Friday, June 21, 2019

With Strings Attached

@rachelcoldicutt notes that "Google Docs new grammar suggestion tool doesn’t like the word 'funding' and prefers 'investment' ".

Many business people have an accounting mindset, in which all expenditure must be justified in terms of benefit to the organization, measured in financial terms. When they hear the word "investment", they hold their breath until they hear the word "return".

So when Big Tech funds the debate on AI ethics (Oscar Williams, New Statesman, 6 June 2019), can we infer that Big Tech sees this as an "investment", to which it is entitled to a return or payback?

Related post: The Game of Wits Between Technologists and Ethics Professors (June 2019)

Saturday, June 8, 2019

The Game of Wits between Technologists and Ethics Professors

What does #TechnologyEthics look like from the viewpoint of your average ethics professor? 

Not surprisingly, many ethics professors believe strongly in the value of ethics education, and advocate ethics awareness training for business managers and engineers. Provided by people like themselves, obviously.

There is a common pattern among technologists and would-be enterpreneurs to first come up with a "solution", find areas where the solution might apply, and then produce self-interested arguments to explain why the solution matches the problem. Obviously there is a danger of confirmation bias here. Proposing ethics education as a solution for an ill-defined problem space looks suspiciously like the same pattern. Ethicists should understand why it is important to explain what this education achieves, and how exactly it solves the problem.

Please note that I am not arguing against the value of ethics education and training as such, merely complaining that some of the programmes seem to involve little more than meandering through a randomly chosen reading list. @ruchowdh recently posted a particularly egregious example - see below.

Ethics professors may also believe that people with strong ethical awareness, such as themselves, can play a useful role in technology governance - for example, participating in advisory councils.

Some technology companies may choose to humour these academics, engaging them as a PR exercise (ethics washing) and generously funding their research. Fortunately, many of them lack deep understanding of business organizations and of technology, so there is little risk of them causing any serious challenge or embarrasment to these companies.

Professors are always attracted to the kind of work that lends itself to peer-reviewed articles in leading Journals. So it is fairly easy to keep their attention focused on theoretically fascinating questions with little or no practical relevance, such as the Trolley Problem.

Alternatively, they can be engaged to try and "fix" problems with real practical relevance, such as algorithmic bias. @juliapowles calls this a "captivating diversion", distracting academics from the more fundamental question, whether the algorithm should be built at all.

It might be useful for these ethics professors to have deeper knowledge of technology and business, in their social and historical context, enabling them to ask more searching and more relevant questions. (Although some ethics experts have computer science degrees or similar, computer science generally teaches people about specific technologies, not about Technology.) 

But if only a minority of ethics professors possess sufficient knowledge and experience, these will be overlooked for the plum advisory jobs. I therefore advocate compulsory technology awareness training for ethics professors, especially "prominent" ones. Provided by people like myself, obviously.

Stephanie Burns, Solution Looking For A Problem (Forbes, 28 May 2019)

Casey Fiesler, Tech Ethics Curricula: A Collection of Syllabi (5 July 2018), What Our Tech Ethics Crisis Says About the State of Computer Science Education (5 December 2018)

Mark Graban, Cases of Technology “Solutions” Looking for a Problem? (26 January 2011)

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

Oscar Williams, How Big Tech funds the debate on AI ethics (New Statesman, 6 June 2019)

Related posts:

Leadership and Governance (May 2019), Selected Reading List - Science and Technology Studies (June 2019), With Strings Attached (June 2019)

Updated 21 June 2019

Tuesday, May 28, 2019

Five Elements of Responsibility by Design

I have been developing an approach to #TechnologyEthics, which I call #ResponsibilityByDesign. It is based on the five elements of #VPECT. Let me start with a high-level summary before diving into some of the detail.

  • Why does ethics matter?
  • What outcomes for whom?

  • Principles and practices of technology ethics
  • Formal codes of practice, etc. Regulation.

Event-Driven (Activity Viewpoint)
  • Effective and appropriate action at different points: planning; risk assessment; design; verification, validation and test; deployment; operation; incident management; retirement. (Also known as the Process Viewpoint). 

