10 Usage measurement for equitable rewards
We are close to our intended goal of making a reasonably complete and self-con-sistent proposal. We have one final question to tackle: can we really measure the non-market use of works precisely and reliably enough to set the basis for re-wards? Strongly divergent opinions have been expressed on this topic in the past.
Some managers of collecting societies, who were hostile to a flat-rate-based lega-lization of file sharing, initially claimed that it was impossible to measure usage, and that the system would be prone to an enormous amount of fraud.1Then, as such a system started looking increasingly likely to be implemented, others said that there was no problem at all, provided they were the ones to do it.2Finally, yet others declared that it was intrinsically unfair, since the methods would neces-sarily use statistics, while their present measures for other sources of revenues use detailed counting of every use.3This objection is ironic, since they measure only an extremely limited fraction of the use of works, compared to what we are trying to address.
These disparate arguments further motivate us to evaluate the precision which can be achieved in practice. We start by describing the structure of a possible measurement system, then detail some of its aspects and describe what it can achieve in realistic conditions. Appendix C details the underlying model support-ing our claims.
More modest, because we do not try to measure the value that users have derived from a work, but only whether they shared files and accessed or uploaded on-line contents. More ambitious, because we are addressing a bigger set of works and media than the classical music and motion picture entertainment domain consid-ered by Fisher.
Fig. 10.1. General structure of a possible usage measurement system.
There are two main sources of measurement in the system: a large sample of voluntary broadband Internet users, and access and usage data provided by con-tent sites. The first source is used for measuring the actual sharing of files, main object of this book. The second source is used to measure how users directly read, listen to or view contents on non-market sites, as well as how they upload and reference for recommendation on these sites. If non-market sharing of digital works is legalized and all broadband Internet users make a statutory contribution, Internet users and the sites they put together to make contents accessible are, considered as a whole, the best guarantors of a fair distribution of the rewards.
Of course, some of them will possibly try to abuse the system, and other parties may disguise their efforts to do the same. We thus will have to put in place a number of security and cross-checking mechanisms to prevent, detect, and inves-tigate fraud.
We have yet to specify what constitutes“usage”, which data would be collected from the Internet user sample or from content sites, and how. For our purposes, usage is defined as any form of action involving publicly accessible contents that man-ifests interest for a work. In other terms, for file sharing, the usage we will mea-sure is the entry into the possession by individuals of a full file representing the work, or the upload of the file on a publicly accessible content site (on P2P file sharing networks, access and upload coincide). For contents that can be used directly on-line, in particular blog entries and other contents produced for direct Web use, usage will be constituted by the display of the work in a Web browser
(complete or sufficiently long for a time-based document such as video and re-cordings). Finally, public manifestations of interest, other than uploading a docu-ment for download on a non-market site, such as linking to or referencing will also be usable if this proves not to be too prone to abuse.
The position we defend here is that the data must not consider what individuals do with the work, once obtained, in the private sphere. Any system that would require even a sample of users to provide data about their personal usage of a work once they have acquired the file that represents it would be rejected by users, and would lead to the construction of an absurdly complex and probably inefficient use measurement system.
The fact that we collect possession, access or recommendation information (we call them usage clues) and not hard proofs of user enjoyment will no doubt be deemed unsatisfactory by some. We find this view misplaced. (Fisher 2004, pp.
225-226) remarked that when people download a music file, quite often they never listen to it or listen to it just long enough in order to decide that they are not truly interested, in many cases they listen to it only once, and only in a few cases do they listen to it repeatedly. Well that’s also true of people who buy CDs (with different proportions). Many supposedly hard proofs of user interest or en-joyment in the existing copyright system are no better, or even much more ap-proximate, than our access, upload and recommendation clues. Statutory licen-sing for radio, a scheme that presently accounts for a significant share of royalties for music authors and composers in many countries, is entirely statisti-cal: it counts broadcasts unit by unit, but rates are based on broad classes of pop-ulation able to receive radio, and in rare cases on statistics about the probable audience share at a given time of broadcast.4Fees for public performance of mu-sic in stores or restaurants are collected from owners (who in turn charge custo-mers as part of the general expenses they must recover in their revenues), while the existence of any enjoyment is uncertain. Clues are just an honest word for indications of interest, whose aggregate sum will be a valid basis for a reward system. If desired, one can give weights to various clues, but we suggest postpon-ing this weightpostpon-ing until we have more experience and understand more about user practices on the Internet and about the way the reward system works.
