The Ethics of the Possible - Chatbotic Sermons

Interesting piece by Deena Prichep, in which clergy agonize as to the ethics of using a chatbot to construct a sermon.

The first point is that it is easy - perhaps too easy. ChatGPT currently advertises its sermon-writing services as follows: 

Your preaching companion. Transform Your Message into Impactful Sermons. Just provide your topic, choose from three tailor-made outlines, and let's co-create a captivating sermon. Fully adaptable to your congregation's needs - denomination, duration, tone, and language.

And for busy clergy the results seem almost touched by the Holy Spirit (aka Ghost in the Machine). Prichep quotes a Lutheran pastor whose first reaction was Oh my God, this is really good. (I may be doing my own research here, but I think there may be something in the Bible about taking the name of the Lord in vain.)

But just because you can doesn't mean you should. One of the arguments in favour of letting a large language model write your sermons for you is that it frees up your time to do more important things, like pastoral care. But are these things really more important? Brad East argues (following Calvin) that the primary task of ministry is the service of Word and sacrament, and that use of Artificial Intelligence shortchanges something essential.

So the underlying principle here seems to be that it might be okay to use AI tools for less important tasks but not for your most important task.

However, there are some other issues with the use of AI tools, including the environmental cost. And East notes the possiblity that large language models might fabricate material as well as pushing a particular agenda, although one might think preachers have always been able to do this without the aid of technology.

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Brad East, AI Has No Place in the Pulpit (Christianity Today, 27 September 2023)

Deena Prichep, We asked clergy if they use AI to help write sermons. Here's what they said (NPR 17 July 2025) HT Carissa Véliz

Deena Prichep, Encore: Religion and AI, what does it mean when the word of God comes from a chatbot? (NPR 19 July 2025)

John Rector, The Ghost in the Machine (19 June 2024) 

Brad Turner, Beatitudes or Platitudes (Milton Church of Christ, 19 December 2021) 

Contagion

The mathematician and broadcaster Hannah Fry did some post-doctoral research modelling social disturbance and crime in terms of contagion. In 2014, she gave a presentation at a conference in Berlin about her work with the police attempting to model the patterns of the 2011 London riots. As she said later, it didn't occur to her that a Berlin audience might have a different perspective on police power than a British audience would, and she got (in her words) absolutely torn apart. 2018 video 11:20

Several members of the audience expressed concerns about handing over too much control to the police, not only in giving them the power to suppress different forms of disturbance, but also in biasing the data on which the mathematical models were based. One person asked whether the data could really represent who the rioters were, referring to sociological research showing that police arrests are anything but neutral ... underprivileged groups of society tend to be arrested more. 2014 video 55:40

Another person noted how the riots depended not only on the behaviour of the rioters and the police, but also on the behaviour of the bystanders, which varied in different parts of London. If the Turkish community on London dealt robustly with the situation without relying on the police, this might be linked to the relationship between police and public in Turkey. In her response, Fry also noted the influence of the British media on the behaviour of bystanders in such situations.

While noting concerns about privacy, and agreeing that handing over too much control to technology is a really scary thing, Fry attempted to balance this against the claim that there is something positive to be gained by looking at the macro level behaviour of people in the way that we can design our society. 2014 video 52:50

In her more recent talks, Professor Fry has been more careful to put mathematical modelling into an ethical frame, as well as encouraging people to question the authority of the algorithm.

When it comes to algorithms, you can't just build them, put them on a shelf, and decide whether they're good or bad in isolation. You have to think about how they are actually going to be used by people. 2018 video 11:40

Once you dress something up as an algorithm or as a bit of artificial intelligence it can take on this air of authority that makes it really hard to argue with. 2018 video 26:10

Peter Polack provides a more fundamental challenge to the something positive claim. He traces the genealogy of this idea from August Comte's social physics to latter-day neorationalism, referencing Michel Foucault's notion of biopower and biopolitics.

Meanwhile, if social disorder appears to follow the same mathematical patterns as contagious disease, and the police are being invited to treat crime as a disease, perhaps it is not surprising when disease (or even the possibility of being infectious) starts to be treated as a crime.

The protective measures during the COVID pandemic include lock-down and self-isolation. So-called social distancing really means physical distancing, with as much social interaction as your technology (from phones to Internet) can provide. This is a lot easier for people with reasonably large houses, good internet connections, and devices for each member of the family, as well as the kinds of jobs that are relatively easy to do from home. For people in cramped housing, and for people who actually need to turn up at work if they want to get paid, self-isolation is a luxury they may not be able to afford. Therefore being tested for COVID may also be a luxury they can't afford.

