The giving quants or "algorithmic philanthropy"

I spent the weekend preparing for, and then taping, a segment on digital activism for Philanthropy Talk. That's the public radio "car talk" program for philosophical questions. The segment will air in January.

So it's no wonder that I have thought experiments on my mind. Yesterday when I opened The New York Times (the actual paper edition) I read a range of stories. Sometimes when I read the paper my mind plays tricks on me - like, did the person who wrote the story on page A1 read the story on page B8? Today, several stories in the paper seemed to be speaking to each other - the first one was on robots and jobs. The other one was on artificial intelligence research.

Here's where my mind went (bear with me, this will be about philanthropy and civil society)

First, some background:
This year while I was working on the Blueprint, I kept wanting to do more imaginative work on digital technologies. It's one reason I regularly read newsletters from Edge.org and the Singularity Institute, I go to campus lectures on neural networks and the block chain and other stuff I don't understand and I'm a member of the Long Now Foundation. All efforts to try to learn about far-edge technology. But the far-edge of technology is not the right topic for the Blueprint, since many people who read it are still coming to grips with hashtags and chat apps. So I keep a notebook of thoughts on edgy technology and civil society, trying to figure out what to do with them. This year, one of the pre-readers of the Blueprint said to me, " You know, this is all well and good, but what about the really interesting technological stuff - like neural networks and pattern recognition and AI and robots, and what not?"

So he and I went off and had coffee. And we're trying to figure out what to do with my notes, his observations, and the intersections and implications of cutting-edgier science and technology on philanthropy and civil society.*

Reading the two news stories noted above, I learn that many economists are no longer sure that   technological advances (such as robotics) will create more jobs than they destroy. And then, turn a few pages, and I read about a 100 year study to look at the real effects of artificial intelligence. That is, the study begins now and will run for 100 years. Where will the study take place? Stanford University (with lots of academic partners). It's called AI100

So how do you reconcile these two thoughts - "tech is changing the economy but not augmenting it" with "let's invest now in something to run for 100 years." You make a gift to a nonprofit. Admittedly, a nonprofit with a large endowment, but what is to say that this university (or any institution) will withstand the very forces this study aims to examine? In other words, if you make a 100 year bet to understand a technology's impact, how do you know the technology won't destroy the place you made the bet before the century is up?**

Now, the thought experiment (When AI ate philanthropy)
Assume some folks begin to apply machine learning and artificial intelligence, data and data analytics to the increasing amount of quantitative data being generated by and about social  organizations. This is a completely plausible step from where we are now - what with the effective altruism movement, random control trials, and the emerging data infrastructure for philanthropy. One possibility is such a trajectory leads to the Hedge Fund version of donors - "quant givers." Guided by algorithms and data (replacing program officers and philanthropy advisors) machines would match a donor's dollars with social causes. If we assume that the capacity to do this (at least in the short term) would take a lot of money to build out, we can also assume that the charitable dollars would be coming from wealthy people (or perhaps pools of money from donors, pushing further on the hedge fund metaphor), and so the gifts would also be large. As such, they'd be attractive to organizations, who would try to fit themselves into the models (just as organizations "optimize their search terms" to fit Google search algorithms). Because the algorithms "learn," they get better and better at finding and matching, data crunching and report generating. They create their own, powerful, self-sustaining feedback loop.

It's possible such algorithmic philanthropy could drive enough resources and draw enough attention from fund seeking nonprofits. The feedback loop thus grows to a larger force that begins to change the kinds of measures and metrics that lots of nonprofits track and provide. This leads to behavior change among the vast swath of nonprofits, changing the "marketplace" of options available to smaller, more passion-oriented (or simply less data-driven) donors (i.e. most of us). What would it do to the measures and data that were collected and fed into the machines?