Content (Knowledge Viewpoint)
  • What matters from an ethical point of view? What issues do we need to pay attention to?
  • Where is the body of knowledge and evidence that we can reference?

Trust (Responsibility Viewpoint)
  • Transparency and governance
  • Responsibility, Authority, Expertise, Work (RAEW)

Concerning technology ethics, there is a lot of recent published material on each of these elements separately, but I have not yet found much work that puts them together in a satisfactory way. Many working groups concentrate on a single element - for example, principles or transparency. And even when experts link multiple elements, the logical connections aren't always spelled out.

At the time of writing this post (May 2019), I haven't yet fully worked out how to join these elements either, and I shall welcome constructive feedback from readers and pointers to good work elsewhere. I am also keen to find opportunities to trial these ideas on real projects.

Related Posts

Responsibility by Design (June 2018) What is Responsibility by Design (October 2018) Why Responsibility by Design Now? (October 2018)

Sunday, May 19, 2019

The Nudge as a Speech Act

As I said in my previous post, I don't think we can start to think about the ethics of technology nudges without recognizing the complexity of real-world nudges. So in this post, I shall look at how nudges are communicated in the real world, before considering what their artificial analogues might look like.

Once upon a time, nudges were physical rather than verbal - a push on the shoulder perhaps, or a dig in the ribs with an elbow. The meaning was elliptical and depended almost entirely on context. "Nudge nudge, wink wink", as Monty Python used to say.

Even technologically mediated nudges can sometimes be physical, or what we should probably call haptic. For example, the fitness band that vibrates when it thinks you have been sitting for too long.

But many of the acts we now think of as nudges are delivered verbally, as some kind of speech act. But which kind?

The most obvious kind of nudge is a direct suggestion, which may take the form of a weak command. ("Try and eat a little now.") But nudges can also take other illocutionary forms, including questions ("Don't you think the sun is very hot here?") and statements / predictions ("You will find that new nose of yours very useful to spank people with.").

(Readers familiar with Kipling may recognize my examples as the nudges given by the Bi-Coloured-Python-Rock-Snake to the Elephant's Child.)

The force of a suggestion may depend on context and tone of voice. (A more systematic analysis of what philosophers call illocutionary force can be found in the Stanford Encyclopedia of Philosophy, based on Searle and Vanderveken 1985.)

@tonyjoyce raises a good point about tone of voice in electronic messages. Traditionally robots don't do tone of voice, and when a human being talks in a boring monotone we may describe their speech as robotic. But I can't see any reason why robots couldn't be programmed with more varied speech patterns, including tonality, if their designers saw the value of this.

Meanwhile, we already get some differentation from electronic communications. For example I should expect an electronic announcement to "LEAVE THE BUILDING IMMEDIATELY" to have a tone of voice that conveys urgency, and we might think it is inappropriate or even unethical to use the same tone of voice for selling candy. We might put this together with other attention-seeking devices, such as flashing red text. The people who design clickbait clearly understand illocutionary force (even if they aren't familiar with the term). 

A speech act can also gain force by being associated with action. If I promise to donate money to a given charity, this may nudge other people to do the same; but if they see me actually putting the money in the tin, the nudge might be much stronger. But then the nudge might be just as strong if I just put the money in the tin without saying anything, as long as everyone sees me do it. The important point is that some communication takes place, whether verbal or non-verbal, and this returns us to something closer to the original concept of nudge.

From an ethical point of view, there are particular concerns about unobtrusive or subliminal nudges. Yeung has introduced the concept of the Hypernudge, which combines three qualities: nimble, unobtrusive and highly potent. I share her concerns about this combination, but I think it is helpful to deal with these three qualities separately, before looking at the additional problems that may arise when they are combined.

Proponents of the nudge sometimes try to distinguish between unobtrusive (acceptable) and subliminal (unacceptable), but this distinction may be hard to sustain, and many people quote Luc Bovens' observation that nudges "typically work better in the dark". See also Baldwin.

