Data-driven approaches to content selection for data-to-text generation

Data-to-text systems are powerful in generating reports from data automatically and thus they simplify the presentation of complex data. Rather than presenting data using visualisation techniques, data-to-text systems use human language, which is the most common way for human-human communication. In addition, data-to-text systems can adapt their output content to users’ preferences, background or interests and therefore they can be pleasant for users to interact with. Content selection is an important part of every data-to-text system, because it is the module that decides which from the available information should be conveyed to the user. This thesis makes three important contributions. Firstly, it investigates data-driven approaches to content selection with respect to users’ preferences. It develops, compares and evaluates two novel content selection methods. The first method treats content selection as a Markov Decision Process (MDP), where the content selection decisions are made sequentially, i.e. given the already chosen content, decide what to talk about next. The MDP is solved using Reinforcement Learning (RL) and is optimised with respect to a cumulative reward function. The second approach considers all content selection decisions simultaneously by taking into account data relationships and treats content selection as a multi-label classification task. The evaluation shows that the users significantly prefer the output produced by the RL framework, whereas the multi-label classification approach scores significantly higher than the RL method in automatic metrics. The results also show that the end users’ preferences should be taken into account when developing Natural Language Generation (NLG) systems. NLG systems are developed with the assistance of domain experts, however the end users are normally non-experts. Consider for instance a student feedback generation system, where the system imitates the teachers. The system will produce feedback based on the lecturers’ rather than the students’ preferences although students are the end users. Therefore, the second contribution of this thesis is an approach that adapts the content to “speakers” and “hearers” simultaneously. It considers initially two types of known stakeholders; lecturers and students. It develops a novel approach that analyses the preferences of the two groups using Principal Component Regression and uses the derived knowledge to hand-craft a reward function that is then optimised using RL. The results show that the end users prefer the output generated by this system, rather than the output that is generated by a system that mimics the experts. Therefore, it is possible to model the middle ground of the preferences of different known stakeholders. In most real world applications however, first-time users are generally unknown, which is a common problem for NLG and interactive systems: the system cannot adapt to user preferences without prior knowledge. This thesis contributes a novel framework for addressing unknown stakeholders such as first time users, using Multi-objective Optimisation to minimise regret for multiple possible user types. In this framework, the content preferences of potential users are modelled as objective functions, which are simultaneously optimised using Multi-objective Optimisation. This approach outperforms two meaningful baselines and minimises regret for unknown users

Harmonizing the agricultural biotechnology debate for the benefit of African farmers

The intense debate over agricultural biotechnology is at once fascinating, confusing and disappointing. It is complicated by issues of ethical, moral, socio-economic, political, philosophical and scientific import. Its vocal champions exaggerate their claims of biotechnology as saviour of the poor and hungry, while, equally loudly, its opponents declare it as the doomsday devil of agriculture. Sandwiched between these two camps is the rest of the public, either absorbed or indifferent. Biotechnology issues specific to the African public must include crop and animal productivity, food security, alleviation of poverty and gender equity, and must exclude political considerations. Food and its availability are basic human rights issues—for people without food, everything else is insignificant. Although we should discuss and challenge new technologies and their products, bringing the agricultural biotechnology debate into food aid for Africa where millions are faced with life-or-death situations is irresponsible. Agricultural biotechnology promises the impoverished African a means to improve food security and reduce pressures on the environment, provided the perceived risks associated with the technology are addressed. This paper attempts to harmonize the debate, and to examine the potential benefits and risks that agricultural biotechnology brings to African farmers. Key words: Agriculture, biotechnology, biotechnology debate, biotechnology and Africa, biotechnology issues, food security, poverty alleviation. African Journal of Biotechnology Vol.2(11) 2003: 394-41

The enchanted house:An analysis of the interaction of intelligent personal home assistants (IPHAs) with the private sphere and its legal protection

