CHI 2018 Student Research Competition
CHI 2018, April 21–26, 2018, Montréal, QC, Canada
Understanding What Africans Say Lameck Mbangula Amugongo Namibia University of Science & Technology Windhoek, Namibia
improve these voice assistants to better understand what Africans say. Therefore, improve the user experience of African users.
[email protected]
Abstract Mobile devices such as smartphones, tablets and IoT have undoubtedly been the largest shapers of the 21st century, changing how people live, think and work. Though, many in the developed world and a few privileged in the developing world can attest to this. Due to illiteracy, socio-economic constraints and unavailability of many non-western languages especially African languages on these mobile devices; majority of the people in developing countries are yet to benefit from these mobile devices. Yet, the rise of powerful artificial intelligence and natural language processing present new opportunities for many users of mobile devices to easily interact with their devices in their own vernacular language. Thus, get the best out of their devices. This paper, presents results collected through an online survey across 6 African countries to establish what African users think of existing voice assistants, such as Siri, Google assistant and alike. Additionally, provides recommendations based on our findings that will Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author. CHI'18 Extended Abstracts, April 21–26, 2018, Montreal, QC, Canada © 2018 Copyright is held by the owner/author(s). ACM ISBN 978-1-4503-5621-3/18/04. https://doi.org/10.1145/3170427.3180301
Author Keywords HCI; Artificial Intelligence; Natural Language Processing; African Users.
ACM Classification Keywords HCI, NLP.
Introduction In the last years, we have witnessed the emergence of high end mobile devices such as smartphones and tablets, more powerful than the computer, which was used to send Apollo II to the moon [1]. Additionally, these devices are more than just phones, equipped with diverse sensors they have become useful aids and companions. However, due to constraints such as illiteracy, poor connectivity and inability to afford these devices, potential users in the third world didn’t have access to smartphones and tablets for a long time. However, thanks to technological advancement, the price of smartphones has drastically reduced, as smartphones become affordable, more new users in the developing world have now access to them. Moreover, investments in telecommunication infrastructures through WACS (West Africa Cable System) and EASSy (Eastern Africa Submarine Cable System) has increased network penetration and the speed of broadband services in several African countries, providing faster access to international PoP (Point of Presence) in
SRC02, Page 1
CHI 2018 Student Research Competition
Europe and America [2]. These present unique opportunities for users in the developing world to leverage on ubiquitous technologies to access services and share content. Over the years we have witnessed a number of advances in the field of machine learning (ML) and artificial intelligence (AI) [3]. Powered by machine learning; natural language processing with its ability to understand the meaning of conversational input and reacts accordingly; creating value and improving the user experience, is becoming increasingly used to process sound signals that enable computers to interact with humans using natural everyday language. This is evident in the number of conferences for ML, speech recognition and natural language processing. In this paper, we present the results of our survey aimed at uncovering what African users think of existing voice assistants such as Siri, Google assistant and so forth. Moreover, outline as recommendation what respondents wish they had on their smart devices.
CHI 2018, April 21–26, 2018, Montréal, QC, Canada
of stereotypes that Africans have no access to the Internet and they are poor. Thus, cannot afford smartphones [4]. However, the availability of affordable smartphones, and subsequently the expansion of network infrastructure in remote areas implies that smart environments are within the reach of rural users. Therefore, limiting solutions for rural users to low-end devices is short-sighted.
State of the art
In their work, [5] attest to the biasness in HCI that is deeply rooted in western epistemology, which has resulted in tensions between HCI principles and local cultures. This evident disconnection between HCI and cultures of users in developing countries has further resulted in the rejection of such designs. Moreover, [5] went further to propose an Afro-centric paradigm as an alternative to guide interaction and reframing HCI to fit communities outside western communities with the focus on community design in southern Africa. [5] present a good starting point to decolonize interaction or reframing HCI. Extending on their work, this study posits that presenting information on the mobile device in the language of the user is one step closer to decolonizing interaction. The subsequent subsection explores the history of artificial intelligence and how it evolved from fiction to reality. The subsection also deliberates on NLP and its application to make computers interact with people using human-like methods.
Over the years, there has been a lot effort from researchers in human computer interaction and user centered design to develop better tools to bring users in developing countries in the digital world. However, most of the existing body of knowledge only explore how low-end devices (2nd generation devices) can be used by users in developing countries, partly because
Artificial intelligence (AI) and Natural language processing (NLP) Artificial Intelligence (AI) is not a new phenomenon, its roots can be traced as far as ancient Greece as captured in the writings of Homer about some fictional mechanical “tripod” waiting on the gods at dinner.
This paper is structured as follows; the next section reviews existing literature. The section thereafter, describe the methods and techniques used to guide the study. Section 4 discusses the results. The last section draws conclusions and outlines recommendations.
