AI for Social Good

This review paper examines the intersection of artificial intelligence (AI) and social sciences, particularly focusing on its potential for promoting social good. Drawing from a selection of research papers, we analyze the applications, methodologies, challenges, and policy implications of AI in various social contexts. We explored how AI technologies are being leveraged in healthcare, education, community, marketing; settings to address the needs of marginalized populations, highlighting the ethical concerns and power dynamics involved. Furthermore, we discuss the importance of feminist perspectives in shaping AI futures to ensure equity and empowerment for underserved communities. Through a critical examination of existing literature, we provide insights into the ways in which AI can be utilized as a tool for social good while emphasizing the need for inclusive and participatory approaches in its development and implementation. This review paper contributes to a deeper understanding of the complex relationship between AI and social sciences, offering recommendations for future research and policy interventions aimed at fostering more equitable and inclusive societies.

Animal trafficking and poaching pose significant threats to global biodiversity, causing irreversible ecological, economic, and social consequences. The illegal trade in wildlife, encompassing both live animals and their body parts, is a lucrative enterprise driven by demand for exotic pets, luxury goods, traditional medicine, and trophy hunting. This multi-billion-dollar industry exploits vulnerable species, pushing many towards the brink of extinction. Loss of keystone species disrupts natural food chains, alters vegetation dynamics, and diminishes biodiversity, thereby compromising the overall stability of ecosystems. Animal trafficking and poaching represent grave threats to global biodiversity, jeopardizing the ecological integrity of ecosystems and the well-being of human societies. The present article provides a comprehensive overview of the detrimental impacts of these illicit activities on wildlife populations and ecosystems.

The Navigate Companion Framework™ is an ethical, evidence-informed methodology developed to guide the design and operation of AI-powered support tools for individuals facing complex emotional and chronic health challenges. It blends therapeutic insights from established clinical models—including CBT, DBT, ACT, CFT, MBSR, and Narrative Therapy—with real-world prompt analysis, user empathy research, and safety-focused system design.

Unlike clinical interventions, Navigate does not aim to diagnose, treat, or replace professional care. Instead, it provides emotionally intelligent, always-available AI companions that offer grounded, supportive conversations for users navigating anxiety, chronic pain, dementia, menopause, and neurodivergent experiences. The framework was built through interdisciplinary research, lived experience consultation, and iterative prompt-response testing, resulting in a design standard focused on trust, validation, and emotional relief.

This document outlines the foundational pillars of the Navigate Methodology, including its psychological roots, safety architecture, AI-specific tone calibration strategies, and ethical boundaries. It serves as a blueprint for responsible AI design in the mental health and self-support domain and is intended for AI developers, health tech innovators, and human-centered design practitioners seeking to replicate or expand the Navigate approach.

AI ethics frameworks—grounded in principles of transparency, fairness, and accountability—have become the dominant mechanism for governing algorithmic systems. However, by treating AI as a neutral technology amenable to technical fixes, they obscure its status as a political project embedded in power, inequality, and historical injustice. In this paper, we introduce the concept of algorithmic contestation—the use of collective, tactical interventions to challenge and reshape the socio-political forces that drive the design and deployment of AI. Drawing on Saul Alinsky’s Rules for Radicals, we derive ten “Rules for Radical AI” that activists and researchers can deploy, including “Reveal the Code,” “Organize a Data Strike,” and “Seize Policy Windows.” We first map mainstream ethics principles onto Alinsky’s tactical logic, then illustrate these dynamics through two case studies—the San Francisco facial recognition ban and a New York City gig worker data strike. Finally, we discuss methodological limits, ethical risks, and future research avenues and issue a manifesto: proper AI accountability arises not from compliance alone but from collective contestation of algorithmic power.

This paper presents a unique AI-powered web application designed to support students in rural areas by providing personalized educational and scholarship opportunities. The tool utilizes natural language processing (NLP) and machine learning (ML) techniques to understand student backgrounds, goals, and challenges, then match them with relevant global programs. Developed using Streamlit and open-source datasets, this solution addresses the digital divide and promotes educational equity.