I'm sure there's more to say on this topic, so I may update this post later. Relevant comments always welcome.

Robert Baldwin, From regulation to behaviour change: giving nudge the third degree (The Modern Law Review 77/6, 2014) pp 831-857

Luc Bovens, The Ethics of Nudge. In Mats J. Hansson and Till Grüne-Yanoff (eds.), Preference Change: Approaches from Philosophy, Economics and Psychology. (Berlin: Springer, 2008) pp. 207-20

John Danaher, Algocracy as Hypernudging: A New Way to Understand the Threat of Algocracy (Institute for Ethics and Emerging Technologies, 17 January 2017)

J. Searle and D. Vanderveken, Foundations of Illocutionary Logic (Cambridge: Cambridge University Press, 1985)

Karen Yeung, ‘Hypernudge’: Big Data as a Mode of Regulation by Design (Information, Communication and Society (2016) 1,19; TLI Think! Paper 28/2016)

Stanford Encyclopedia of Philosophy: Speech Acts

Related post: On the Ethics of Technologically Mediated Nudge (May 2019)

Updated 28 May 2019. Many thanks to @tonyjoyce

Friday, May 17, 2019

On the Ethics of Technologically Mediated Nudge

Before we can discuss the ethics of technologically mediated nudge, we need to recognize that many of the ethical issues are the same whether the nudge is delivered by a human or a robot. So let me start by trying to identify different categories of nudge.

In its simplest form, the nudge can involve gentle persuasions and hints between one human being and another. Parents trying to influence their children (and vice versa), teachers hoping to inspire their pupils, various forms of encouragement and consensus building and leadership. In fiction, such interventions often have evil intent and harmful consequences, but in real life let's hope that these interventions are mostly well-meaning and benign.

In contrast, there are more large-scale forms of nudge, where a team of social engineers (such as the notorious "Nudge Unit") design ways of influencing the behaviour of lots of people, but don't have any direct contact with the people whose behaviour is to be influenced. A new discipline has grown up, known as Behavioural Economics.

I shall call these two types unmediated and mediated respectively.

Mediated nudges may be delivered in various ways. For example, someone in Central Government may design a nudge to encourage job-seekers to find work. Meanwhile, YouTube can nudge us to watch a TED talk about nudging. Some nudges can be distributed via the Internet, or even the Internet of Things. In general, this involves both people and technology - in other words, a sociotechnical system.

To assess the outcome of the nudge, we can look at the personal effect on the nudgee or at the wider socio-economic impact, either short-term or longer-term. In terms of outcome, it may not make much difference whether the nudge is delivered by a human being or by a machine, given that human beings delivering the nudge might be given a standard script or procedure to follow, except in so far as the nudgee may feel differently about it, and may therefore respond differently. It is an empirical question whether a given person would respond more positively to a given nudge from a human bureaucrat or from a smartphone app, and the ethical difference between the two will be largely driven by this.

The second distinction involves the beneficiary of the nudge. Some nudges are designed to benefit the nudgee (Cass Sunstein calls these "paternalistic"), while others are designed to benefit the community as a whole (for example, correcting some market failure such as the Tragedy of the Commons). On the one hand, nudges that encourage people to exercise more; on the other hand, nudges that remind people to take their litter home. And of course there are also nudges whose intended beneficiary is the person or organization doing the nudging. We might think here of dark patterns, shades of manipulation, various ways for commercial organizations to get the individual to spend more time or money. Clearly there are some ethical issues here.

A slightly more complicated case from an ethical perspective is where the intended outcome of the nudge is to get the nudgee to behave more ethically or responsibly towards someone else.

Sunstein sees the "paternalistic" nudges as more controversial than nudges to address potential market failures, and states two further preferences. Firstly, he prefers nudges that educate people, that serve over time to increase rather than decrease their powers of agency. And secondly, he prefers nudges that operate at a slow deliberative tempo ("System 2") rather than at a fast intuitive tempo ("System 1"), since the latter can seem more manipulative.

