scholarly journals From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation

2021 ◽  
Vol 8 ◽  
Author(s):  
Juana Valeria Hurtado ◽  
Laura Londoño ◽  
Abhinav Valada
Keyword(s):  
Machine Learning ◽  
Social Relationships ◽  
Robot Navigation ◽  
Urban Environments ◽  
Social Robot ◽  
Social Environments ◽  
Social Navigation ◽  
Ethical Implications ◽  
Social Abilities ◽  
Compliant Robot

The exponentially increasing advances in robotics and machine learning are facilitating the transition of robots from being confined to controlled industrial spaces to performing novel everyday tasks in domestic and urban environments. In order to make the presence of robots safe as well as comfortable for humans, and to facilitate their acceptance in public environments, they are often equipped with social abilities for navigation and interaction. Socially compliant robot navigation is increasingly being learned from human observations or demonstrations. We argue that these techniques that typically aim to mimic human behavior do not guarantee fair behavior. As a consequence, social navigation models can replicate, promote, and amplify societal unfairness, such as discrimination and segregation. In this work, we investigate a framework for diminishing bias in social robot navigation models so that robots are equipped with the capability to plan as well as adapt their paths based on both physical and social demands. Our proposed framework consists of two components: learning which incorporates social context into the learning process to account for safety and comfort, and relearning to detect and correct potentially harmful outcomes before the onset. We provide both technological and societal analysis using three diverse case studies in different social scenarios of interaction. Moreover, we present ethical implications of deploying robots in social environments and propose potential solutions. Through this study, we highlight the importance and advocate for fairness in human-robot interactions in order to promote more equitable social relationships, roles, and dynamics and consequently positively influence our society.

scholarly journals Facilitating fine-grained intra-urban dengue forecasting by integrating urban environments measured from street-view images

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Kang Liu ◽  
Ling Yin ◽  
Meng Zhang ◽  
Min Kang ◽  
Ai-Ping Deng ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Feature Vector ◽  
Urban Environments ◽  
Environmental Feature ◽  
Fine Grained ◽  
Environmental Features ◽  
Street View ◽  
The City ◽  
Dengue Epidemic

Abstract Background Dengue fever (DF) is a mosquito-borne infectious disease that has threatened tropical and subtropical regions in recent decades. An early and targeted warning of a dengue epidemic is important for vector control. Current studies have primarily determined weather conditions to be the main factor for dengue forecasting, thereby neglecting that environmental suitability for mosquito breeding is also an important factor, especially in fine-grained intra-urban settings. Considering that street-view images are promising for depicting physical environments, this study proposes a framework for facilitating fine-grained intra-urban dengue forecasting by integrating the urban environments measured from street-view images. Methods The dengue epidemic that occurred in 167 townships of Guangzhou City, China, between 2015 and 2019 was taken as a study case. First, feature vectors of street-view images acquired inside each township were extracted by a pre-trained convolutional neural network, and then aggregated as an environmental feature vector of the township. Thus, townships with similar physical settings would exhibit similar environmental features. Second, the environmental feature vector is combined with commonly used features (e.g., temperature, rainfall, and past case count) as inputs to machine-learning models for weekly dengue forecasting. Results The performance of machine-learning forecasting models (i.e., MLP and SVM) integrated with and without environmental features were compared. This indicates that models integrating environmental features can identify high-risk urban units across the city more precisely than those using common features alone. In addition, the top 30% of high-risk townships predicted by our proposed methods can capture approximately 50–60% of dengue cases across the city. Conclusions Incorporating local environments measured from street view images is effective in facilitating fine-grained intra-urban dengue forecasting, which is beneficial for conducting spatially precise dengue prevention and control.

Socio-Ethical Impact of the Emerging Smart Technologies

2018 ◽  
pp. 226-240
Author(s):  
Octavian M. Machidon
Keyword(s):  
New Technology ◽  
Current Trend ◽  
Societal Implications ◽  
Social Environments ◽  
Privacy And Security ◽  
Ethical Implications ◽  
The Social ◽  
Smart Technologies ◽  
Research Domains ◽  
Technical Considerations

Today, technology is being integrated in all social environments, at home, school, or work, shaping a new world in which there is a closer interaction between human and machine than ever before. While every new technology brings along the expected “blessings,” there is also the thick end of the stick, namely the potential undesired effects it might cause. Explorative research in smart and enhancing technologies reveals that the current trend is for them to transcend to persuasive technologies, capable of shaping human behavior. In this context, this chapter aims at identifying the social and ethical implications of such technologies, being elaborated after reviewing literature from various research domains. It addresses the implications of today's smart and enhancing technologies on several levels: health repercussions, the social and behavioral changes they generate, and concerns of privacy and security. Also, the chapter emphasizes the need for scientists and researchers to engage not only with the technical considerations, but also with the societal implications mentioned above.

