Anyone who is involved in app creation – from defining the concept, to requirements gathering, to final implementation – takes into consideration how the app will work successfully and will give advice to on how it can be used most effectively.

However, most recommendations are focused on security and privacy aspects, omitting the critical ethical implications of building and providing an app used for decision-making.

Inevitably, the builders of the app are implicitly assuming some of the responsibility for other people’s decision making. Therefore, to evaluate the ethical responsibilities of the app creators we should look at what potential decisions could go wrong.

The ethical approach begins with a check list to minimize all objective factors that can contribute to a wrong decision. The creators of the app should ensure that people can reach the right conclusions from the facts and the way they are presented in the app.

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So let us look at the issues.

The primary issue is to do with the data itself. Every developer needs to ask two questions: 'is the data accurate?' and 'is the data complete?' Hopefully, the answer to both of these will be affirmative, but the ethical developer needs to make sure all potential data issues have been pre-empted.

It’s something that is oft-discussed in the business intelligence community, but the end user is unlikely to take it into consideration – therefore, it shouldn’t be left to them to solve.

To use the example of retail, a retailer may, on occasion, put the wrong price tag on an item (perhaps due to an earlier data mistake). In this case, you’d expect to be charged the price on the tag, even if it is clearly incorrect.

The retailer loses out, and all because of a data mistake. Tracking the cost of wrong decisions is difficult, so the answer is to impose stricter standards on data assurance and leave the responsibility for this with the developers and data creators who have a sounder understanding of what is involved.

Secondly, the analytics themselves need to be considered. Are the methods used to analyse both effective and valid? Do they have a real impact on the desired outcomes? Selecting the right method is crucial, especially when it comes to predictive and prescriptive analytics.

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Think of this way: you would expect clinical trial research to be done with many checks and balances, so that the doctor doesn’t prescribe the wrong test and base the diagnosis and treatment on faulty test results. Analytics methodology must be checked in a similar way, with a formal review and sign off process for the methods deployed in apps.

Finally, the way in which information is presented is important too. It directly impacts the perception of the data from the outside, as often the quickest of conclusions and decisions are made from the visuals. Any embellishment or unnecessary design decoration that can impede or exaggerate perceptions can be unethical, as they divert attention away from the primary purpose and function of the application.

Visuals can sometimes obscure the facts and misrepresent proportions and ratios, leading to incorrect conclusions. The developer should always have in mind the true aim of the app and convey this with clarity and without ambiguity.

In conclusion, we need to create awareness among the creators of apps about the ethical aspects of its usage and their implicit responsibilities. While the answers to many of the issues raised above are soft and many issues may fall in grey areas, overall awareness would help the creators make better choices about data, analytics and presentation of information.