SRC02, Page 2
CHI 2018 Student Research Competition
However, until half a century ago, AI has been part of human culture through imaginations, promises and storytelling. Nevertheless, the emergence of powerful computers in the 1950s has enabled AI researchers and early adopters to explore embedding intelligence in machines, thus enabling them to practically evaluate possibilities that only existed in theories and fantasies [6]. In the early day of AI, Chess and gaming was used to represent algorithms and machines that can learn, thus solve complex tasks that requires intelligence. Alan Turing the father of modern Computer Science reinvented the Minimax algorithm and applied it to play Chess. Moreover, Turing also proposed that computers can think, which challenged Lady Lovelace’s objection, which stated that the machine can only do what we tell it to do [7]. Despite the breakthrough of AI in gaming in 70s and 80s, AI was not widely adopted and used. However, the emergence of deep learning with scaled up hidden layers that are capable of emulating the human brain has proliferated artificial intelligence over the last few years [8]. Additionally, the rise of AI based applications such as Siri, Amazon Alexa and Google home are just some of the evidence of how good AI has been come. Yet, Siri and the likes are not without problems, so is AI. Unlike humans who rely mostly on natural interaction, computers are disappointing when it comes to understanding natural language and showing human emotional intelligence. Moreover, applications such as Siri, Google home and Alexa are not developed with users from developing/emerging markets in mind, and they do not cater for non-western accents. With the intentions of perfecting AI, NPL is increasingly being used to facilitate natural interaction between
CHI 2018, April 21–26, 2018, Montréal, QC, Canada
machines and computers. Naturally, people communicate in different ways, by speaking, making gestures, signing and through written text. Natural language processing, a branch of computer Science focusing on developing systems that enable computers to communicate with people using everyday language is increasingly being adopted as an approach to make computers understand humans better. Up to date, only few works exists that investigated processing of African languages. However, none of these existing works look into how African think about voice assistant tools such as Siri, Cortana, Google assistant and alike. In their work [10] emphasized on the need to develop a wide range of applications in vernacular languages, such as translation systems, spelling and grammar checkers, speech synthesis and recognition, information retrieval and filtering, and so forth. However, they highlighted the lack of qualified personnel with expertise in African language development as an obstacle. No doubt, African researchers, linguists and developers needs to get more involved in the development of new tools that will support the processing of African languages to ensure that they accurate, unbiased and useful for Africans. The next section, describe methods and techniques used to undertake this study.
Method Research has become a primary activity used by scientists, researchers and even unknowingly by citizens to uncover the unknowns and solve their daily problems [11]. This study intent on addressing the question, how can we enable computers and mobile devices better understand what Africans say? Therefore, enable Africans to better interact with their
SRC02, Page 3
CHI 2018 Student Research Competition
devices, more important make life easier and help solve some of their daily challenges. In this section, methods and procedures used in undertaking this study are clearly described. In this study, a combination of quantitative and ethnography methods was used to collect data from users. Quantitative method was used preferred because it enabled the researchers to use measurable data to uncover patterns and formulate facts. Additionally, ethnography was used to observe how participants practically use voice-based assistants. An online survey, created using Google form was used as a tool to collect data from participants. There was no population sampling done, we wanted the study to be open to everyone who was interested in participating. Each participant had to answer seven compulsory questions and depending on whether they own a smartphone or used a voice assistant, they respondent to additional questions. For data analysis, Pandas, a python library was used. Pandas was chosen as the preferred tool because of its rich functionalities and it’s very easy to use. The results are explained in the next section.
Results and discussions The obtained results from the survey were very insightful, relevant and can be useful in the creation of inclusive applications and services for everyone. 65.5% of study participants have postgraduate degree, this could be because people with postgraduate qualifications are generally interested in participating research studies. Though, the study was available for everyone, only one participant was below the age of 20. All participants own a mobile device, either a tablet
CHI 2018, April 21–26, 2018, Montréal, QC, Canada
or cellphone. 96.8% of participants own smartphones and only one participant owns a feature phone. However, majority of the study participants previously owned a smart device before. It is interesting to note that though 52.4% of study participants find voice assistant useful for interaction, 63.5% stated that their voice assistant can only understand them to a certain extent and 2.3% stated that their voice assistant cannot understand them as shown in figure 1.
Figure 1. Participants who think their voice assistant understands them.
52.4% of the study participants believe that their mobile device(s) and or computer do not understand them. Though, the word ‘understand’ may be vague; in the context of this study refers to when they speak. Google assistant is the most commonly used voice assistant, followed by Siri as illustrated in table 1. Additionally, a significant number of participants have used more than one voice assistant. Participants in this study were diverse as indicated in figure 2. Based on the languages they speak, one can conclude that study participants come from 6 different countries in Africa, namely; Namibia, Zimbabwe, South Africa, Nigeria,
SRC02, Page 4
CHI 2018 Student Research Competition
CHI 2018, April 21–26, 2018, Montréal, QC, Canada
Zambia and Niger. Majority of study participant have Oshiwambo as their native language, this is because the study was carried out from Namibia. Oshiwambo is a Bantu language spoken in southern Angola and northern Namibia by about 1.5 million people in 2006.