Meanwhile, there is a significant category of self-nudging. There are now countless apps and other devices that will nudge you according to a set of rules or parameters that you provide yourself, implementing the kind of self-binding or precommitment that Jon Elster described in Ulysses and the Sirens (1979). Examples include the Tomato system for time management, fitness trackers that will count your steps and vibrate when you have been sitting for too long, money management apps that allocate your spare change to your chosen charity. Several years ago, Microsoft developed an experimental Smart Bra that would detect changes in the skin to predict when a women was about to reach for the cookie jar, and give her a friendly warning. Even if there is no problem with the nudge itself (because you have consented/chosen to be nudged) there may be some ethical issues with the surveillance and machine learning systems that enable the nudge. Especially when the nudging device is kindly made available to you by your employer or insurance company.

And even if the immediate outcome of the nudge is benefical to the nudgee, in some situations there may be concerns that the nudgee becomes over-dependent on being nudged, and thereby loses some element of self-control or delayed gratification.

The final distinction I want to introduce here concerns the direction of the nudge. The most straightforward nudges are those that push an individual in the desired direction. Suggestions to eat more healthy food, suggestions to direct spare cash to charity or savings. But some forms of therapy are based on paradoxical interventions, where the individual is pushed in the opposite directly, and they react by moving in the direction you want them to go. For example, if you want someone to give up some activity that is harming them, you might suggest they carry out this activity more systematically or energetically. This is sometimes known as reverse psychology or prescribing the symptom. For example, faced with a girl who was biting her nails, the therapist Milton Erickson advised her how she could get more enjoyment from biting her nails. Astonished by this advice, which was of course in direct opposition to all the persuasion and coercion she had received from other people up to that point, she found she was now able to give up biting her nails altogether.

(Richard Bordenave attributes paradoxical intervention to Paul Watzlawick, who worked with Gregory Bateson. It can also be found in some versions of Neuro-Linguistic Programming (NLP), which was strongly influenced by both Bateson and Erickson.)

Of course, this technique can also be practised in an ethically unacceptable direction as well. Imagine a gambling company whose official message to gamblers is that they should invest their money in a sensible savings account instead of gambling it away. This might seem like an ethically noble gesture, until we discover that the actual effect on people with a serious gambling problem is that this causes them to gamble even more. (In the same way that smoking warnings can cause some people to smoke more. Possibly cigarette companies are aware of this.)

Paradoxical interventions make perfect sense in terms of systems theory, which teaches us that the links from cause to effect are often complex and non-linear. Sometimes an accumulation of positive nudges can tip a system into chaos or catastrophe, as Donella Meadows notes in her classic essay on Leverage Points.

The Leverage Point framework may also be useful in comparing the effects of nudging at different points in a system. Robert Steele notes the use of a nudge based on restructuring information flows; in contrast, a nudge that was designed to alter the nudgee's preferences or goals or political opinions could be much more dangerously powerful, as @zeynep has demonstrated in relation to YouTube.

One of the things that complicates the ethics of Nudge is that the alternative to nudging may either be greater forms of coercion or worse outcomes for the individual. In his article on the Ethics of Nudging, Cass Sunstein argues that all human interaction and activity takes place inside some kind of Choice Architecture, thus some form of nudging is probably inevitable, whether deliberate or inadvertent. He also argues that nudges may be required on ethical grounds to the extent that they promote our core human values. (This might imply that it is sometimes irresponsible to miss an opportunity to provide a helpful nudge.) So the ethical question is not whether to nudge or not, but how to design nudges in such a way as to maximize these core human values, which he identifies as welfare, autonomy and human dignity.

While we can argue with some of the detail of Sunstein's position, I think his two main conclusions make reasonable sense. Firstly, that we are always surrounded by what Sunstein calls Choice Architectures, so we can't get away from the nudge. And secondly, that many nudges are not just preferable to whatever the alternative might be but may also be valuable in their own right.