The EPSILON project - European Platform for Social Data Science Incubation, Learning, Operation and Network - aims to bridge the gap between data science and social good by fostering impactful, research-driven initiatives. Co-funded by the European Union, EPSILON provides open educational resources and best practices for leveraging data science methodologies in addressing societal challenges. This document synthesizes OER learning materials created by the EPSILON project team, structured across six modules, covering fundamental concepts in data science, ethical considerations, comparative analyses of "Data for Good" initiatives, and real-world case studies. Topics range from big data processing workflows and data science project life cycle management to best practices in data science volunteer management and data science applications in social sectors. The materials highlight the growing real-world impact of volunteer-driven, nonprofit data initiatives, with an emphasis on the importance of ethical responsibility, sustainable project design, and scientific collaboration. EPSILON was co-funded by the European Union (2021-1-DE01-KA220-HED-000029711). All views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or DAAD. Neither the European Union nor the granting authority can be held responsible for them.

This paper follows calls for critical approaches to computing and conceptualisations of intersectional, feminist, decolonial HCI and AI design and asks what a feminist intersectional perspective in HCXAI research and design might look like. Sketching out initial research directions and implications for explainable AI design, it suggests that explainability from a feminist perspective would include the fostering of responseability -the capacity to critically evaluate and respond to AI systems -and would centre marginalised perspectives.

The "Loving AI" project involves developing software enabling humanoid robots to interact with people in loving and compassionate ways, and to promote people' self-understanding and self-transcendence. Currently the project centers on the Hanson Robotics robot "Sophia" -- specifically, on supplying Sophia with personality content and cognitive, linguistic, perceptual and behavioral content aimed at enabling loving interactions supportive of human self-transcendence. In September 2017 a small pilot study was conducted, involving the Sophia robot leading human subjects through dialogues and exercises focused on meditation, visualization and relaxation. The pilot was an apparent success, qualitatively demonstrating the viability of the approach and the ability of appropriate human-robot interaction to increase human well-being and advance human consciousness.

AI for Social Good (AI4SG) has been advocated as a way to address social impact problems using emerging technologies, but little research has examined practitioner motivations behind building these tools and how practitioners make such tools understandable to stakeholders and end users, e.g., through leveraging techniques such as explainable AI (XAI). In this study, we interviewed 12 AI4SG practitioners to understand their experiences developing social impact technologies and their perceptions of XAI, focusing on projects in the Global South. While most of our participants were aware of XAI, many did not incorporate these techniques due to a lack of domain expertise, difficulty incorporating XAI into their existing workflows, and perceiving XAI as less valuable for end users with low levels of AI and digital literacy. Our work reflects on the shortcomings of XAI for real-world use and advocates for a reimagined agenda for human-centered explainability research.

The "Loving AI" project involves developing software enabling humanoid robots to interact with people in loving and compassionate ways, and to promote people' self-understanding and self-transcendence. Currently the project centers on the Hanson Robotics robot "Sophia" -- specifically, on supplying Sophia with personality content and cognitive, linguistic, perceptual and behavioral content aimed at enabling loving interactions supportive of human self-transcendence. In September 2017 a small pilot study was conducted, involving the Sophia robot leading human subjects through dialogues and exercises focused on meditation, visualization and relaxation. The pilot was an apparent success, qualitatively demonstrating the viability of the approach and the ability of appropriate human-robot interaction to increase human well-being and advance human consciousness.

The contribution aims to reflect on ongoing experiences of connecting the members of technological world with vulnerable and marginalised communities inside the framework of CommuniCity Horizon Europe project. 15 The project draws on three rounds of open calls starting in cities of Porto, Amsterdam and Helsinki and then 'replicated' in other European cities during the project timeframe of 3 years. In the beginning of the open call rounds, hosting cities announce societal challenges to which the pilot proposals need to respond. The selected by independent jury pilots aim to develop technological solutions tailored to the specific needs of local communities together with the members of those communities, by means of co-creation. 16 The overall aspiration of the project is to accumulate the experiences and learnings on co-creation with disadvantaged groups, to come up with scalable practices and solutions, with the possibility to use the guidelines for successful open call and piloting processes as well as technical components and tools to replicate solutions in other cities and communities.

The Guidelines for Translating Methods, Tools and Principles of Local Innovations for Marginalised and Vulnerable Communities aim to reflect on the ongoing experiences and lessons learned based on the first piloting round in the cities of Amsterdam, Helsinki and Porto inside the framework of CommuniCity Horizon Europe project and propose solutions for ethical and inclusive engagement with communities aimed to translate, in its metaphorical but oftentimes also literal meaning, the aims, definitions, points of reference of the project and its pilots. The overall goal to bring together representatives of the world of technology and vulnerable and marginalised communities implies the need for translation being a starting point for the mutually rewarding engagement and cocreation of technological solutions targeted to the specific needs of cities and communities. The reflective points outlined in this work add value to the next rounds of open calls and consequent piloting processes as well as to the practices exercised by the so-called replicator cities, keeping in mind the overall project goal of proposing scalable solutions to be further taken up by the European cities and communities.