How much would it take before the quant-driven feedback loop affected change throughout the ecosystem of nonprofits? How would parallel movements of impact investing and performance measures, earned revenue and double bottom lines play into this story? How would the more personal, direct-involvement approach of most donors counteract this approach? What countervaling influence could intuition, expression, personal passions, minority voice, and donor choice have on this feedback loop? Just how far can we rationalize/algorithmically structure giving? And how far should we? What if we took all the humanity out of philanthropy?

One likely answer to that question is that the measures would focus ever more on quantifiable, short term outputs that can be easily collected. From the perspective of the algorithm it would be harder to justify longer-term investments in hard-to- measure activities (it would also probably skip over startups with no metrics at all). Long term enterprises and startups might not even "make it into the data" being used by the algorithms.

What falls into this category of long-term hard-to-measure charitable opportunities? Well lots of things like advocacy, policy analysis, and basic research. Oh, and endowed universities.

So, are 100 year research efforts on technological change even possible? How do we factor in the effects of these digital technologies on the peripheral technologies like nonprofit universities that house them? What happens when the thing being researched consumes the place that conducts the research?


SIDEBAR
*What do you think we should do? A new blog? An annual look at just tech and philanthropy? A "hype cycle" analysis? A two by two?
**This paradox, by the way, is one of the reasons I believe so strongly in focusing on the effects of digital innovation on civil society. Most studies of digital change look at their implications for business or government and assume that nonprofits will just keep on keeping on. I think that's nuts.


The (failure) and future of civil society

A friend forwarded this to me in an email - it's a link to an article in The Guardian, a letter from the head of Civicus, a global organization representing civil society organizations.

In it he notes the growth of the sector.
And its failures to solve the problems it seeks to address.

In his words:

"We – civil society – have been co-opted into economic and institutional processes in which we are being outwitted and out-manoeuvred. Our conception of what is possible has narrowed dramatically. Since demonstrating bang for your buck has become all-important, we divide our work into neat projects, taking on only those endeavours that can produce easily quantifiable outcomes. Reliant on funding to service our own sizeable organisations, we avoid approaches or issues that might threaten our brand or upset our donors. We trade in incremental change."
And so we find ourselves reinforcing the social, economic and political systems we once set out to transform. We have become part of the problem, rather than the solution."
The article links to an open letter signed by many of the founders and leaders of the organization - there they state:
"Around the world, ordinary people are losing trust in the global governance system.  They have little faith in elected governments and public institutions. They do not believe that big corporations tell them the truth. They see the international intergovernmental system as irrelevant at best and ineffectual at worst.  ...

Yet still they dream of equality and rights.  Indeed, beyond dreaming, many actively fight for it in their daily lives.  Across all continents, people rise up on the streets, in slums and villages and towns and cities, in protest to demand jobs and decent education and health for their communities.
They have done so to end corruption, they have marched to demand participation in the decisions that affect their lives and they have risen to demand basic services like water and sanitation. ...



Sadly, those of us who work in civil society organisations nationally and globally have come to be identified as part of the problem.  We are the poor cousins of the global jet set.  We exist to challenge the status quo, but we trade in incremental change.  ...

A new and increasingly connected generation of women and men activists across the globe question how much of our energy is trapped in the internal bureaucracy and the comfort of our brands and organisations.  They move quickly, often without the kinds of structures that slow us down.  ..."
This is a call for reflection, renewal and reconsideration. Of an entire sector. By its leaders. 

We need civil society. People and societies need the space to protest, to make change, to protect minority rights, to express differences, arts, values, and opinions that don't win the votes of a majority or the dollars of industry.

I take heart in this call for change. A call "from the top," to make necessary changes, to consider the activists, protestors, networks and new enterprise forms as allies not threats, as part of the future of civil society not just as threats to the past. We need to re-imagine how civil society will work and what it will look like, not give up on it. This is especially true right now, as we get smarter about digital tools and infrastructure, both of which can do great things but which also default to settings at cross purposes to many civil society values. We need to make the tools work for the values - this is what we mean when we talk about "ReCoding Good."