So what happens when we introduce advanced technology into the mix? For example, what if we have a robot that is programmed to nudge people, perhaps using some kind of artificial intelligence or machine learning to adapt the nudge to each individual in a specific context at a specific point in time?

Within technology ethics, transparency is a major topic. If the robot is programmed to include a predictive model of human psychology that enables it to anticipate the human response in certain situations, this model should be open to scrutiny. Although such models can easily be wrong or misguided, especially if the training data set reflects an existing bias, with reasonable levels of transparency (at least for the appropriate stakeholders) it will usually be easier to detect and correct these errors than to fix human misconceptions and prejudices.

In science fiction, robots have sufficient intelligence and understanding of human psychology to invent appropriate nudges for a given situation. If we start to see more of this in real life, we could start to think of these as unmediated robotic nudges, instead of the robot merely being the delivery mechanism for a mediated nudge. But does this introduce any additional ethical issues, or merely amplify the importance of the ethical issues we are already looking at?

Finally, some people think that the ethical rules should be more stringent for robotic nudges than for other kinds of nudges. For example, I've heard people talking about parental consent before permitting children to be nudged by a robot. But other people might think it was safer for a child to be nudged (for whatever purpose) by a robot than by an adult human. And if you think it is a good thing for a child to work hard at school, eat her broccoli, and be kind to those less fortunate than herself, and if robotic persuasion turns out to be the most effective and child-friendly way of achieving these goals, do we really want heavier regulation on robotic child-minders than human ones?

Richard Bordenave, Comment les paradoxes permettent de réinventer les nudges (Harvard Business Review France, 30 January 2019). Adapted English version: When paradoxes inspire Nudges (6 April 2019)

Jon Elster, Ulysses and the Sirens (1979)

Jochim Hansen, Susanne Winzeler and Sascha Topolinski, When the Death Makes You Smoke: A Terror Management Perspective on the Effectiveness of Cigarette On-Pack Warnings (Journal of Experimental Social Psychology 46(1):226-228, January 2010) HT @ABMarkman

Donella Meadows, Leverage Points: Places to Intervene in a System (Whole Earth Review, Winter 1997)

Robert Steele, Implementing an integrated and transformative agenda at the regional and national levels (AtKisson, 2014)

Cass Sunstein, The Ethics of Nudging (Yale J. on Reg, 32, 2015)

Iain Thomson, Microsoft researchers build 'smart bra' to stop women's stress eating (The Register, 6 Dec 2013)

Zeynep Tufecki, YouTube, the Great Radicalizer (New York Times, 10 March 2018)

Wikipedia: Behavioural Insights Team ("Nudge Unit"), Reverse Psychology,

Stanford Encyclopedia of Philosophy: The Ethics of Manipulation

Related posts: Have you got big data in your underwear? (December 2014), Ethical Communication in a Digital Age (November 2018), The Nudge as a Speech Act (May 2019)

Tuesday, May 14, 2019

Leadership versus Governance

@j2bryson has commented on her blog about the fate of Google's Advanced Technology External Advisory Council (ATEAC), to which she had been appointed.

She argues that the people who were appointed to the ATEAC were selected because they were "prominent" in the field. She notes that "although being prominent doesn't mean you're the best, it probably does mean you're at least pretty good, at least at something".

Ignoring the complexities of university politics, academics generally achieve prominence because they are pretty good at having interesting and original ideas, publishing papers and books, coordinating research, and supervising postgraduate work, as well as representing the field in wider social and intellectual forums (e.g. TED talks). Clearly that can be regarded as an important type of leadership.

Bryson argues that leading is about problem-solving. And clearly there are some aspects of problem-solving in what has brought her to prominence, although that's certainly not the whole story.

But that argument completely misses the point. The purpose of the ATEAC was not problem-solving. Google does not need help with problem-solving, it employs thousands of extremely clever people who spend all day solving problems (although it may sometimes need a bit of help in the diversity stakes).

The stated purpose of the ATEAC was to help Google implement its AI principles. In other words, governance.