The Guidelines for Translating Methods, Tools and Principles of Local Innovations for Marginalised and Vulnerable Communities aim to reflect on the ongoing experiences and lessons learned based on the first piloting round in the cities of Amsterdam, Helsinki and Porto inside the framework of CommuniCity Horizon Europe project and propose solutions for ethical and inclusive engagement with communities aimed to translate, in its metaphorical but oftentimes also literal meaning, the aims, definitions, points of reference of the project and its pilots. The overall goal to bring together representatives of the world of technology and vulnerable and marginalised communities implies the need for translation being a starting point for the mutually rewarding engagement and cocreation of technological solutions targeted to the specific needs of cities and communities. The reflective points outlined in this work add value to the next rounds of open calls and consequent piloting processes as well as to the practices exercised by the so-called replicator cities, keeping in mind the overall project goal of proposing scalable solutions to be further taken up by the European cities and communities.

Social Impact Research Lab examines opportunities for analytics and artificial intelligence in creating social impact. It presents frameworks and methodologies that utilize data for social good. Existing applications and publications on analytics and artificial intelligence that were crafted within the realm of social good are presented along with the discussion of its current and potential impact on society.

The development of AI to function as communicators (i.e., conversational agents), has opened the opportunity to rethink AI's place within people's social worlds, and the process of sense-making between humans and machines, especially for people with autism who may stand to benefit from such interactions. The current study aims to explore the interactions of six autistic and six nonautistic adults with a conversational virtual human (CVH/conversational agent/chatbot) over 1-4 weeks. Using semi-structured interviews, conversational chatlogs and post-study online questionnaires, we present findings related to human-chatbot interaction, chatbot humanization/dehumanization and chatbot's autistic/non-autistic traits through thematic analysis. Findings suggest that although autistic users are willing to converse with the chatbot, there are no indications of relationship development with the chatbot. Our analysis also highlighted autistic users' expectations of empathy from the chatbot. In the case of the non-autistic users, they tried to stretch the conversational agent's abilities by continuously testing the AI conversational/cognitive skills. Moreover, non-autistic users were content with Kuki's basic conversational skills, while on the contrary, autistic participants expected more in-depth conversations, as they trusted Kuki more. The findings offer insights to a new human-chatbot interaction model specifically for users with autism with a view to supporting them via companionship and social connectedness.

The framework of the seventeen sustainable development goals is a challenge for developers and researchers applying artificial intelligence (AI). AI and earth observations (EO) can provide reliable and disaggregated data for better monitoring of the sustainable development goals (SDGs). In this paper, we present an overview of SDG targets, which can be effectively measured with AI tools. We identify indicators with the most significant contribution from the AI and EO and describe an application of state-of-the-art machine learning models to one of the indicators. We describe an application of U-net with SE blocks for efficient segmentation of satellite imagery for crop detection. Finally, we demonstrate how AI can be more effectively applied in solutions directly contributing towards specific SDGs and propose further research on an AI-based evaluative infrastructure for SDGs.