Hannah Fry's mathematical model of the London riots identified that many of those arrested were from disadvantaged areas, although as we've seen this finding can be interpreted in more than one way. A model of disease might also show increased infection in disadvantaged areas. Maps of disadvantage and disease show strong persistence over time, as I discuss in my post on Location, Location, Location, quoting a study by Dr Douglas Noble and his colleagues.

But the COVID testing data are not going to show this pattern if people from disadvantaged areas are reluctant to come forward for testing. So much for biopower then.

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Hannah Fry, I predict a riot (re:publica 2014, May 2014) recording via YouTube

Hannah Fry, Contagion: The BBC Four Pandemic (BBC March 2018) recording not currently available

Hannah Fry, Should Computers Run the World (Royal Institution, November 2018) recording via YouTube

Douglas Noble et al, Feasibility study of geospatial mapping of chronic disease risk to inform public health commissioning. BMJ Open 2012;2:e000711 doi:10.1136/bmjopen-2011-000711

Peter Polack, False Positivism (Real Life Mag, 18 October 2021) HT @jjn1

Stanford Encyclopedia of Philosophy: August Comte, Michel Foucault

Related posts: Location, Location, Location (February 2012), Algorithms and Governmentality (July 2019), Algorithmic Bias (March 2021)

This is not who we are

@jesslynnrose offers an allegory for an unnamed technology company with dubious ethics.

The function of a system is its output.

If you have dog grooming machine that sometimes smashes puppies and you keep running it, you're in the dog smashing business.

If you work for a mass surveillance company that keeps enabling genocide and undermining democracy...

— Jessica Rose (@jesslynnrose) January 7, 2021

 

You might try to guess whether there is any particular technology company she is talking about. People from at least three different companies thought she might be referring to them.

I've had people from three different companies message to ask if I'm attacking them personally and this feels like Schrodinger's take on self knowledge. pic.twitter.com/a2xV1aMJQv

— Jessica Rose (@jesslynnrose) January 8, 2021

A common form of defensive denial takes the form This is not who we are, which @AlexGraul describes as an oxymoron. @ayourtch reinforces this point by quoting from Donella Meadows: Purposes are deduced from behaviour, not from rhetoric or stated goals

In other words, POSIWID.

 

But why does this count as an oxymoron? Because it seems to be openly acknowledging the behaviour that contradicts the espoused identity. 

In some cases, the contradiction appears to be resolved if we believe that the behaviour of a minority is not characteristic of the majority - as if the minority were not fully part of the "we". Bill Clinton used the phrase in 1995 following the verdict in the O.J. Simpson trial, and Barack Obama used the phrase many times. It has also been used on the Republican side. Christopher Scalia calls this a rhetorical sleight-of-hand.

In a corporate setting, executives use this kind of language to blame bad things on the actions of individual rogue employees rather than the corporation as a whole. Yeah, right.

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Donella Meadows, Thinking in Systems (2008). The quote is on page 14 of my copy.

Christopher J. Scalia, Why Obama says That's not who we are (USA Today, 8 February 2016)

See also The Fallacy of Rotten Apples (July 2004)

Algorithmic Bias

I have written several blogposts touching on various aspects of #AlgorithmicBias. This post summarizes my position, with links to further blogposts and other supporting material. 

 

1. Algorithmic bias exists. As Cathy O'Neil says, algorithms are opinions embedded in code.

 

2. Algorithmic bias can have negative consequences for individuals and society, and is therefore an ethical issue. Algorithms may be performative (producing what they claim to predict) and final (producing conclusions that are incapable of revision). Furthermore, because of its potential scaling effects, a biased algorithm may have much broader consequences than a biased human being. Hence the title of O'Neil's book, Weapons of Math Destruction.

3. Algorithms often have an aura of infallibility. People may be misled by an illusion of objectivity and omniscience.

 

4. There are different kinds of bias. People may disagree about the relative importance of these. This is one of the reasons why the notion of media balance and impartiality is problematic.

5. Attempts to eliminate one kind of bias may reinforce another kind of bias. It may not be possible to eliminate all kinds of bias simultaneously.

 

6. Attempts to address algorithmic bias may distract from other ethical issues. Furthermore, researchers may be pushed or pulled into solving algorithmic bias as a fascinating technical problem. Julia Powles calls this a seductive diversion.