The whole social economy in one place

Nonprofit. Network. Benefit corporations. Peer-to-peer platforms, the sharing economy, social welfare nonprofits.... I've been writing about this mix for years - on this blog, in the Blueprint series, and at Stanford.

(photo: (c) Copyright Lucy Bernholz, All rights reserved.)

Today, Peers, a nonprofit membership association that owned and operated a for-benefit corporation - all in the service of the sharing economy - has now split into two organizations. One will be be a benefit corporation (and a B Corporation) and one will be a nonprofit organization. One will raise investment capital, the other will rely on memberships and donations. Both organizations have stopped focusing on the sharing economy companies and started focusing on the people who use those platforms for work (the drivers, shoppers, and room-renter-outers). You can read all about the transition here.

It's like all those little bubbles in one. Not your grandmother's nonprofit sector.

Oh, and on this topic, I'm pleased to have contributed the Foreword to a new book, Understanding the Social Economy in the United States, due out in February from University of Toronto Press.

Data and Civil Rights

In 12 years of blogging, I've never done this before.

I urge you to go to the website Data and Civil Rights, download all of the papers, and read every single one. Then think about your life, your work and your next steps. Whatever area of civil society you focus on - education, health, criminal justice, employment, finance, health or housing - there are materials there for you. (What happened to the environment?)

I was not involved in the conference that the website documents. I wish I had been. This work matters to all us. All of us need to think about these issues, engage in the conversations and research, and take what is being learned and apply it to our efforts. Here's what the conference organizers had to say in preparing their follow up:

"This event built on the civil rights community’s efforts in producing the Civil Rights Principles for the Era of Big Data and the lessons learned by the White House in their 2014 review of big data. The White House’s report – “Big Data: Seizing Opportunities, Preserving Values – highlighted the need to better understand the potential for discrimination, inequality, and other civil rights issues. While technology-minded communities have considered the potential and challenges presented by “big data,” these techniques and data practices affect more than the technology sector. Many issues central to the civil rights community – including criminal justice, education, employment, finance, health, and housing – are being affected by “big data” dynamics. In order to better understand what is at stake for our civil rights, we sparked a conversation that crossed sectors to identify a path forward. We wanted to better understand technology’s potential and develop a grounded sense of where there are concerns and what we can do to prevent problems.
This site offers documentation of the event, including written primers that attendees used during the event to explore key issues as well as write-ups of the discussions themselves.
Please send any feedback or ideas to nextsteps at datacivilrights.org."
Several years ago I wrote to the nonprofit and foundation community that "how we use our digital data will define us." The conversations and papers from this website put detail, pain, and possibility to that statement. 

I hope to incorporate much of this work into the 2015 plans and products for the Digital Civil Society Lab. I am so glad this work is being done.


Philanthropy Buzzwords 2015

I have been keeping a list. Checking it twice. (And doing so since 2007).

Below are the Philanthropy Buzzwords to look out for 2015. The list is also featured on the Chronicle of Philanthropy's site.


The 2015 Blueprint - which goes global this year with help from the great folks at betterplace lab  - is now live. It has a buzzword list + special "design school edition" of buzzwords, predictions for the year ahead, and all the usual annual goodies. This is the 6th annual forecast - get your free copy at GrantCraft.

1. Internet of Things

It’s no longer just your laptop and your phone that hook you in to the online world. Digital connections are now linking our watches, shoes, refrigerators, thermostats, cars, and almost anything else that can hold a teeny-tiny chip. Each of these devices becomes a sensor—a collector and distributor—of data about our habits, our activities, and us. More promise and more peril await nonprofits and the people they serve as a result of this transformation. The Internet of Things is also known as ubiquitous computing or the "web with many things."