Access and use data from content sites will be particularly useful for media where the number of creators and works is very large, such as blogs or photogra-phy (even when an entire personal blog or photo gallery is treated as a single work). Content site data is not to be trusted any more blindly than user-provided data. However, it is amenable to different forms of control, and importantly, it is exhaustive in its own realm. The independence between the two sources is also precious. For fraud detection purposes, the consolidated data coming from our two sources can be cross-checked against other data from independent sources that are hard to skew simultaneously: observation of Internet traffic and possibly data from audience measurement intermediaries.
usage measurement for equitable rewards 147
Last but not least, any usage measurement system must include surveys of a representative sample of Internet users, conducted periodically (every two years?) to monitor their non-market use practices. Considering the specifics of the do-main under study, where rare practices can be important, the sample will have to be large, in the same range as for time budget surveys. It will provide very valu-able information on Internet use practices which are not connected to commer-cial transactions, and thus poorly understood at present. It will be possible to use the results of these surveys to evaluate the bias introduced by the voluntary per-manent sample, and by other technical aspects of the reward system. This does not mean that this bias must necessarily be corrected: it is not absurd to give a higher weight to the use patterns of the voluntary participants in the sample, provided that participation is open to all. The same is true for data resulting from the use of user-generated content sites. It can be seen as a form of reward to the rewarders, which is in the spirit of a contributive system.
In the following sections, we detail some moderately technical aspects of the usage measurement system and discuss the precision that can be obtained. The system we propose is by no means the only workable one, and it is open to further discussion, but it is important to show that there exists at least one approach that can meet the requirements of precision and resistance to~fraud.
The requirements for a usage measurement system
Since our measurement system produces the data used to compute the level of rewards, it must be precise enough for these rewards to be equitable, in particular for rewards that represent more than a symbolic amount. We address this issue in detail in section 10.4 using a model described in appendix C.
One common argument against the idea of rewards for the use of digital works in file sharing is that it would be prone to fraud. We address this issue also in the sections where we discuss precision, as both aspects are linked. It is useful to describe here the philosophy of our approach to fraud for such a system.
In a recent paper, Andrew Odlyzko, one of the key analysts of Internet issues, has discussed the joys and pains of designing secure systems that use insecure means (Odlyzko 2010). He proposes two approaches, both of which we will use:
slowing things down and cross-checking independent sources. Slowing things down is a pain, when the beauty of digital technology and the Internet is that provision of data can be fast and seamless, but we will not be able to avoid some of it. Just like any website needs precaution against spam robots, we will have to require human confirmation at some key stages. It will be the case for the regis-tration of participants in the voluntary sample, and for the periodic reporting of consolidated use clues by members of the sample. Cross-checking is the key to the prevention, detection and possible investigation of fraud. It creates some complexity in the overall design, but no digital measurement system can avoid it.
Fortunately, some widely used software such as anti-spam plug-ins for the
free-software WordPress platform provide very useful inspiration.5The technology to be put in place will have to be safer, as the benefits of possible fraud are more immediate that those of spam. No system will be safe forever, they will have to be revised in face of new techniques and approaches to fraud. Despite all this, one should not overestimate the level of fraud in social systems in general, and infor-mation technology systems in particular.
Contrary to a common ideological bias, fraud in social systems such as the one we propose does not frequently emanate from ordinary recipients. Most abuse originates with established economic players, and most fraud comes from orga-nized networks. A typical example is the unemployment benefit system put in place, in France, for workers in the live performance and creative activity sector, called régime des intermittents du spectacle.6 When the total cost of the system in-creased, fraud and abuse were invoked among possible causes. Independent stud-ies were conducted, which concluded that the system was indeed being abused, mostly by large media companies, in particular broadcasters, including– ironi-cally– public broadcasters, who disguised permanent employment as intermittent in order to receive subsidies from the system. Direct fraud by recipients was found to be insignificant, even though people received benefits for activities whose inclusion in the system was not originally intended (Corsani-Lazzara-to 2008). There was also criticism against the high-level benefits distributed (Corsani-Lazzara-to some movie actors. Similarly, fraudulent use of social and health insurance bene-fits by ordinary recipients has been shown to have a limited relative financial im-pact (CAF 2010).
In order to be accepted, the usage measurement system must not install con-trols which would be perceived as a presumption of fraud, but it must not be naive either: it must be capable of detecting and – if possible – of preventing fraud if and when it occurs. No system is perfectly resistant to fraud. Existing copyright rewards are no more exempt from fraud than VAT, for instance. The reward system we propose won’t be either, but one can make fraud easier to detect, limit economic incentives for it, take various measures to eliminate frau-dulent data from measurements, and prosecute fraufrau-dulent behavior. As rewards are based on comparative levels of use, the best protection against fraud will come from the sheer mass of non-fraudulent data, but as fake data can be pro-duced at will, we must make sure that it will be hard to inject it in the system.