 

Note: Points 4 and 5 are not exclusively about algorithmic bias, but apply also to other kinds of systemic bias.

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Further discussion and links in the following posts. 

Reinforcing Stereotypes (May 2006). Early evidence of search engines embedding human bias.

Weapons of Math Destruction (October 2016). Links to book reviews, talks and other material relating to Cathy O'Neil's book.

Transparency of Algorithms (October 2016). Policy-makers are often willing to act as if algorithms are somehow above human error and human criticism. But when people are sent to prison based on an algorithm, or denied a job or health insurance, it seems reasonable to allow them to know what criteria these algorithmic decisions were based on. Reasonable but not necessarily easy. 

The Road Less Travelled - Whom does the algorithm serve? (June 2019). In general, an algorithm is juggling the interests of many different stakeholders, and we may assume that this is designed to optimize the commercial returns to the algorithm-makers.

The Game of Wits Between Technologists and Ethics Professors (June 2019). Technology companies fund ethics researchers to work on obscure philosophical problems and technical fixes.

Algorithms and Auditability (July 2019). Looking at proposed remedies to algorithmic bias.

Algorithms and Governmentality (July 2019). Looking at the use of algorithms to support bureaucratic biopower (Foucault). If the prime job of the bureaucrat is to compile lists that could be shuffled and compared (Latour), then this function is increasingly being taken over by the technologies of data and intelligence - notably algorithms and so-called big data.

Limitations of Machine Learning (July 2020). Problems resulting from using biased datasets to train machine learning algorithms.

Bias or Balance (March 2021). Discusses how different stakeholders within Facebook prioritize different kinds of bias, comparing this with approaches to impartiality and balance in other media organizations including the BBC.

Does the algorithm have the last word? (February 2022). Algorithms may be performative (producing what they claim to predict) and final (producing conclusions that are incapable of revision).  See also On the Performativity of Data (August 2021) and Can Predictions Create their Own Reality (August 2021).

 

 

Lots of references and links in the above posts. Here are some of the main sources.

Cathy O'Neil, How algorithms rule our working lives (Guardian, 1 Sept 2016)

Cathy O'Neil, Weapons of Math Destruction (Crown Books, 2016)

Julia Powles, The Seductive Diversion of ‘Solving’ Bias in Artificial Intelligence (7 December 2018)

Wikipedia: Algorithmic bias  

Bias or Balance?

@_KarenHao has written a detailed exposé of Facebook's approach to ethics. In addition to some useful material about political polarization, which I have discussed in previous posts, the article contains some insight into the notion of bias preferred by Mark Zuckerberg and Joel Kaplan (VP Global Public Policy). 

The article describes the work of several ethics teams within Facebook, including SAIL (Society and AI Lab) and Responsible AI. There were various challenges that these teams identified as important, including polarization and misinformation. However, because of Kaplan’s and Zuckerberg’s worries about alienating conservatives, they were directed to focus on algorithmic bias.

Narrowing SAIL’s focus to algorithmic fairness would sideline all Facebook’s other long-standing algorithmic problems. Its content-recommendation models would continue pushing posts, news, and groups to users in an effort to maximize engagement, rewarding extremist content and contributing to increasingly fractured political discourse.

The Responsible AI team produced a tool called Fairness Flow, intended to measure the accuracy of machine-learning models for different user groups. The research team took the view that

when deciding whether a misinformation model is fair with respect to political ideology, ... fairness does not mean the model should affect conservative and liberal users equally. If conservatives are posting a greater fraction of misinformation, as judged by public consensus, then the model should flag a greater fraction of conservative content. If liberals are posting more misinformation, it should flag their content more often too.

But according to Hao, Kaplan's team took the opposite view:

they took fairness to mean that these models should not affect conservatives more than liberals. When a model did so, they would stop its deployment and demand a change. Once, they blocked a medical-misinformation detector that had noticeably reduced the reach of anti-vaccine campaigns, the former researcher told me. They told the researchers that the model could not be deployed until the team fixed this discrepancy. But that effectively made the model meaningless.

On this evidence, Facebook seems to be following pretty much the same narrow approach to balance and impartiality that responsible news organizations claim now to be trying to move away from. Perhaps the most egregious example of this approach in recent times was the coverage of climate change. For many years, the BBC felt it necessary to invite a climate change denier to debate any discussion of climate change. In 2018, they acknowledged that this was a mistake.

