Showing posts with label algorithm. Show all posts
Showing posts with label algorithm. Show all posts

Sunday, April 10, 2022

Lie Detectors at Airports

@jamesvgingerich reports that the EU is putting lie detector robots on its borders. @Abebab is horrified.

 

There are several things worth noting here.

Firstly, lie detectors work by detecting involuntary actions (eye movements, heart rate) that are thought to be a proxy for mendacity. But there are often alternative explanations for these actions, and so the interpretation of these is highly problematic. See my post on Memory and the Law (June 2008)

Secondly, there is a lot of expensive and time-wasting technology installed at airports already, which has dubious value in detecting genuine threats, but may help to make people feel safer. Bruce Schneier calls this Security Theatre. See my posts on the False Sense of Security (June 2019) and Identity, Privacy and Security at Heathrow Terminal Five (March 2008).

What is more important is to consider the consequences of adding this component (whether reliable or otherwise) to the larger system. In my post Listening for Trouble (June 2019), I discussed the use of Aggression Detection microphones in US schools, following an independent study that was carried out with active collaboration from the supplier of the equipment. Obviously this kind of evaluation requires some degree of transparency.

Most important of all is the ethical question. Is this technology biased against certain categories of subject, and what are the real-world consequences of being falsely identified by this system? Is having a human in the loop sufficient protection against the dangers of algorithmic profiling? See Algorithmic Bias (March 2021).

Given the inaccuracy of detection, there may be a significant rate of false positives and false negatives. False positives affect the individual concerned, suffering consequences ranging from inconvenience and delay to much worse. False negatives mean that a person has got away with an undetected lie, so this has consequences for society as a whole. 

How much you think this matters depends on what you think they might be lying about, and how important this is. For example, it may be quicker to say you packed your suitcase yourself and it hasn't been out of your sight, even if this is not strictly true, because any other answer may trigger loads of other time-wasting questions. However, other lies may be more dangerous ...

 


For more details on the background of this initiative, see

Matthias Monroy, EU project iBorderCtrl: Is the lie detector coming or not? (26 April 2021)

Thursday, July 18, 2019

Robust Against Manipulation

As algorithms get more sophisticated, so do the opportunities to trick them. An algorithm can be forced or nudged to make incorrect decisions, in order to yield benefits to a (hostile) third party. John Bates, one of the pioneers of Complex Event Processing, has raised fears of algorithmic terrorism, but algorithmic manipulation may also be motivated by commercial interests or simple vandalism.

An extreme example of this could be a road sign that reads STOP to humans but is misread as something else by self-driving cars. Another example might be false signals that are designed to trigger algorithmic trading and thereby nudge markets. Given the increasing reliance on automatic screening machines at airports and elsewhere, there are obvious incentives for smugglers and terrorists to develop ways of fooling these machines - either to get their stuff past the machines, or to generate so many false positives that the machines aren't taken seriously. And of course email spammers are always looking for ways to bypass the spam filters.

"It will also become increasingly important that AI algorithms be robust against manipulation. A machine vision system to scan airline luggage for bombs must be robust against human adversaries deliberately searching for exploitable flaws in the algorithm - for example, a shape that, placed next to a pistol in one's luggage, would neutralize recognition of it. Robustness against manipulation is an ordinary criterion in information security; nearly the criterion. But it is not a criterion that appears often in machine learning journals, which are currently more interested in, e.g., how an algorithm scales upon larger parallel systems." [Bostrom and Yudkowsky]

One kind of manipulation involves the construction of misleading examples (known in the literature as "adversarial examples"). For example, an example that exploits the inaccuracies of a specific image recognition algorithm to produce an image that will be incorrectly classified, thus producing an incorrect action (or suppressing the correct action).

Another kind of manipulation involves poisoning the model - deliberately feeding a machine learning algorithm with biased or bad data, in order to disrupt or skew its behaviour. (Historical analogy: manipulation of pop music charts.)

We have to assume that some bad actors will have access to the latest technologies, and will themselves be using machine learning and other techniques to design these attacks, and this sets up an arms race between the good guys and the bad guys. Is there any way to keep advanced technologies from getting in the wrong hands?

In the security world, people are familiar with the concept of Distributed Denial of Service (DDOS). But perhaps this now becomes Distributed Distortion of Service. Which may be more subtle but no less dangerous.

While there are strong arguments for algorithmic transparency of automated systems, some people may be concerned that transparency will aid such attacks. The argument here is that the more adversaries can discover about the algorithm and its training data, the more opportunities for manipulation. But it would be wrong to conclude that we should keep algorithms safe by keeping them secret ("security through obscurity"). A better conclusion would be that transparency should be a defence against manipulation, by making it easier for stakeholders to detect and counter such attempts.