When Google published its AI principles last year, the question everyone was asking was about governance:
  • @mer__edith (Twitter 8 June 2018, tweet no longer available) called for "strong governance, independent external oversight and clarity"
  • @katecrawford (Twitter 8 June 2018) asked "How are they implemented? Who decides? There's no mention of process, or people, or how they'll evaluate if a tool is 'beneficial'. Are they... autonomous ethics?" 
  • and @EricNewcomer (Bloomberg 8 June 2018) asked "who decides if Google has fulfilled its commitments".

Google's appointment of an "advisory" council was clearly a half-hearted attempt to answer this question.

Bryson points out that Kay Coles James (the most controversial appointee) had some experience writing technology policy. But what a truly independent governance body needs is experience monitoring and enforcing policy, which is not the same thing at all.

People talk a lot about transparency in relation to technology ethics. Typically this refers to being able to "look inside" an advanced technological product, such as an algorithm or robot. But transparency is also about process and organization - ability to scrutinize the risk assessment and the design and the potential conflicts of interest. There are many people performing this kind of scrutiny on a full-time basis within large organizations or ecosystems, with far more experience of extremely large and complex development programmes than your average professor.

Had Google really wanted a genuinely independent governance body to scrutinize them properly, could they have appointed a different set of experts? Can people appointed and paid by Google ever be regarded as genuinely independent? And doesn't the word "advisory" give the game away? As Brustein and Bergen point out, the actual decisions are made by an internal body, the Advanced Technology Review Council, and external critics doubt that this body will ever seriously challenge Google's commercial or strategic interests.

Veena Dubal suggests that the most effective governance over Google is currently coming from Google's own workforce. It seems that their protests were significant in getting Google to disband the ATEAC, while earlier protests (re Project Maven) had led to the production of the AI principles in the first place. Clearly the kind of courageous leadership demonstrated by people like Meredith Whittaker isn't just about problem-solving.

Joshua Brustein and Mark Bergen, The Google AI Ethics Board With Actual Power Is Still Around (Bloomberg, 6 April 2019)

Joanna Bryson, What we lost when we lost Google ATEAC (7 April 2019), What leaders are actually for (13 May 2019)

Veena Dubal, Who stands between you and AI dystopia? These Google activists (The Guardian, 3 May 2019)

Bobbie Johnson and Gideon Lichfield, Hey Google, sorry you lost your ethics council, so we made one for you (MIT Technology Review 6 April 2019

Abner Li, Google details formal review process for enforcing AI Principles, plans external advisory group (9to5 Google, 18 December 2018

Eric Newcomer, What Google's AI Principles Left Out (Bloomberg 8 June 2018)

Kent Walker, An external advisory council to help advance the responsible development of AI (Google, 26 March 2019, updated 4 April 2019)

Related post: Data and Intelligence Principles From Major Players (June 2018)

Updated 15 May 2019

Sunday, April 28, 2019

Responsible Transparency

It is difficult to see how we can achieve an ethical technology without some kind of transparency, although we are still trying to work out how this could be achieved in an effective yet responsible manner. There are several concerns that are thought to conflict with transparency, including commercial advantage, security, privacy, and the risk of the device being misused or "gamed" by adversaries. There is a good summary of these issues in Mittelstadt et al (2016).

An important area where demands for transparency conflict with demands for confidentiality is with embedded software that serves the interests of the manufacturer rather than the consumer or the public. For example, a few years ago we learned about a "defeat device" that VW had built in order to cheat the emissions regulations; similar devices have been discovered in televisions to falsify energy consumption ratings.

Even when the manufacturers aren't actually breaking the law, they have a strong commercial interest in concealing the purpose and design of these systems, and they use Digital Rights Management (DRM) and the US Digital Millenium Copyright Act (DMCA) to prevent independent scrutiny. In what appears to be an example of regulatory capture, car manufacturers were abetted by the US EPA, which was persuaded to inhibit transparency of engine software, on the grounds that this would enable drivers to cheat the emissions regulations.

