Crowd-based reporting tools have garnered tremendous attention for their role in anticorruption efforts all around the world. Deservedly so: these platforms harness rapidly increasing internet and mobile access in the developed world to tackle the age-old problem of corruption. Perhaps the best known of this new wave of crowdsourced reporting tools, iPaidABribe (which started in India and has been successfully recreated in other parts of the world), allows any citizen with a smartphone or other access to the internet to report bribery incidents nearly-instantaneously. Citizens can report the amount of a bribe, the recipient of the bribe, the institution that took or demanded the bribe, and so on — all anonymously. Visitors can read the reports as well as view a sort of “heat map” that aggregates the reports to demonstrate where bribery is most prevalent. The very act of broadcasting one’s own experiences with corrupt officials, and the commensurate naming-and-shaming effect this has when many such reports are aggregated, is proving to be extremely powerful.
To be sure, not all attempts to use modern internet and mobile technology to crowdsource anticorruption reporting have been as successful. Some (perhaps most) platforms never really get off the ground. Observers on this blog and elsewhere have pointed out that this may be due to a mismatch between local social conditions and the platform itself. These challenges are real, but I want to focus for now on platforms that have managed to gather and report significant data on corruption. Even in these cases, some commentators have pointed out, the full potential of crowd-based corruption reporting platforms has yet to be realized. The data they are gathering is still relatively “raw” and unprocessed by entities that could really use it — such as government anticorruption agencies. Thus it is important to highlight how these platforms can improve, and how they can avoid having their efforts thwarted by unwanted side-effects. As platform developers move past their early obstacles and start achieving real success in their primary goal — getting people to use their reporting system — the need now is to direct the platforms and their potential partners in such a way as to enhance their effectiveness and to avoid the possibility that their data will be misused.
First, how to increase their effectiveness? Dieter Zinnbauer of Transparency International has a new paper on crowd-based reporting mechanisms that contains several interesting suggestions, a few of which I have selected here because they all seem to indicate that the data these platforms are already gathering could be better used right now by existing anticorruption actors:
- Drawing from the experience of other crowdsourced governance models, platforms should ideally allow for a two-way communication between citizens and officials. For instance, the popular apps FixMyStreet in the UK and SeeClickFix in the US allow users to report needed roadworks — and for the relevant city officials to report when and how they have addressed the issue. IPaidABribe is close to this with its promise to follow up on initial reports with government agencies.
- Information gathered on these platforms could be useable in future legal actions. Mr. Zinnbauer uses an analogy from New York before the internet existed: enterprising lawyers used to pay people to map and report potholes because one needed to prove that the city knew about a problem before the city could be sued over it. Although crowd platforms don’t need to pay their contributors, their reports could be used to prove in a lawsuit that officials were aware of a problem, where necessary. As an example, the platform Not In My Country, which focuses on corruption in Kenyan and Ugandan universities, is forming partnerships with private lawyers to bring lawsuits with particularly information-rich reports.
- Local antocorruption NGOs or legal aid services as well as investigative journalists could also do more to use this data. Connecting the crowd-gathered corruption data to non-government outlets that have a proven capacity to file complaints, launch investigations, and publicize scandals could at least amplify the naming and shaming effect. TI Macedonia’s partnership with Ushahadi is a good example of this partnership potential.
These are obviously just a few of the connections that are possible between crowd-based platforms and existing government, private, and NGO or media-led anticorruption efforts. Implementing such connections is crucial, not only to realize the full potential of crowdsourced anticorruption reporting technologies, but also because publishing the corruption data gathered by these platforms can actually have a harmful impact on the anticorruption effort if the data are not used properly. Mr. Zinnbauer’s paper is again helpful in highlighting some of the most serious potential side effects:
- Broadcasting the prevalence of bribery may allow participants to determine the market price for their bribes. As economists know, price transparency contributes to lower transaction costs and thus more deals being made successfully.
- Seeing how prevalent bribery is may create a “keeping up with the Joneses” atmosphere — individuals may feel that, if everyone else is paying a bribe, then in order to remain competitive he or she must pay bribes as well. The pressures on a parent to bribe school officials to favorably grade their children, for instance, may actually increase with the knowledge that such activity is pervasive.
- Relatedly, the knowledge that bribery is pervasive can have the affect of normalizing it; much like with the Broken Windows Theory of crime prevention, if the public becomes (even more) aware of bribery then future transgressions become more acceptable.
