Want to develop ethical AI? Then we need more African voices
AI needs to be more inclusive
Context
Research and development of AI and machine learning technologies are growing in African countries. Programs such asData Science Africa,Data Science Nigeria, and theDeep Learning Indabawith itssatellite IndabaX events, which have so far been held in 27 different African countries, illustrate the interest and human investment in the fields.
The potential of AI and related technologies to promote opportunities forgrowth, development, and democratization in Africais a key driver of this research.
Yet very few African voices have so far been involved in the international ethical frameworks that aim to guide the research. This might not be a problem if the principles and values in those frameworks have universal application. But it’s not clear that they do.
For instance, theEuropean AI4People frameworkoffers a synthesis of six other ethical frameworks. It identifies respect for autonomy as one of its key principles. This principle has beencriticizedwithin the applied ethical field of bioethics. It is seen asfailing to do justice to the communitarian valuescommon across Africa. These focus less on the individual and more on community, evenrequiring that exceptionsare made to uphold such a principle to allow for effective interventions.
Challenges like these – or even acknowledgment that there could be such challenges – are largely absent from the discussions and frameworks for ethical AI.
Just like training data can entrench existing inequalities and injustices, so can failing to recognize the possibility of diverse sets of values that can vary across social, cultural, and political contexts.
Unusable results
In addition, failing to take into account social, cultural, and political contexts can mean that even a seemingly perfectethical technical solution can be ineffective or misguided once implemented.
For machine learning to be effective at making useful predictions, any learning system needs access to training data. This involves samples of the data of interest: inputs in the form of multiple features or measurements, and outputs which are the labels scientists want to predict. In most cases, both these features and labels require human knowledge of the problem. But a failure to correctly account for the local context could result in underperforming systems.
For example, mobile phone call records havebeen usedto estimate population sizes before and after disasters. However, vulnerable populations are less likely to have access to mobile devices. So, this kind of approachcould yield results that aren’t useful.
Similarly, computer vision technologies for identifying different kinds of structures in an area will likely underperform where different construction materials are used. In both of these cases, as we and other colleagues discuss inanother recent paper, not accounting for regional differences may have profound effects on anything from the delivery of disaster aid, to the performance of autonomous systems.
Going forward
AI technologies must not simply worsen or incorporate the problematic aspects of current human societies.
Being sensitive to and inclusive of different contexts is vital for designing effective technical solutions. It is equally important not to assume that values are universal. Those developing AI need to start including people of different backgrounds: not just in the technical aspects of designing data sets and the like but also in defining the values that can be called upon to frame and set objectives and priorities.
This article byMary Carman, Lecturer in Philosophy,University of the WitwatersrandandBenjamin Rosman, Associate Professor in the School of Computer Science and Applied Mathematics,University of the Witwatersrand,is republished fromThe Conversationunder a Creative Commons license. Read theoriginal article.
Story byThe Conversation
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