However, the language/accent challenges will only be solved if the native speakers of the language are involved in the development of such NLP and speech recognition tools. In her work [17] talks about the bias of English-speaking Americans to Asian accented English, which results in language-based discrimination. Because many of the existing voice assistants are often developed by white male, they often discriminatory. Therefore, using local datasets for training and including the formerly excluded non-Westerners is critical to avoiding the biases. The subsequent section draws conclusions and outline intended future works.
Conclusion
Table 1. Voice assistants used by study participants.
When asked, any idea of how we can make your computer/mobile device understand you better? Majority of the participants, pointed out on the need for more Africans accents and languages to be incorporated in voice assistants such Siri, Google and alike. This evidence that if the language problem is solved, many of the interaction problems, especially voice-based interaction will be almost solved.
Figure 2. Mother tongue language distribution of participant.
This study presents results of a survey aimed at establishing what African users think of existing voice assistants. Moreover, provide recommendations to improve voice-assistant. No doubt as smartphones and smart devices become cheaper, smart technologies will be everywhere. Everyone will have access to these smart devices, even those who couldn’t previously afford them. These present new opportunities to leverage on the power of these tools to make services accessible from anywhere. However, in order for these services to be useful to many non-English speakers, it’s important that new interaction techniques need to be explored. Additionally, it’s also important that local languages are catered for. The results of this study affirm that the inclusion of native languages is critical for the success voice-based assistant in Africa. The inclusion of African languages in voice assistants will also improve user experiences, as it will enable device users to gain more value from their devices and use their devices to full potential. As part of the future works, we’ll leverage on existing resources
SRC02, Page 5
CHI 2018 Student Research Competition
CHI 2018, April 21–26, 2018, Montréal, QC, Canada
of Bantu languages such as Swahili to build NLP library for Oshiwambo.
Acknowledgement We sincerely thank all those who participated in this study by responding to our online survey. We would also like to thank all reviewers for their useful feedback, which has positively improved this manuscript.
References
Paradigm. Interntional Journal of Human Computer Interaction 29, 4: 243–255. 8. 9.
Nicola J. Bidwell. 2014. Moving the Center to Design Social Media in Rural Africa. AI & Society. Matt Jones, Simon Robinson, Jenifer Pearson, Manjiri Joshi, Dani Raju, Charity C. Mbogo, Sharon Wangari, Anieudha Joshi, Edward Cutrell, and Richard Harper. 2017. Beyond yesterday’s tomorrow: future-focused mobile interaction design by and for emergent users. Pers. Ubiquitous Comput., 21, 1:157–171.
1. Dirk Helbing. 2005. The Automation of Society is Next: How to Survive the Digital Revolution.
10. B. G. Buchanan. 2005. A Very Brief History of Artificial Intelligence. AI Magazine, 26, 4:53–60.
2.
11. Alan M. Turing. 1950. Computing Machinery and Intelligence. Mind, 49: 433-460.
3.
ITU. The world in 2014 - ICT facts and figures, 2014. Bjorn Gamback, Gunnar Eriksson, and Athanassia Fourla. 2005. Natural Language Processing at the School of Information Studies for Africa. In proceeding of the Second ACL Workshop on Effective Tools and Methodologies for Teaching NLP and CL, Ann Arbor, Association for Computational Linguistics, 49–56.
4.
Dunjko Vedran and Hans J. Briegel. 2017. Machine learning & artificial intelligence in the quantum domain. arXiv:1709.02779.
5.
Susan P. Wyche and Laura L. Murphy. 2012. Dead China - Make Phones Off the Grid : Investigating and Designing for Mobile Phone Use in Rural Africa. In Proceeding of the designing interactive systems conference (DIS '12), 186–195.
6.
Hamed Ketabdar. 2010. New multimodal interaction techniques for mobile devices. Ph.D Dissertation. Deutsche Telekom Laboratories/Technische Universität Berlin, Berlin.
7.
12. A. L. Samuel. 1959. Some Studies in Machine Learning Using the Game of Checkers. IBM J. Res. Dev., 3, 3:210–229. 13. Babak Hodjat. 2015. The AI Resurgence: Why Now? Wired magazine, March. 14. Ronan Collobert, Jason Weston, Leon Bottou, Michael Karlen, Koray Kavukcuogluu, and Pavel Kuksa. 2011. Natural Language Processing (Almost) from Scratch. Journal of Machine Learning Research. 12, 2493–2537. 15. Roald Eiselen and Martin J. Puttkammer. 2013. Developing Text Resources for Ten South African Languages. In proceding LREC conference, 3698– 3703, Iceland. 16. Uma Sekaran. 2006. Research methods for business: A skill-building approach. 5th ed. New York: John Wiley & Sons, Inc.
Heike Winschiers-Theophilus and Nicola J. Bidwell. 2013. Toward an Afro-Centric Indigenous HCI
SRC02, Page 6
