Posts tagged decision making

Can speculative evidence inform decision making?

Medium, Anab Jain, futures, decision making, choice, uncertainty, evidence, speculation, data, 2017, Superflux

Over at Superflux, our work investigating potential and plausible futures, involves extensively scanning for trends and signals from which we trace and extrapolate into the future. Both qualitative and quantitative data play an important role. In doing such work, we have observed how data is often used as evidence, and seen as definitive. Historical and contemporary datasets are often used as evidence for a mandate for future change, especially in some of the work we have undertaken with governments and policy makers. But lately we have been thinking if this drive for data as evidence has led to the unshakeable belief that data is evidence.


Rules for trusting “black boxes” in algorithmic control systems

algortihmics, trust, black boxes, security, decision making, prediction, data, machine learning, ethics


Tim O'Reilly writes about the reality that more and more of our lives – including whether you end up seeing this very sentence! – is in the hands of “black boxes” – algorithmic decision-makers whose inner workings are a secret from the people they effect.

O'Reilly proposes four tests to determine whether a black box is trustable:

1. Its creators have made clear what outcome they are seeking, and it is possible for external observers to verify that outcome.

2. Success is measurable.

3. The goals of the algorithm’s creators are aligned with the goals of the algorithm’s consumers.

4. Does the algorithm lead its creators and its users to make better longer term decisions?

O'Reilly goes on to test these assumptions against some of the existing black boxes that we trust every day, like aviation autopilot systems, and shows that this is a very good framework for evaluating algorithmic systems.

But I have three important quibbles with O'Reilly’s framing. The first is absolutely foundational: the reason that these algorithms are black boxes is that the people who devise them argue that releasing details of their models will weaken the models’ security. This is nonsense.

For example, Facebook’s tweaked its algorithm to downrank “clickbait” stories. Adam Mosseri, Facebook’s VP of product management told Techcrunch, “Facebook won’t be publicly publishing the multi-page document of guidelines for defining clickbait because ‘a big part of this is actually spam, and if you expose exactly what we’re doing and how we’re doing it, they reverse engineer it and figure out how to get around it.’”

There’s a name for this in security circles: “Security through obscurity.” It is as thoroughly discredited an idea as is possible. As far back as the 19th century, security experts have decried the idea that robust systems can rely on secrecy as their first line of defense against compromise.

The reason the algorithms O'Reilly discusses are black boxes is because the people who deploy them believe in security-through-obscurity. Allowing our lives to be manipulated in secrecy because of an unfounded, superstitious belief is as crazy as putting astrologers in charge of monetary policy, no-fly lists, hiring decisions, and parole and sentencing recommendations.

So there’s that: the best way to figure out whether we can trust a black box is the smash it open, demand that it be exposed to the disinfecting power of sunshine, and give no quarter to the ideologically bankrupt security-through-obscurity court astrologers of Facebook, Google, and the TSA.

Then there’s the second issue, which is important whether or not we can see inside the black box: what data was used to train the model? Or, in traditional scientific/statistical terms, what was the sampling methodology?

Garbage in, garbage out is a principle as old as computer science, and sampling bias is a problem that’s as old as the study of statistics. Algorithms are often deployed to replace biased systems with empirical ones: for example, predictive policing algorithms tell the cops where to look for crime, supposedly replacing racially biased stop-and-frisk with data-driven systems of automated suspicion.

But predictive policing training data comes from earlier, human-judgment-driven stop-and-frisk projects. If the cops only make black kids turn out their pockets, then all the drugs, guns and contraband they find will be in the pockets of black kids. Feed this data to a machine learning model and ask it where the future guns, drugs and contraband will be found, and it will dutifully send the police out to harass more black kids. The algorithm isn’t racist, but its training data is.

There’s a final issue, which is that algorithms have to have their models tweaked based on measurements of success. It’s not enough to merely measure success: the errors in the algorithm’s predictions also have to be fed back to it, to correct the model. That’s the difference between Amazon’s sales-optimization and automated hiring systems. Amazon’s systems predict ways of improving sales, which the company tries: the failures are used to change the model to improve it. But automated hiring systems blackball some applicants and advance others, and the companies that makes these systems don’t track whether the excluded people go on to be great employees somewhere else, or whether the recommended hires end up stealing from the company or alienating its customers.

I like O'Reilly’s framework for evaluating black boxes, but I think we need to go farther.