John Bates, Algorithmic Terrorism (Apama, 4 August 2010). To Catch an Algo Thief (Huffington Post, 26 Feb 2015)

Nick Bostrom and Eliezer Yudkowsky, The Ethics of Artificial Intelligence (2011)

Ian Goodfellow, Patrick McDaniel and Nicolas Papernot, Making Machine Learning Robust Against Adversarial Inputs (Communications of the ACM, Vol. 61 No. 7, July 2018) Pages 56-66. See also video interview with Papernot.

Neil Strauss, Are Pop Charts Manipulated? (New York Times, 25 January 1996)

Wikipedia: Security Through Obscurity

Related posts: The Unexpected Happens (January 2017)

Tuesday, July 16, 2019

Nudge Technology

People are becoming aware of the ways in which AI and big data can be used to influence people, in accordance with Nudge Theory. Individuals can be nudged to behave in particular ways, and large-scale social systems (including elections and markets) can apparently be manipulated. In other posts, I have talked about the general ethics of nudging systems. This post will concentrate on the technological aspects.

Technologically mediated nudges are delivered by a sociotechnical system we could call a Nudge System. This system might contain several different algorithms and other components, and may even have a human-in-the-loop. Our primary concern here is about the system as a whole.

As an example, I am going to consider a digital advertisement in a public place, which shows anti-smoking messages whenever it detects tobacco smoke.

Typically, a nudge system would perform several related activities.

1. There would be some mechanism for "reading" the situation. For example, detecting the events that might trigger a nudge, as well as determining the context. This might be a simple sense-and-respond or it might include some more sophisticated analysis, using some kind of model. There is typically an element of surveillance here. In our example, let us imagine that the system is able to distinguish different brands of cigarette, and determine how many people are smoking in its vicinity.

2. Assuming that there was some variety in the nudges produced by the system, there would be a mechanism for selecting or constructing a specific nudge, using a set of predefined nudges or nudge templates. For example, different anti-smoking messages for the expensive brands versus the cheap brands. Combined with other personal data, the system might even be able to name and shame the smokers.

3. There would then be a mechanism for delivering or otherwise executing the nudge. For example private (to a person's phone) or public (via a display board). We might call this the nudge agent. In some cases, the nudge may be delivered by a human, but prompted by an intelligent system. If the nudge is publicly visible, this could allow other people to infer the circumstances leading to the nudge - therefore a potential breach of privacy. (For example, letting your friends and family know that you were having a sneaky cigarette, when you had told them that you had given up.)

4. In some cases, there might be a direct feedback loop, giving the system immediate data about the human response to the nudge. Obviously this will not always be possible. Nevertheless, we would expect the system to retain a record of the delivered nudges, for future analysis. To support multiple feedback tempos (as discussed in my work on Organizational Intelligence) there could be multiple components performing the feedback and learning function. Typically, the faster loops would be completely automated (autonomic) while the slower loops would have some human interaction.

There would typically be algorithms to support each of these activities, possibly based on some form of Machine Learning, and there is the potential for algorithmic bias at several points in the design of the system, as well as various forms of inaccuracy (for example false positives, where the system incorrectly detects tobacco smoke). More information doesn't always mean better information - for example, someone might design a sensor that would estimate the height of the smoker, in order to detect underage smokers - but this obviously introduces new possibilities of error.

In many cases, there will be a separation between the technology engineers who build systems and components, and the social engineers who use these systems and components to produce some commercial or social effects. This raises two different ethical questions.

Firstly, what does responsible use of nudge technology look like - in other words, what are the acceptable ways that nudge technology can be deployed. What purposes, what kind of content, the need for testing and continuous monitoring to detect any signals of harm or bias, and so on. Should the nudge be private to the nudgee, or could it be visible to others? What technical and organizational controls should be in place before the nudge technology is switched on?

And secondly, what does responsible nudge technology look like - in other words, one that can be used safely and reliably, with reasonable levels of transparency and user control.

We may note that nudge technologies can be exploited by third parties with a commercial or political intent. For example, there are constant attempts to trick or subvert the search and recommendation algorithms used by the large platforms, and Alex Hern recently reported on Google's ongoing battle to combat misinformation and promotion of extreme content. So one of the requirements of responsible nudge technology is being Robust Against Manipulation.

We may also note that if there is any bias anywhere, this may either be inherent in the design of the nudge technology itself, or may be introduced by the users of the nudge technology when customizing it for a specific purpose. For example, nudges may be deliberately worded as "dog whistles" - designed to have a strong effect on some subjects while being ignored by others - and this can produce significant and possibly unethical bias in the working of the system. But this bias is not in the algorithm but in the nudge templates, and there may be other ways in which bias is relevant to nudging in general, so the question of algorithmic bias is not the whole story.



Alex Hern, Google tweaked algorithm after rise in US shootings (Guardian, 2 July 2019)

Wikipedia: Nudge Theory

Related posts: Organizational Intelligence in the Control Room (October 2010), On the Ethics of Technologically Mediated Nudge (May 2019), The Nudge as a Speech Act (May 2019), Algorithms and Governmentality (July 2019), Robust Against Manipulation (July 2019)