but taking HFT as allegoric – which I recognise is somewhat of a stretch – we may also want to consider some of the issues that HFT has encountered. Indeed, given that some people are already calling for philanthropists to develop hedge fund style “quants” for philanthropy, the time to raise any potential issues is now.

Cast your eyes back to Friday 7, 2016. Only a couple of weeks had passed since Britain had voted 52 per cent to 48 per cent in Favour of leaving the European Union. When traders Europe arrived to work in the morning and turned on their computers, what they saw must have beggared belief. Overnight, Sterling (the UKs currency) had plummeted in value by a staggering 8 per cent. Even more remarkably, within half an hour, Sterling had almost entirely recovered. Those traders not on the phone to their IT departments trying to establish why their computer was malfunctioning began to scramble to find the cause of this bizarre event. The culprit? Most are pointing the finger at algorithms.

Only two weeks earlier, on June 23, the British public voted in a closely contested referendum to leave the European Union (EU). Traders around the world, understanding the ramifications of negotiations on ‘Brexit’ on the global financial market – and in particular, whether a so called ‘Hard Brexit’ which would see Britain face tariffs in order to trade with the continent – had introduced codes into their algorithms to monitor news sources for indications of likely approaches to diplomatic negotiations.

While traders in Europe were fast asleep, an article was published on the Financial Times website with the title “Hollande demands tough Brexit negotiations”. Apparently, as some algorithms reacted by selling shares, other algorithms subsequently responded to those orders prompting Sterling to fall off a cliff. I say apparently, because the sheer volume of trades on the global market and the speed at which decisions are made, makes it almost impossible to find out why an event like this has happened, and crucially, it makes it very difficult to predict or prevent in the future.

A FLAWED HUMAN EVALUATION

I won’t pretend that I can approach this issue without prejudice (in the way that an algorithm might be able to), or that I can take on board all of the available information in coming to a conclusion. Indeed, it is fair to say that given my vested interests in philanthropy advice and services as a CAF employee, information suggesting that algorithm-led philanthropy is somehow a terrible idea is likely to resonate with me due to my inevitable confirmation bias on the issue.

However, despite my strong preference for a world in which my vocation still exists, I am objective enough to see that a demand for more information on all aspects of giving by donors, regulators and even beneficiaries, the ability to access that data electronically, access to huge cloud-based processing power and the prevalence of algorithmic decision making in other spheres of life are all pointing to the inevitability of a future in which some, most or nearly all philanthropy will be either influenced by, or entirely directed by, algorithm.

Furthermore, I recognise the possible positive outcomes for philanthropy and those that it benefits in a trend for algorithmic data crunching. Though there are many, for me, the principal benefits are;

• The ability to use data to identify effective solutions – particularly those which have been undervalued. Solutions to problems are often unglamorous but algorithms are only concerned with what works – unless we program them otherwise.
• By responding to activity elsewhere in the philanthropy ‘market’ algorithms can avoid duplication, or ensure spending is complimentary.
• Remove false assumptions from decision making. We carry with us the lessons gleaned from all our experiences and regularly use these as a way to make intuitive decisions, rather than engaging in detailed thinking each time the question arises. This can result in false assumptions. Algorithms do not need to cut corners like this.
• Reduce costs through disintermediation. A whole host of services that advise donors or manage their philanthropy could be automated at a fraction of the cost.
• Counter the perception that philanthropy is politically motivated or that wealthy individuals are exerting power in their own interests. Philanthropists are coming under increasing scrutiny for the way they give away their wealth. Algorithms could provide a way to give selflessly.

Given this, I can do something that even the most powerful AIs might still struggle with: I can incorporate this knowledge into a moral framework and consider how we might be able to mitigate the potential ethical, social and political unintended consequence of algorithmic philanthropy. Again, there are likely many more but those which strike me are;

• The data used by algorithms will necessarily be drawn from our unfair world and as a result the actions it will suggest might reflect, or even reinforce the current structure of society. Algorithms are not prejudiced, but society is.
• Data reflects the world as it is and algorithms can only seek to find improvements within the current system rather than imagine a different system. Some of the most important philanthropic movements- such as the anti-apartheid movement or the campaign for same sex marriage for example – required an imagining of a systemic change.
• Algorithms can be so powerful and process so much data that humans cannot have proper oversight. This creates a dangerous gap in governance where we can neither understand and learn from errors or predict future problems.
• Many people are motivated to give because they have an emotional connection to a cause. Will people want to give if their actions are being dictated by an algorithm?

THE FRIENDLY GHOST IN THE MACHINE

Given the inevitability that technological advances will come to dominate our lives in ways which are negative as well as positive, it is possible to feel powerless. But we can do something. First, we need to be as cold and calculating as computers are in sizing up our future robot overlords and recognise that they are neither good nor bad, and that they are merely reflections of the prejudices that we feed them (would a computer get that joke?). To this extent, we need to be cognisant of this and be careful to redress unhelpful prejudices.

Secondly, we need to by empowered by the knowledge that civil society is in fact ideally placed to do this and has in fact been doing this in other spheres of life for generations. Thirdly, each of us, in our philanthropy – be it modest or grand – has the power to push the CSOs that we fund to ask questions of themselves and others. This could be as simple as enquiring into the way a charity we give to uses data or as far reaching as the approach of LinkedIn and eBay founders Reid Hoffman and Pierre Omidyar who donated $10m each to the Ethics and Governance of Artificial Intelligence Fund. Finally, we need to ensure that we evaluate the appropriateness, advantages and limitations of algorithms in guiding our opinions, choices and of course our giving.