scholarly journals SocNav1: A Dataset to Benchmark and Learn Social Navigation Conventions

Data ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 7 ◽  
Author(s):  
Luis J. Manso ◽  
Pedro Nuñez ◽  
Luis V. Calderita ◽  
Diego R. Faria ◽  
Pilar Bachiller
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Structures ◽  
Machine Learning Algorithms ◽  
Training Algorithms ◽  
Social Navigation ◽  
As Graph ◽  
Graph Neural Networks ◽  
Gathering Data ◽  
Culture Dependent

Datasets are essential to the development and evaluation of machine learning and artificial intelligence algorithms. As new tasks are addressed, new datasets are required. Training algorithms for human-aware navigation is an example of this need. Different factors make designing and gathering data for human-aware navigation datasets challenging. Firstly, the problem itself is subjective, different dataset contributors will very frequently disagree to some extent on their labels. Secondly, the number of variables to consider is undetermined culture-dependent. This paper presents SocNav1, a dataset for social navigation conventions. SocNav1 aims at evaluating the robots’ ability to assess the level of discomfort that their presence might generate among humans. The 9280 samples in SocNav1 seem to be enough for machine learning purposes given the relatively small size of the data structures describing the scenarios. Furthermore, SocNav1 is particularly well-suited to be used to benchmark non-Euclidean machine learning algorithms such as graph neural networks. This paper describes the proposed dataset and the method employed to gather the data. To provide a further understanding of the nature of the dataset, an analysis and validation of the collected data are also presented.

scholarly journals Machine learning approaches to understand the influence of urban environments on human’s physiological response

2019 ◽  
Vol 474 ◽  
pp. 154-169 ◽  
Author(s):  
Varun Kumar Ojha ◽  
Danielle Griego ◽  
Saskia Kuliga ◽  
Martin Bielik ◽  
Peter Buš ◽  
...  
Keyword(s):  
Machine Learning ◽  
Physiological Response ◽  
Urban Environments ◽  
Learning Approaches

How 15- to 16-year-old Swedish adolescents experience social relationships and health-related behaviours: A qualitative study

2019 ◽  
Vol 48 (5) ◽  
pp. 527-536
Author(s):  
Ulrica Paulsson Do ◽  
Birgitta Edlund ◽  
Christina Stenhammar ◽  
Ragnar Westerling
Keyword(s):  
Social Relationships ◽  
Qualitative Content Analysis ◽  
Well Being ◽  
Social Contexts ◽  
Social Environments ◽  
Personal Management ◽  
The Social ◽  
Study Results ◽  
Old Swedish ◽  
Health Related

Aims: Health-related behaviours are associated with social relationships. Adolescence is a time when healthy and unhealthy behaviours are established. There is a need to investigate adolescents’ views on how social relationships are related to health-related behaviours of adolescents in the Scandinavian welfare system. This study aimed to explore Swedish adolescents’ experiences and thoughts of how social relationships in different social environments are related to health-related behaviours. Methods: A total of 36 adolescents aged 15–16 years were interviewed in seven focus-group sessions. Qualitative content analysis was used for analysis of the transcribed interviews. Results: Two themes – social context and personal management – emerged. Swedish adolescents describe that their health-related behaviours as being partly shaped by their own personal management but mainly by the social contexts that surround them. Social contexts were expressed as playing a role in the adolescents’ health-related behaviours, as they provide fellowship, pressure, dependability and engagement. Fellowship with friends and family was expressed as providing healthy behaviours and high levels of well-being. Fellowship with friends was particularly important for physical activity. Close relationships were stated to influence health-related behaviours. Pressure from friends, teachers and social media were described as mainly influencing unhealthy behaviours and, to some extent, low levels of well-being. However, adolescents’ personal ability illustrated how adolescents shaped their own health-related behaviours. Conclusions: The study results contribute to the understanding of Swedish adolescents’ views on how social relationships can shape their health-related behaviours. The findings may be useful to school professionals in supporting adolescents to improve well-being and healthy behaviours.