Since the mid-2010s, chatbots have grown in usage and popularity across the humanitarian sector. While this usage has gained traction, there is scarce information on the collective successes, risks, and trade-offs of this automation. This research addresses this gap, documenting chatbot deployments across the humanitarian sector and exploring the existing uses, benefits, trade-offs and challenges of using chatbots in humanitarian contexts. The research was conducted by The Engine Room, with support from the American Red Cross, the IFRC and valuable input from our advisory committee. This report is not created as a comprehensive overview of chatbots use, instead, the goal of the report is twofold: to offer guiding questions and considerations for humanitarian organisations deciding if a chatbot is an appropriate tool to address programme and community needs. It also contains use cases highlighting the experiences of practitioners working in diverse geographic contexts and issue areas. Executive Summary-3 Main findings Always consider community needs, design limitations and responsible data practices • Before choosing to deploy a chatbot, it's important for humanitarian organisations to properly understand what a chatbot can and cannot do, and to have a clear idea of the resources that will be needed for effective deployment and maintenance over time. • Chatbots should be designed around the priorities and communication practices of the people humanitarian organisations are trying to reach. Where possible, chatbots should be designed together with communities, local humanitarian actors and technologists. • The use of chatbots brings up several responsible data concerns, with a need for more comprehensive risk assessments before chatbot deployment, better safeguarding of vulnerable users, the development of best practices around training data collection and holistic data minimisation strategies. • Automation does not inherently lead to greater efficiency. Any gains presented by chatbots should be considered alongside ethical imperatives that come with a humanitarian mandate and affected populations' specific communication needs. Chatbots work best when they are integrated into existing community engagement approaches and communication channels, and when adequate resources are planned for • Rather than replacing human contact, chatbots can be understood as a complementary component of a larger ecosystem of tools, services, and communication channels. They may support the effective provision of basic information, serve as a "triage" mechanism to aid information provision, or steer people to specific services and human assistance. Successes with chatbots have come from the development of "hybrid" communication channels that incorporate chatbot interactions but are also clearly linked to services staffed by people. • Chatbots have varying degrees of sophistication, and managing user expectations around what the chatbot they are interacting with can (and can't) do is essential for transparency. People interacting with humanitarian organisations should always know when they are talking to a bot, and it should be clear how they can contact a human for further support. • Though chatbots can enable humanitarian organisations to collect useful information about the needs of people in touch with them, it is important for organisations to understand that digitally collected feedback in general represents only a small sample of the full spectrum of needs of the people that humanitarian organisations might be trying to reach. • To successfully integrate chatbots within humanitarian operations, chatbots should be seen as a long-term investment: the resources needed to successfully deploy a chatbot will depend on the type of chatbot rolled out and how many people are expected to interact with it. However, any successful chatbot will require at minimum some degree of continued investment and maintenance, alongside dedicated staff time for design, deployment, and ongoing maintenance.

Using AI chatbots in African psychotherapy is becoming more popular for mental health support. However, it's important to be culturally sensitive and ethical when implementing these chatbots. This abstract discusses the ethical aspects of using AI chatbots for culturally sensitive mental health support in African psychotherapy. It emphasizes considering cultural differences, language diversity, privacy issues, and data security when creating and using AI chatbot interventions. By addressing these ethics properly, AI chatbots can improve access to mental healthcare while respecting different cultures and achieving positive outcomes in African communities.

The main challenge for fine-grained few-shot image classification is to learn feature representations with higher inter-class and lower intra-class variations, with a mere few labelled samples. Conventional few-shot learning methods however cannot be naively adopted for this fine-grained setting-a quick pilot study reveals that they in fact push for the opposite (i.e., lower inter-class variations and higher intraclass variations). To alleviate this problem, prior works predominately use a support set to reconstruct the query image and then utilize metric learning to determine its category. Upon careful inspection, we further reveal that such unidirectional reconstruction methods only help to increase inter-class variations and are not effective in tackling intraclass variations. In this paper, we for the first time introduce a bi-reconstruction mechanism that can simultaneously accommodate for inter-class and intra-class variations. In addition to using the support set to reconstruct the query set for increasing inter-class variations, we further use the query set to reconstruct the support set for reducing intra-class variations. This design effectively helps the model to explore more subtle and discriminative features which is key for the fine-grained problem in hand. Furthermore, we also construct a self-reconstruction module to work alongside the bi-directional module to make the features even more discriminative. Experimental results on three widely used finegrained image classification datasets consistently show considerable improvements compared with other methods. Codes are available at: https://github.com/PRIS-CV/Bi-FRN.

The "Loving AI" project involves developing software enabling humanoid robots to interact with people in loving and compassionate ways, and to promote people' self-understanding and self-transcendence. Currently the project centers on the Hanson Robotics robot "Sophia" -- specifically, on supplying Sophia with personality content and cognitive, linguistic, perceptual and behavioral content aimed at enabling loving interactions supportive of human self-transcendence. In September 2017 a small pilot study was conducted, involving the Sophia robot leading human subjects through dialogues and exercises focused on meditation, visualization and relaxation. The pilot was an apparent success, qualitatively demonstrating the viability of the approach and the ability of appropriate human-robot interaction to increase human well-being and advance human consciousness.