These negative side effects are admittedly speculative, but they are plausible. More important, they are avoidable. The corruption data gathered and published by crowd-based reporting platforms is extremely powerful on its own merits, and given the tireless efforts of these platforms’ developers I have no doubt the data will continue to amass. What I think Mr. Zinnbauer is too polite to say is that the onus is now squarely on institutional anticorruption groups — governments, NGOs, private lawyers, investigative journalists, and the like — to use these new tools. But that’s for future posts.
GAB | The Global Anticorruption Blog 
Chris, I generally agree with you that there is only so much that can be done with a social media platform that just aggregates and publicizes corruption reports. The next step is obviously to put that information in the hands of the groups who can use it best to directly or indirectly target corrupt actors. At the same time, though, we should remember that consumers themselves are one such group. While it is certainly possible that a pure reporting platform not tied to a prosecutorial or advocacy organization could have the ill effects you suggest, such a result is not clearly inevitable. Consumers, when provided with sufficient aggregate data regarding hot spots of corruption or patterns of corruption activity, may choose instead to circumvent offices, institutions, or officials that the sites/apps suggest are corrupt. This type of behavior might, in turn, indirectly suppress corruption over the long term. Perhaps there is another problem with these apps, then, in that they aggregate instances of corruption but not corresponding data about where people can obtain services without being forced to pay a bribe.
I admit that there is value to simply publishing instances of corruption — as you say, just knowing where corruption lies gives one an opportunity to avoid it. The naming-and-shaming effect can be powerful too. To add color to my concerns, though, consider that these effects may only adhere in a society that already has a fairly healthy civil society. You can only avoid corruption if there is an uncorrupt office that is an adequate alternative for your needs, and officials are only shamed if corruption is the exception rather than the norm among their fellow civil servants. Moreover, I fear that publishing this information can backfire even in this fairly healthy society — citizens may become inured to a certain level of corruption that they previously were unaware of, and civil servants may take the knowledge that their colleagues are taking bribes as a license to do so for themselves. If, however, the publication of this information was accompanied by some real consequences, as you suggest in your comment and as I know is the intention of the publishers, then the negative side-effects will be mitigated and the publication platform will indeed enhance and amplify the society’s desire to eradicate corruption.
The crowdsourcing anti-corruption phenomenon is something to be applauded (and is also just really interesting), but I think it’s worth noting one more potential negative side-effect of strengthening crowd-based approaches. If actionable items (investigations, prosecutions, etc.) are tied to crowdsourced information, the incentives for reporting change, as does (possibility) the faith we should put in that information. Anonymous platforms which enjoy strong credibility precisely because the reporter had nothing (or very little) to gain may lose that credibility if that information is explicitly relied upon by donors, NGOs, prosecutors and other officials. Political rivals, competitors for government contracts, administrative bodies vying for funding or territory, would then have strong incentives use these platforms to discredit any government official standing in their way. I’m sure there is a way around this, but it’s important to keep in mind that while the crowd may be powerful, it is also unaccountable.
Related to the point about accountability, reliability, and credibility, I am wondering about the ways in which crowd-sourced information could become legally actionable. If, as Melanie cautions, some reports are unfounded or politically motivated but the reporters are anonymous, does the platform become liable for libel? My hunch is that, at least under U.S. law, it would be very difficult to prevail on such a claim.
To your point about requirements for using the information as evidence – would that also require a known (even if redacted) source?
The promise of user anonymity seems essential to the success of companies like IPaidABribe. But, like Melanie, I’m a little of leery of the implications when the information is used to take action against specific individuals (or, on the flip side, when such action invokes identifying details about the reporter). At the same time, I recognize that targeted investigations/prosecutions would be effective.
This is a really good point. Why should we trust Yelp, for instance, if we have reason to believe that its review statistics are being gamed? I guess, to Melanie’s suggestion, we trust that the majority of posters are just disinterested third parties and they couldn’t possibly ALL have a self-interested motive to publish. This assumption may be faulty in the context of a restaurant (or a civil servant) whose backers have a lot of money on the line and need positive reviews, or who want to discredit their rivals. I’m pretty sure negative or positive reviews can be bought.
All of that being said, I still trust Yelp. I’ve heard of Yelp outing restaurants that pay for reviews and using algorithms to determine who is gaming their system — sort of like how Google filters their search results. I think there is also an effort now to algorithmically identify legitimate frequent posters and to prioritize their opinions. In the end, the best crowd platforms make it too costly or difficult to fake a body of crowd-sourced information.