Politicians often complain to news organizations that their party is being treated unfairly. The traditional belief is that if you are getting similar numbers of complaints from both sides, you are probably getting things about right. However, this assumes that politics is symmetrical, with exactly two sides to any given argument. Professor Angela Phillips, one of the founders of the Media Reform Coalition, quotes research from Loughborough University showing that the BBC’s obsession with balance took Labour off air ahead of Brexit, because of the belief that a fair balance between Remain and Leave could be largely achieved by close coverage of the conflicts within the Conservative party.

One politician who has regularly complained about a lack of coverage on the BBC is Nigel Farage. Writing from a Scottish Nationalist perspective, the Jouker argues that the BBC responds to such complaints by giving him the oxygen of publicity he craves, while denying equivalent or fair coverage to the SNP. And as Simon Read notes,

It was the mainstream media that gave Mr Farage all the publicity he has wanted over the past couple of decades, including a record number of appearances on the BBC’s Question Time and his own show on radio station LBC. ... Without a doubt, he is the establishment – apart from his failure to become an MP despite 25 years of trying – and to paint himself as otherwise is rather disingenuous.

Stuart Cosgrove argues:

Due impartiality is one of the load-bearing props of the BBC’s producer guidelines. Not only is it a concept that is easily unpicked, I would argue that it has run its course as a guiding principle and is now singularly unsuited to a society where the media is fragmented, where views do not sit comfortably on the see-saw of balance and when the digital world has disrupted television’s authority.

Quite so.

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Damian Carrington, BBC admits we get climate change coverage wrong too often (The Guardian, 7 September 2018)

Centre for Research in Communication and Culture, Media Coverage of the EU Referendum 5 (Loughborough University, 27 June 2016)

Stuart Cosgrove, Emily Maitlis row exposes BBC's outdated obsession with due impartiality (The National, 31 May 2020)

Karen Hao, How Facebook got addicted to spreading misinformation (MIT Technology Review, 11 March 2021)

Angela Phillips, How the BBC’s obsession with balance took Labour off air ahead of Brexit (The Conversation, 14 July 2016)

Simon Read, Beware Farage's advice (FT Advisor, 21 October 2020)

The Jouker, BBC has explaining to do over record Farage Question Time appearance (The National, 10 May 2019)

See also tweet by @leobarasi via @tonyjoyce

Related posts: Polarization (November 2018), Polarizing Filters (March 2021), Algorithmic Bias (March 2021)

 

False Sense of Security

According to the dictionary, a false sense of security is a feeling of being safer than one really is. Apparently that's a bad thing.

Peter Sandman is a strong believer in what he calls precaution advocacy - to arouse some healthy outrage and use it to mobilize people to take precautions or demand precautions. He has helped environmental groups arouse public concern about the need for recycling, the dangers of factory emissions, etc. In such contexts, his concern is that people are disregarding or underestimating some category of risk, and he is urging the introduction of appropriate precautions - whether individual or collective.

There are countless risk and security experts who take a similar position - for example, advocating greater diligence in corporate security, especially cybersecurity.

However, as Dr Sandman acknowledges, the notion of a false sense of security is often used rhetorically, suggesting that a given regulation or other precaution is not only unnecessary but even counter-productive, making people careless or complacent. This argument is sometimes based on the notion of risk homeostasis or risk compensation - that people adjust their behaviour to maintain a comfortable level of risk. The classic example is people with seatbelts and airbags driving faster and more recklessly.

Dr Sandman notes that the rhetoric can sometimes be deployed by both sides of an argument - for example "gun controls create a false sense of security" versus "guns create a false sense of security". What this suggests is that the rhetoric is often about other people - the implication is that We have a true sense of security, but They would be misled.

The notion of a false sense of security also arises in connection with security theatre - a performance that may have little real impact on security, but is intended to reassure people that Something Is Being Done. When Bruce Schneier introduced this term in his 2003 book, he regarded security theatre as fraudulent, and believed it was always a Bad Thing. However, he later came to acknowledge that security theatre, while still deceptive and potentially problematic, could sometimes be valuable. His example is security bracelets on newborn babies, which don't do much to protect against the actual but extremely small risk of abduction, but do a great deal to calm anxious parents. If Dr Sandman's precaution advocacy is targetted at situations of High Hazard, Low Outrage (in other words, people not worrying enough), then Security Theatre could be legitimately targetted at situations of Low Hazard, High Outrage (people worrying too much).

So perhaps sometimes giving people a false sense of security is ethically justified?