This research report, based on research conducted by The Engine
Room from October 2021 to April 2022, is part of a larger body of
work around the intersection of digital rights with environmental
and climate justice, supported by the Ford Foundation, Ariadne
and Mozilla Foundation. This research project aims at better
equipping digital rights funders to craft grantmaking strategies
that maximise impact on these issues.
This report was published alongside several publications, including
issue briefs by Association for Progressive Communications (APC), BSR,
and the Open Environmental Data Project and Open Climate. All publications
can be found at https://engn.it/climatejusticedigitalrights

In this paper, we analyse patients’ perspectives on the introduction of artificial intelligence (AI) and robotic systems in healthcare. Based on citizens’ experiences when hospitalised for COVID-19, we explore how the opinions and concerns regarding healthcare automation could not be disassociated from a context of high pressure on the health system and lack of resources, and a political discourse on AI and robotics; a situation intensified by the pandemic. Thus, through the analysis of a set of interviews, a series of issues are identified that revolve around the following: the empirical effects of imagined robots, the vivid experience of citizens with the care crisis, the discomfort of the ineffective, the virtualised care assemblages, the human-based face-to-face relationships, and the automatisation of healthcare tasks. In light of these results, we show the variability in patients’ perspectives on AI and robotic systems and explain it by distinguishing two interpretive repertoi...

The 2021 sales volume in the market of service robots is attractive. Expert reports from the International Federation of Robotics confirm 27 billion USD in total market share. Moreover, the number of new startups with the denomination of service robots nowadays constitutes 29% of the total amount of robotic companies recorded in the United States. Those data, among other similar figures, remark the need for formal development in the service robots area, including knowledge transfer and literature reviews. Furthermore, the COVID-19 spread accelerated business units and some research groups to invest time and effort into the field of service robotics. Therefore, this research work intends to contribute to the formalization of service robots as an area of robotics, presenting a systematic review of scientific literature. First, a definition of service robots according to fundamental ontology is provided, followed by a detailed review covering technological applications; state-of-the-ar...

Fine-grained classification aims at distinguishing between items with similar global perception and patterns, but that differ by minute details. Our primary challenges comes from both small inter-class variations and large intra-class variations. In this article, we propose to combine several innovations to improve fine-grained classification within the use-case of fauna, which is of practical interest for experts. We utilize geo-spatiotemporal data to enrich the picture information and further improve the performance. We also investigate state-of-the-art methods for handling the imbalanced data issue.

In this paper, we analyse patients’ perspectives on the introduction of artificial intelligence (AI) and robotic systems in healthcare. Based on citizens’ experiences when hospitalised for COVID-19, we explore how the opinions and concerns regarding healthcare automation could not be disassociated from a context of high pressure on the health system and lack of resources, and a political discourse on AI and robotics; a situation intensified by the pandemic. Thus, through the analysis of a set of interviews, a series of issues are identified that revolve around the following: the empirical effects of imagined robots, the vivid experience of citizens with the care crisis, the discomfort of the ineffective, the virtualised care assemblages, the human-based face-to-face relationships, and the automatisation of healthcare tasks. In light of these results, we show the variability in patients’ perspectives on AI and robotic systems and explain it by distinguishing two interpretive repertoi...

In this paper, we analyse patients’ perspectives on the introduction of artificial intelli- gence (AI) and robotic systems in healthcare. Based on citizens’ experiences when hospitalised for COVID-19, we explore how the opinions and concerns regarding healthcare automation could not be disassociated from a context of high pressure on the health system and lack of resources, and a politi- cal discourse on AI and robotics; a situation intensified by the pandemic. Thus, through the analysis of a set of interviews, a series of issues are identified that revolve around the following: the empirical effects of imagined robots, the vivid experience of citizens with the care crisis, the discomfort of the ineffective, the virtualised care assemblages, the human-based face-to-face relationships, and the automatisation of healthcare tasks. In light of these results, we show the variability in patients’ perspectives on AI and robotic systems and explain it by distinguishing two interpretive repe...

In this paper, we analyse patients’ perspectives on the introduction of artificial intelligence (AI) and robotic systems in healthcare. Based on citizens’ experiences when hospitalised for COVID-19, we explore how the opinions and concerns regarding healthcare automation could not be disassociated from a context of high pressure on the health system and lack of resources, and a political discourse on AI and robotics; a situation intensified by the pandemic. Thus, through the analysis of a set of interviews, a series of issues are identified that revolve around the following: the empirical effects of imagined robots, the vivid experience of citizens with the care crisis, the discomfort of the ineffective, the virtualised care assemblages, the human-based face-to-face relationships, and the automatisation of healthcare tasks. In light of these results, we show the variability in patients’ perspectives on AI and robotic systems and explain it by distinguishing two interpretive repertoi...