2. Citizen Science

As the cost of materials, equipment, and information drop, the do-it-yourself and "maker" movements are turning to garage biology, chemistry, and physics. Teenager Jack Andraka made headlines as a self-taught cancer researcher who discovered a pathbreaking way to detect pancreatic cancer early, and Public Lab has launched numerous well-known science projects for social good. Lots of people engaging in science is a good thing. On the other hand, given the ubiquity of data-collecting devices (see Internet of Things), we’ll surely have more occasions to ask, "How did they get that information?" and "Who should be monitoring the scientists?"

3. Giving Days

Dedicating a specific day to fundraising for a certain cause has a long history. Galvanizing lots of people around challenge grants has been a mainstay fundraising tool deployed by American community foundations for several years. But with the spectacular success of Giving Tuesday, a re-branding of the first Tuesday after Thanksgiving to focus on charitable giving, these events have reached a new pitch. In its third year, the event has gone global and become a much-watched case example of using social media for good.

4. A/B Testing

This is the practice of showing different interfaces or options to specific groups of people and seeing which one is best at generating the behavior you want to spark. Commonly used by direct-marketing firms, software developers, and interface designers, A/B testing entered common parlance with the Obama presidential campaign’s widespread use of it in testing fundraising emails. The 2014 Facebook "contagion" study, which used algorithmic manipulation to see whether happy or grim news changed how people behaved, reminded us that the software behind our screens is making choices about what we see.

5. Data Gender Gap

Gender disparities abound in data. Yes, even today medical research is still done mostly on men (or male mice). Many other large sets of data are used to inform policy or grant-making decisions, despite the built-in biases created by omission. Similarly, large collections of data also abound in—and can reinforce and exacerbate—racial, ethnic, linguistic, geographic, and economic biases. Look for resources on data discrimination and the built-in biases of data analytics and prediction to get far more (much-needed) attention in 2015.

6. Encryption

Human-rights activists are on the front edge of creating and using secure technologies to stay clear of corporate and government oversight. Major foundations and large nonprofits have become targets for hackers, whether they’re looking for sensitive grant information or stealing donor information. A new British nonprofit, Simply Secure, makes encrypted software for email and mobile phones easier to use and more readily available. Nowadays, security is about more than not clicking on the suspicious link in that phishing email. Nonprofits and foundations will be taking more steps to keep their abundant digital data secure.

7. Artivists

Take art, mix it with activism, and you get artivists. Whether it’s posters and sculptures in public squares or the artistic protest associated with the Occupy Wall Street movement, artivists are stepping out of the shadows and into the limelight. There’s a book of case studies, Beautiful Trouble, to help inspire and coach. Art played a role in the 2014 Hong Kong protests and is part of an effort by cyclists in Germany to connect crowdsourced data on biking routes to public art projects, all in the name of changing public policy.

8. Wearables

Bracelet-style fitness monitors, upmarket pedometers masquerading as jewelry, and digital-sensor-enabled clothing to monitor sweat patterns or heart rhythms are just the latest ways people are wearing devices to connect them to the Internet. Opportunities to donate to charity based on your "steps walked" emerged almost instantly after Fitbit tracker became popular. These devices also fed a widely publicized data visualization of how the 2014 Napa Valley earthquake disturbed sleep, which may be a harbinger of how big data will be used. The data from these devices have already made it into courtroom battles. The more common these devices become, the more people resemble walking, talking cheap data points.

9. Smart Cities

More and more of the world’s population now lives in cities. Cheap materials and improved data-collection processes mean our cities are filled not only with more people but with more sensors, cameras, parking-space sensors, tollgate passes, building codes, heat meters, you name it. If it’s being built into today’s cityscape, it probably gathers data ("senses") and sends that information somewhere.

The goal is to use all this remotely gathered information to improve municipal services, making our cities "smart." Smart will require that we set the right rules for what is gathered and what is done with it.

10. Iterate

The dictionary tells us that iterate means to do again and again. In its buzzword guise, it is one of many design terms that has jumped the rhetorical fence, pulled along by related terms like "innovate," into philanthropy. Sexier than your grandmother’s pilot program, iterations mean trying something small, learning from it, and improving as you go along.