Defending the EPA, David Golumbia sees a choice between two trust models, which he calls democratic and cyberlibertarian. For him, the democratic model "puts trust in bodies specifically chartered and licensed to enforce regulations and laws", such as the EPA, whereas in the cyberlibertarian model, it is the users themselves who get the transparency and can scrutinize how something works. In other words, trusting the wisdom of crowds, or what he patronizingly calls "ordinary citizen security researchers".

(In their book on Trust and Mistrust, John Smith and Aidan Ward describe four types of trust. Golumbia's democratic model involves top-down trust, based on the central authority of the regulator, while the cyberlibertarian model involves decentralized network trust.)

Golumbia argues that the cyberlibertarian position is incoherent. 
"It says, on the one hand, we should not trust manufacturers like Volkswagen to follow the law. We shouldn’t trust them because people, when they have self-interest at heart, will pursue that self-interest even when the rules tell them not to. But then it says we should trust an even larger group of people, among whom many are no less self-interested, and who have fewer formal accountability obligations, to follow the law."
One problem with this argument is that it appears to confuse scrutiny with compliance. Cyberlibertarians may be strongly in favour of deregulation, but increasing transparency isn't only advocated by cyberlibertarians and doesn't necessarily imply deregulation. It could be based on a recognition that regulatory scrutiny and citizen scrutiny are complementary, given two important facts. Firstly, however powerful the tools at their disposal the regulators don't always spot everything; and secondly, regulators are sometimes subject to improper influence from the companies they are supposed to be regulating (so-called regulatory capture). Therefore having independent scrutiny as well as central regulation increases the likelihood that hazards will be discovered and dealt with. This could include the detection of algorithmic bias or previously unidentified hazards/vulnerabilities/malpractice.

Another small problem with his argument is that the defeat device had already hoodwinked the EPA and other regulators for many years.

Golumbia claims that "what the cyberlibertarians want, even demand, is for everyone to have the power to read and modify the emissions software in their cars" and complains that "the more we put law into the hands of those not specifically entrusted to follow it, the more unethical behavior we will have". It is certainly true that some of the advocates of open source are also advocating "right to repair" and customization rights. But there were two separate requests for exemptions to DMCA - one for testing and one for modification. And the researchers quoted by Kyle Wiens, who were disadvantaged by the failure of the EPA to mandate a specific exemption to DMCA to allow safety and security tests, were not casual libertarians or "ordinary citizens" but researchers at the International Council of Clean Transportation and West Virginia University.

It ought to be possible for regulators and academic researchers to collaborate productively in scrutinizing an industry, provided that clear rules, protocols and working practices are established for responsible scrutiny. Perhaps researchers might gain some protection from regulatory action or litigation by notifying a regulator in advance, or by prompt notification of any discovered issues. For example, the UK Data Protection Act 2018 (section 172) defines what it calls "effectiveness testing conditions", under which researchers can legitimately attempt to crack the anonymity of deidentified personal data. Among other things, a successful attempt must be notified to the Information Commissioner within 72 hours.

Meanwhile, in the cybersecurity world there are fairly well-established protocols for responsible disclosure of vulnerabilities, and in some cases rewards are paid to the researchers who find them, provided they are disclosed responsibly. Although not all of us have the expertise to understand the technical detail, the existence of this kind of independent scrutiny should make us all feel more confident about the safety, reliability and general trustworthiness of the products in question.

David Golumbia, The Volkswagen Scandal: The DMCA Is Not the Problem and Open Source Is Not the Solution (6 October 2015)

Brent Mittelstadt et al, The ethics of algorithms: Mapping the debate (Big Data and Society July–December 2016)

Jonathan Trull, Responsible Disclosure: Cyber Security Ethics (CSO Cyber Security Pulse, 26 February 2015)

Aidan Ward and John Smith, Trust and Mistrust (Wiley 2003)

Kyle Wiens, Opinion: The EPA shot itself in the foot by opposing rules that could've exposed VW (The Verge, 25 September 2015)

Related posts: Four Types of Trust (July 2004), Defeating the Device Paradigm (October 2015)