Abstract In less than five years, Alexa has become a familiar presence in many households, and even those who do not own one have stumbled into it, be it at a friend’s house or in the news. Amazon Alexa and its friend Google Assistant represent an evolution of IoT: they have an advanced ‘intelligence’ based on Cloud computing and Machine Learning; they collect data and process them to profile and understand users, and they are placed inside our home. I refer to them as intelligent personal and home assistants, or IPHAs.  This research applies multidisciplinary resources to explore the phenomenon of IPHAs from two perspectives. From a more socio-technical angle, the research reflects upon what happens to the private sphere and the home once IPHAs enter it. To do so, it looks at theories and concepts borrowed from history, behavioural science, STSs, philosophy, and behavioural design. All these disciplines contribute to highlight different attributes that individuals and society associate with the private sphere and the home. When the functioning of IPHAs is mapped against these attributes it is possible to identify where Alexa and Assistant might have an impact: there is a potential conflict between the privacy expectations and norms existing in the home (as sanctuary of the private sphere) and the marketing interests introduced in the home by IPHAs’ profiling. Because of the voice-interaction, IPHAs are also potentially highly persuasive, can influence and manipulate users and affect their autonomy and control in their daily lives. From the legal perspective, the research explores the application of the GDPR and proposal for e-Privacy Regulation to IPHAs, as legislative tools for the protection of the private sphere in horizontal relationships. The analysis focuses in particular on those provisions whose application to IPHAs is more challenging, based on the technology but also on the sociotechnical analysis above. Special attention is dedicated to the consent of users to the processing, the general principles of the GDPR, attributing the role of controllers or processors to the stakeholders involved, profiling and automated decisions, data protection by design and default, as well as spam and robocalls. For some of the issues, suggestions are offered on how to interpret and apply the legal framework, in order to mitigate undesired effects. This is the case, for instance, of determining whether the owners of IPHAs should be considered controllers vis-à-vis the data of their guests, or of the implications of data protection by design and default on the design of IPHAs. Some questions, however, require a wider debate at societal and political level. This is the case of the behavioural design techniques used to entice users and stimulate them to use the vocal assistants, which present high levels of persuasion and can affect the agency and autonomy of individuals. The research brings forward the necessity to determine where the line should be drawn between acceptable practices and unacceptable ones

Responsible AI and Analytics for an Ethical and Inclusive Digitized Society

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Algorithmic business and EU law on fair trading

This thesis studies how commercial practice is developing with artificial intelligence (AI) technologies and discusses some normative concepts in EU consumer law. The author analyses the phenomenon of 'algorithmic business', which defines the increasing use of data-driven AI in marketing organisations for the optimisation of a range of consumer-related tasks. The phenomenon is orienting business-consumer relations towards some general trends that influence power and behaviors of consumers. These developments are not taking place in a legal vacuum, but against the background of a normative system aimed at maintaining fairness and balance in market transactions. The author assesses current developments in commercial practices in the context of EU consumer law, which is specifically aimed at regulating commercial practices. The analysis is critical by design and without neglecting concrete practices tries to look at the big picture. The thesis consists of nine chapters divided in three thematic parts. The first part discusses the deployment of AI in marketing organisations, a brief history, the technical foundations, and their modes of integration in business organisations. In the second part, a selected number of socio-technical developments in commercial practice are analysed. The following are addressed: the monitoring and analysis of consumers’ behaviour based on data; the personalisation of commercial offers and customer experience; the use of information on consumers’ psychology and emotions, the mediation through marketing conversational applications. The third part assesses these developments in the context of EU consumer law and of the broader policy debate concerning consumer protection in the algorithmic society. In particular, two normative concepts underlying the EU fairness standard are analysed: manipulation, as a substantive regulatory standard that limits commercial behaviours in order to protect consumers’ informed and free choices and vulnerability, as a concept of social policy that portrays people who are more exposed to marketing practices

The Dynamics of Influencer Marketing

YouTube, Instagram, Facebook, Vimeo, Twitter, etc. have their own logics, dynamics and different audiences. This book analyses how the users of these social networks, especially those of YouTube and Instagram, become content prescribers, opinion leaders and, by extension, people of influence. What influence capacity do they have? Why are intimate or personal aspects shared with unknown people? Who are the big beneficiaries? How much is vanity and how much altruism? What business is behind these social networks? What dangers do they contain? What volume of business can we estimate they generate? How are they transforming cultural industries? What legislation is applied? How does the legislation affect these communications when they are sponsored? Is the privacy of users violated with the data obtained? Who is the owner of the content? Are they to blame for ""fake news""? In this changing, challenging and intriguing environment, The Dynamics of Influencer Marketing discusses all of these questions and more. Considering this complexity from different perspectives: technological, economic, sociological, psychological and legal, the book combines the visions of several experts from the academic world and provides a structured framework with a wide approach to understand the new era of influencing, including the dark sides of it. It will be of direct interest to marketing scholars and researchers while also relevant to many other areas affected by the phenomenon of social media influence