Posts tagged trust

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.

The meaning of trust in the age of Airbnb

trust, economics, community, collaboration, culture, prosperity

Since Germany is one of the most successful economies in the world and Bavaria is one of the most successful economies in Germany, the thought did cross my mind that trust might be one of the secrets of economic success. Steve Knack, an economist at the World Bank with a long-standing interest in trust, once told me that if one takes a broad enough view of trust, “it would explain basically all the difference between the per capita income of the United States and Somalia”. In other words, without trust — and its vital complement, trustworthiness — there is no prospect of economic development. Simple activities become arduous in a low-trust society. How can you be sure you won’t be robbed on the way to the corner store? Hire a bodyguard? (Can you trust him?) The watered-down milk is in a locked fridge. As for something more complex like arranging a mortgage, forget about it. Prosperity not only requires trust, it also encourages it. Why bother to steal when you are already comfortable?


Why don’t we trust the experts?

psychology, trust, experts, post-factual, metaknowledge, context

So here we have a partial answer to why experts aren’t trusted. They aren’t trusted by people who feel alienated from them. My reading of this study would be that it isn’t that we live in a ‘post-fact’ political climate. Rather it is that attempts to take facts out of their social context won’t work. For me and my friends it seems incomprehensible to ignore the facts, whether about the science of vaccination, or the law and economics of leaving the EU. But me and my friends do very well from the status quo - the Treasury, the Bar, the University work well for us. We know who these people are, we know how they work, and we trust them because we feel they are working for us, in some wider sense. People who voted Leave do suffer from a lack of trust, and my best guess is that this is a reflection of a belief that most authorities aren’t on their side, not because they necessarily reject their status as experts.


Ignore the GPS. That Ocean Is Not a Road.

GPS, navigation, trust, technology, algoritmics, determinism, travel

Faith is a concept that often enters the accounts of GPS-induced mishaps. “It kept saying it would navigate us a road,” said a Japanese tourist in Australia who, while attempting to reach North Stradbroke Island, drove into the Pacific Ocean. A man in West Yorkshire, England, who took his BMW off-road and nearly over a cliff, told authorities that his GPS “kept insisting the path was a road.” In perhaps the most infamous incident, a woman in Belgium asked GPS to take her to a destination less than two hours away. Two days later, she turned up in Croatia.

Provenance and Trust

Provenance, trust, systems, science, Yolanda Gil, traces

Provenance refers to the origins of objects. Software systems should generate provenance records for their results, containing assertions about the entities and activities involved in producing and delivering or otherwise influencing that object. By knowing the provenance of an object, we can for example make assessment about its validity and whether it can be trusted, we can decide how to integrate it with others, and can validate that it was generated according to specifications.