Accountable algorithms? The ethical implications of data-driven business models

2020 ◽  
Vol 31 (2) ◽  
pp. 163-185 ◽  
Author(s):  
Christoph F. Breidbach ◽  
Paul Maglio
Keyword(s):  
Machine Learning ◽  
Business Models ◽  
Analytical Framework ◽  
Data Driven ◽  
Service Research ◽  
Future Research ◽  
Cost Models ◽  
Ethical Challenges ◽  
Content Type ◽  
Ethical Implications

PurposeThe purpose of this study is to identify, analyze and explain the ethical implications that can result from the datafication of service.Design/methodology/approachThis study uses a midrange theorizing approach to integrate currently disconnected perspectives on technology-enabled service, data-driven business models, data ethics and business ethics to introduce a novel analytical framework centered on data-driven business models as the general metatheoretical unit of analysis. The authors then contextualize the framework using data-intensive insurance services.FindingsThe resulting midrange theory offers new insights into how using machine learning, AI and big data sets can lead to unethical implications. Centered around 13 ethical challenges, this work outlines how data-driven business models redefine the value network, alter the roles of individual actors as cocreators of value, lead to the emergence of new data-driven value propositions, as well as novel revenue and cost models.Practical implicationsFuture research based on the framework can help guide practitioners to implement and use advanced analytics more effectively and ethically.Originality/valueAt a time when future technological developments related to AI, machine learning or other forms of advanced data analytics are unpredictable, this study instigates a critical and timely discourse within the service research community about the ethical implications that can arise from the datafication of service by introducing much-needed theory and terminology.

scholarly journals From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices

2019 ◽  
Vol 26 (4) ◽  
pp. 2141-2168 ◽  
Author(s):  
Jessica Morley ◽  
Luciano Floridi ◽  
Libby Kinsey ◽  
Anat Elhalal
Keyword(s):  
Machine Learning ◽  
New York ◽  
Future Research ◽  
Ethical Implications ◽  
Ethical Debate ◽  
Ai Ethics ◽  
Development Pipeline ◽  
Closing The Gap ◽  
Learning Development ◽  
The 1960S

AbstractThe debate about the ethical implications of Artificial Intelligence dates from the 1960s (Samuel in Science, 132(3429):741–742, 1960. 10.1126/science.132.3429.741; Wiener in Cybernetics: or control and communication in the animal and the machine, MIT Press, New York, 1961). However, in recent years symbolic AI has been complemented and sometimes replaced by (Deep) Neural Networks and Machine Learning (ML) techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles—the ‘what’ of AI ethics (beneficence, non-maleficence, autonomy, justice and explicability)—rather than on practices, the ‘how.’ Awareness of the potential issues is increasing at a fast rate, but the AI community’s ability to take action to mitigate the associated risks is still at its infancy. Our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers apply ethics at each stage of the Machine Learning development pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs.

scholarly journals A neurocognitive investigation of the impact of socializing with a robot on empathy for pain

2019 ◽  
Vol 374 (1771) ◽  
pp. 20180034 ◽  
Author(s):  
Emily S. Cross ◽  
Katie A. Riddoch ◽  
Jaydan Pratts ◽  
Simon Titone ◽  
Bishakha Chaudhury ◽  
...  
Keyword(s):  
Social Relationships ◽  
Functional Neuroimaging ◽  
Region Of Interest ◽  
Brain Regions ◽  
Social Robot ◽  
Human Robot Interaction ◽  
Functional Region ◽  
Repetition Suppression ◽  
Empathy For Pain ◽  
The Impact

To what extent can humans form social relationships with robots? In the present study, we combined functional neuroimaging with a robot socializing intervention to probe the flexibility of empathy, a core component of social relationships, towards robots. Twenty-six individuals underwent identical fMRI sessions before and after being issued a social robot to take home and interact with over the course of a week. While undergoing fMRI, participants observed videos of a human actor or a robot experiencing pain or pleasure in response to electrical stimulation. Repetition suppression of activity in the pain network, a collection of brain regions associated with empathy and emotional responding, was measured to test whether socializing with a social robot leads to greater overlap in neural mechanisms when observing human and robotic agents experiencing pain or pleasure. In contrast to our hypothesis, functional region-of-interest analyses revealed no change in neural overlap for agents after the socializing intervention. Similarly, no increase in activation when observing a robot experiencing pain emerged post-socializing. Whole-brain analysis showed that, before the socializing intervention, superior parietal and early visual regions are sensitive to novel agents, while after socializing, medial temporal regions show agent sensitivity. A region of the inferior parietal lobule was sensitive to novel emotions, but only during the pre-socializing scan session. Together, these findings suggest that a short socialization intervention with a social robot does not lead to discernible differences in empathy towards the robot, as measured by behavioural or brain responses. We discuss the extent to which long-term socialization with robots might shape social cognitive processes and ultimately our relationships with these machines. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

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