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Peter Glaskowsky, Bruce Schneier's New View on Security Theater (CNET, 9 April 2008)

Peter Sandman, False Sense of Security (25 May 2018), Precaution Advocacy (undated)

Bruce Schneier, Beyond Fear (2003), In Praise of Security Theatre (Wired, 25 January 2007)

Gerald Wilde, Risk homeostasis theory: an overview (Injury Prevention Vol 4 No 2, 1998)

Wikipedia: Risk Compensation, Security Theatre

Related posts: Surveillance and its Effects (May 2005), Technical Security and Context (September 2005), Hard Cases Make Bad Law (September 2009), The Illusion of Architecture (September 2012), Anxiety as a Cost (January 2013), Listening for Trouble (June 2019), Lie Detectors at Airports (April 2022)


Updated 28 June 2019. Thanks to Peter Sandman for comments.

Insurance and the Veil of Ignorance

Put simply, the purpose of insurance is to shift risk from the individual to the collective. When an individual cannot afford to bear a given risk, the individual purchases some risk cover from an organization - typically an insurance company or mutual - which spreads the risk over many individuals and is supposedly better able to bear these risks.

Individuals are sometimes obliged to purchase insurance - for example, car insurance before driving on the public roads, or house insurance before getting a mortgage. In some countries, there may be legal requirements to have some form of health insurance.

Insurance companies typically charge different premiums to different individuals depending on the perceived risk and the available statistics. For example, if young inexperienced drivers and very elderly drivers have more accidents, it would seem fair for these drivers to pay a higher premium.

Insurance companies therefore try to obtain as much information about the individual as possible, in order to calculate the correct premium, or even to decide whether to offer cover at all. But this is problematic for two reasons.

The first problem is about fairness, as these calculations may embed various forms of deliberate or inadvertent discrimination. As Joi Ito explains,

The original idea of risk spreading and the principle of solidarity was based on the notion that sharing risk bound people together, encouraging a spirit of mutual aid and interdependence. By the final decades of the 20th century, however, this vision had given way to the so-called actuarial fairness promoted by insurance companies to justify discrimination.

The second problem is about knowledge and what Foucault calls biopower. Just suppose your insurance company is monitoring your driving habits through sensors in the vehicle or cameras in the street, knows how much red meat you are eating, knows your drinking habits through the motion and location sensors on your phone, is inferring your psychological state from your Facebook profile, and has complete access to your fitness tracker and your DNA. If the insurance company now has so much data about you that it can accurately predict car accidents, ill-health and death, the amount of risk actually taken by the insurance company is minimized, and the risk is thrown back onto the individual who is perceived (fairly or unfairly) as a high-risk.

In her latest book, Shoshana Zuboff describes how insurance companies are using the latest technologies, including the Internet of Things, not only to monitor drivers but also to control them.

Telematics are not intended merely to know but also to do (economics of action). They are hammers; they are muscular; they enforce. Behavioral underwriting promises to reduce risk through machine processes designed to modify behavior in the direction of maximum profitability. Behavioral surplus is used to trigger punishments, such as real-time rate hikes, financial penalties, curfews, and engine lockdowns, or rewards, such as rate discounts, coupons, and gold stars to redeem for future benefits. The consultancy firm AT Kearney anticipates 'IoT enriched relationships' to connect 'more holistically' with customers 'to influence their behaviors'. (p215)

So much for risk sharing then. Surely this undermines the whole point of insurance?

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Sami Coll, Consumption as Biopower: Governing Bodies with Loyalty Cards, (Journal of Consumer Culture 13(3) 2013) pp 210-220

Caley Horan, Actuarial age: insurance and the emergence of neoliberalism in the postwar United States (PhD Thesis 2011)

Joi Ito, Supposedly ‘Fair’ Algorithms Can Perpetuate Discrimination (Wired Magazine, 5 February 2019) HT @WolfieChristl @zeynep

AT Kearney, The Internet of Things: Opportunity for Insurers (2014)

Cathy O'Neil, How algorithms rule our working lives (The Guardian, 1 September 2016)

Jathan Sadowski, Alarmed by Admiral's data grab? Wait until insurers can see the contents of your fridge (The Guardian, 2 November 2016)

Carissa Véliz, If AI Is Predicting Your Future, Are You Still Free? (Wired, 27 December 2021)

Shoshana Zuboff, The Age of Surveillance Capitalism (Profile Books 2019) esp pages 212-218


Stanford Encyclopedia of Philosophy: Foucault

Related posts: The Transparency of Algorithms (October 2016) Pay as you Share (November 2016), Shoshana Zuboff on Surveillance Capitalism (Book Review, February 2019) 

 

Update: I have just come across a journal special issue on the Personalization of Insurance (Big Data and Society, November 2020). I note that the editorial starts with the same Zuboff quote that I used here. Also adding link to a recent article by Professor Véliz.

