[How] Can Pluralist Approaches to Computational Cognitive Modeling of Human Needs and Values Save our Democracies?

In our increasingly digital societies, many companies have business models that perceive users’ (or customers’) personal data as a siloed resource, owned and controlled by the data controller rather than the data subjects. Collecting and processing such a massive amount of personal data could have many negative technical, social and economic consequences, including invading people’s privacy and autonomy. As a result, regulations such as the European General Data Protection Regulation (GDPR) have tried to take steps towards a better implementation of the right to digital privacy. This paper proposes that such legal acts should be accompanied by the development of complementary technical solutions such as Cognitive Personal Assistant Systems to support people to effectively manage their personal data processing on the Internet. Considering the importance and sensitivity of personal data processing, such assistant systems should not only consider their owner’s needs and values, but also be transparent, accountable and controllable. Pluralist approaches in computational cognitive modelling of human needs and values which are not bound to traditional paradigmatic borders such as cognitivism, connectionism, or enactivism, we argue, can create a balance between practicality and usefulness, on the one hand, and transparency, accountability, and controllability, on the other, while supporting and empowering humans in the digital world. Considering the threat to digital privacy as significant to contemporary democracies, the future implementation of such pluralist models could contribute to power-balance, fairness and inclusion in our societies

Towards player-driven procedural content generation

Generating immersive game content is one of the ultimate goals for a game designer. This goal can be achieved by realizing the fact that players' perception of the same game differ according to a number of factors including: players' personality, playing styles, expertise and culture background. While one player might find the game immersive, others may quit playing as a result of encountering a seemingly insoluble problem. One promising avenue towards optimizing the gameplay experience for individual game players is to tailor player experience in real-time via automatic game content generation. Specifying the aspects of the game that have the major influence on the gameplay experience, identifying the relationship between these aspect and each individual experience and defining a mechanism for tailoring the game content according to each individual needs are important steps towards player-driven content generation.peer-reviewe

Towards Knowledge-Based Personalized Product Description Generation in E-commerce

Quality product descriptions are critical for providing competitive customer experience in an e-commerce platform. An accurate and attractive description not only helps customers make an informed decision but also improves the likelihood of purchase. However, crafting a successful product description is tedious and highly time-consuming. Due to its importance, automating the product description generation has attracted considerable interests from both research and industrial communities. Existing methods mainly use templates or statistical methods, and their performance could be rather limited. In this paper, we explore a new way to generate the personalized product description by combining the power of neural networks and knowledge base. Specifically, we propose a KnOwledge Based pErsonalized (or KOBE) product description generation model in the context of e-commerce. In KOBE, we extend the encoder-decoder framework, the Transformer, to a sequence modeling formulation using self-attention. In order to make the description both informative and personalized, KOBE considers a variety of important factors during text generation, including product aspects, user categories, and knowledge base, etc. Experiments on real-world datasets demonstrate that the proposed method out-performs the baseline on various metrics. KOBE can achieve an improvement of 9.7% over state-of-the-arts in terms of BLEU. We also present several case studies as the anecdotal evidence to further prove the effectiveness of the proposed approach. The framework has been deployed in Taobao, the largest online e-commerce platform in China.Comment: KDD 2019 Camera-ready. Website: https://sites.google.com/view/kobe201

Nano: Nested Human-in-the-Loop Reward Learning for Few-shot Language Model Control

Pretrained language models have demonstrated extraordinary capabilities in language generation. However, real-world tasks often require controlling the distribution of generated text in order to mitigate bias, promote fairness, and achieve personalization. Existing techniques for controlling the distribution of generated text only work with quantified distributions, which require pre-defined categories, proportions of the distribution, or an existing corpus following the desired distributions. However, many important distributions, such as personal preferences, are unquantified. In this work, we tackle the problem of generating text following arbitrary distributions (quantified and unquantified) by proposing Nano, a few-shot human-in-the-loop training algorithm that continuously learns from human feedback. Nano achieves state-of-the-art results on single topic/attribute as well as quantified distribution control compared to previous works. We also show that Nano is able to learn unquantified distributions, achieves personalization, and captures differences between different individuals' personal preferences with high sample efficiency.Comment: Accepted to ACL Findings 202

A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations

Despite the growing availability of artificial intelligence models for predicting type 2 diabetes, there is still a lack of personalized approaches to quantify minimum viable changes in biomarkers that may help reduce the individual risk of developing disease. The aim of this article is to develop a new method, based on counterfactual explanations, to generate personalized recommendations to reduce the one-year risk of type 2 diabetes. Ten routinely collected biomarkers extracted from Electronic Medical Records of 2791 patients at low risk and 2791 patients at high risk of type 2 diabetes were analyzed. Two regions characterizing the two classes of patients were estimated using a Support Vector Data Description classifier. Counterfactual explanations (i.e., minimal changes in input features able to change the risk class) were generated for patients at high risk and evaluated using performance metrics (availability, validity, actionability, similarity, and discriminative power) and a qualitative survey administered to seven expert clinicians. Results showed that, on average, the requested minimum viable changes implied a significant reduction of fasting blood sugar, systolic blood pressure, and triglycerides and a significant increase of high-density lipoprotein in patients at risk of diabetes. A significant reduction in body mass index was also recommended in most of the patients at risk, except in females without hypertension. In general, greater changes were recommended in hypertensive patients compared to non-hypertensive ones. The experts were overall satisfied with the proposed approach although in some cases the proposed recommendations were deemed insufficient to reduce the risk in a clinically meaningful way. Future research will focus on a larger set of biomarkers and different comorbidities, also incorporating clinical guidelines whenever possible. Development of additional mathematical and clinical validation approaches will also be of paramount importance

Nanoscale metal–organic frameworks as key players in the context of drug delivery: evolution toward theranostic platforms

This is a post-peer-review, pre-copyedit version of an article published in Analytical and Bioanalytical Chemistry. The final authenticated version is available online at: https://doi.org/10.1007/s00216-019-02217-yMetal–organic frameworks (MOFs) have emerged as one of the most fascinating libraries of porous materials with a huge potential in very diverse application areas. In particular, the bioanalytical and biomedical fields have evolved tremendously due to the emergence of these hybrid inorganic–organic MOF-based materials. This is because these materials possess a series of key properties essential for bioapplications, such as minimal toxicity to living cells, intrinsic biodegradability, and possibility of synthesizing with nanoscale sizes. Additional properties of MOFs such as ultra-large surface-to-volume ratios, tunable pore size, high drug loading capacity, tunable structure and chemical composition, and potential for multiple postsynthetic modification make them ideal candidates for drug delivery. This review highlights recent research progress on MOF-based drug delivery systems (DDS), pointing out the evolution of these systems toward the development of theranostic nanoplatforms. Rather than a comprehensive review, representative recent examples are selected to illustrate such an evolution, and a critical discussion of the advantages and limitations of the different DDS types is given. Finally, the remaining challenges and future opportunities in this field are presented, highlighting that overcoming the current issues will pave the way toward the elusive dream of “personalized medicine.”C.C.C. thanks the financial support from the European Union under the H2020-MSCA-IF-2016 program (MSCA-IF-EF-ST Grant Agreement No. 749667)S