Scissors Paper Stone 3

Discussing #Murdoch, @paulmasonnews argues that the network defeats the hierarchy. Mason tries to argue that the fall of News International represents a triumph for "the network", with particular reference to Facebook and Twitter. He references a book by Edward S. Herman and Noam Chomsky, Manufacturing Consent: The Political Economy of the Mass Media (Pantheon, 1988) (link), and also name-drops Slavoj Žižek.

But of course that's only one possible interpretation of recent events, and only one meaning of the word "network". Reading Adam Curtis's piece from a few months ago, ironically entitled Rupert Murdoch - A Portrait of Satan, we might instead get a picture of News International as (at least until recently) a supremely powerful network, which has now been (perhaps temporally) outmanoeuvred by the establishment hierarchy it for so long tried to subvert.

The establishment probably cares as little about poor Millie Dowler as it does about any foolish and over-sexed footballer. But when her mobile phone turns out to have been hacked, it gives everyone the perfect pretext to express indignation about the scurrilous tactics of a newspaper that has for decades been entertaining the working classes with the foibles of the rich and famous, as well as detailed accounts of crime. (Just read George Orwell on the Decline of the English Murder.)

While we may all deplore the tactics of the News of the World, investigative journalism is one of those activities we all benefit from while turning a blind eye to exactly how it is done. And how are we to hold the establishment to account, if the establishment sets up the rules of the game to make real investigative journalism as difficult and unprofitable as possible? Some moral as well as political dilemmas here.

Defence Against the Dark Arts

Following @TimHarford 's illuminating advice on the dark art of ‘drip pricing’ (Financial Times, 21 Aug 2010) and my post on badly designed websites, @gagan_s comments "Sometimes bad websites, phone-trees and policies have a dark purpose".

The word "dark" can mean "hidden", "obscure", "mysterious", "secret", "unconscious"; or it can mean "devious", "evil", "malicious", "treacherous".  Harford's use of the term "Dark Art" in relation to drip pricing clearly denotes a practice that is not just unclear and confusing but also morally questionable.

In terms of the dark purposes of behavioural economics, a website designed to exploit drip pricing needs to be just complicated enough that when the customer reaches the webpage that demands an additional payment for paying by credit card, the customers have already wasted so much time that it isn't worth starting again with a competing website, so most of them grumble but pay.

IT professionals who understand commercial software and/or website design also grumble about the incompetence of the developers of the website. That just goes to show how naive most IT professionals are, if they imagine that all companies genuinely want (and are willing to pay for) websites that are simple and easy to use. Or they may feel morally outraged that companies are not willing to invest the time and money to do things properly.

As regular readers of this blog will know, our starting point is Stafford Beer's maxim The Purpose Of a System Is What It Does (POSIWID). According to this principle, there is no such thing as an unnecessarily complicated website: the complication emerges from some conscious or unconscious dark purpose. In the case of drip pricing, the purpose is to chisel a few more dollars from weary and/or confused customers. This purpose may exist even if the managers of the company aren't aware of it; they may intend (one day) to simplify their pricing scheme, but they feel no pressing need to do so; meanwhile, the company continues to profit from complexity.

WiFi Minefield

Bruce Schneier blogs about Wi-Fi Minefield (April 2005).

"Put aside arguments about the ethics and efficacy of landmines. Assume they exist and are being used. Given that, the question is whether radio-controlled landmines are better or worse than regular landmines."

Is an intelligent landmine better than a dumb one? At one level perhaps it is, because it might be more discriminating about whose legs it blows off.

But then military planners may judge that this technology makes it okay to have a greater number of more powerful landmines (on both ethical and efficacy grounds). Furthermore, it makes it harder for the good guys to clear landmines. So now the landmine situation has escalated.

As an anonymous comment to Bruce's post puts it:

"I don't think arguments about the ethics and efficacy of landmines *can* be put aside here, because surely the "smartness" of these mines will be used to justify laying more of them."

The problem here isn't the intelligent technology itself, it's the stupid (man-made) judgements that the intelligent technology supports.

A levelling law seems to operate in many systems, by which a local increase in intelligence is compensated by a loss of intelligence elsewhere. So what's the purpose of intelligence here?