In this paper, we analyse patients’ perspectives on the introduction of artificial intelli- gence (AI) and robotic systems in healthcare. Based on citizens’ experiences when hospitalised for COVID-19, we explore how the opinions and concerns regarding healthcare automation could not be disassociated from a context of high pressure on the health system and lack of resources, and a politi- cal discourse on AI and robotics; a situation intensified by the pandemic. Thus, through the analysis of a set of interviews, a series of issues are identified that revolve around the following: the empirical effects of imagined robots, the vivid experience of citizens with the care crisis, the discomfort of the ineffective, the virtualised care assemblages, the human-based face-to-face relationships, and the automatisation of healthcare tasks. In light of these results, we show the variability in patients’ perspectives on AI and robotic systems and explain it by distinguishing two interpretive repertoires that account for different views and opinions: a well-being repertoire and a responsibility repertoire. Both interpretative repertoires are relevant in order to grasp the complexity of citizens’ approaches to automatisation of healthcare. Attending to both allows us to move beyond the dominant (political) discourse of technology markets as the only way to respond to healthcare challenges. Thus, we can analyse and integrate patients’ perspectives to develop AI and robotic systems in healthcare to serve citizens’ needs and collective well-being.

• We face an uphill battle against the illicit wildlife poaching industry given its estimated value of US$7-23 billion. It remains an attractive market as it is regarded as a high-profit, low-risk business. Artificial Intelligence (AI) based approaches promise to flip the script and make it a high-risk pursuit.
• To combat wildlife crime, the World Wildlife Fund’s (WWF) programme identified four core pillars – stop the poaching, stop the trafficking, stop the buying, and international policy. For AI oriented strategies to be successful, it needs to intervene at all four levels.
• Given that half of the world’s wildlife poaching takes place in Africa, and that this continent is plunging into recession due to the pandemic, the illicit wildlife trade is expected to gain strides. This has renewed the urgency for innovative AI based solutions and fostered partnerships between global technology companies and conservation organizations to rise to this challenge.
• This study maps the current AI-based challenges, initiatives, and voices from stakeholders and captures insights to important questions such as to what extent can/does AI mitigate illegal wildlife trafficking problems? What are the different beliefs among stakeholders about wildlife poaching and online trafficking and why? How is AI being embedded in these initiatives?
• There is a bias towards market-based solutions among the African stakeholders at a time when the international funding sectors in Europe are going against such measures. Cultural perspectives matter in AI-led enforcement, which demands local buy-in.
• Exorbitant costs to sustain AI interventions sit uncomfortably with major resource scarcities in pay for the rangers and their informant networks on the ground, still seen by conservationists as the most “intelligent” way to combat the trade.
• Anti-poaching tracking initiatives need to address ongoing dilemmas of data governance such as data cooperation vs data localization/ownership, and open science vs privacy/security to have real impact.

Artificial intelligence (AI) applications have been introduced in humanitarian operations in order to help with the significant challenges the sector is facing. This article focuses on chatbots which have been proposed as an efficient method to improve communication with, and accountability to affected communities. Chatbots, together with other humanitarian AI applications such as biometrics, satellite imaging, predictive modelling and data visualisations, are often understood as part of the wider phenomenon of ‘AI for social good’. The article develops a decolonial critique of humanitarianism and critical algorithm studies which focuses on the power asymmetries underpinning both humanitarianism and AI. The article asks whether chatbots, as exemplars of ‘AI for good’, reproduce inequalities in the global context. Drawing on a mixed methods study that includes interviews with seven groups of stakeholders, the analysis observes that humanitarian chatbots do not fulfil claims such as ‘intelligence’. Yet AI applications still have powerful consequences. Apart from the risks associated with misinformation and data safeguarding, chatbots reduce communication to its barest instrumental forms which creates disconnects between affected communities and aid agencies. This disconnect is compounded by the extraction of value from data and experimentation with untested technologies. By reflecting the values of their designers and by asserting Eurocentric values in their programmed interactions, chatbots reproduce the coloniality of power. The article concludes that ‘AI for good’ is an ‘enchantment of technology’ that reworks the colonial legacies of humanitarianism whilst also occluding the power dynamics at play.

Fine-grained classification aims at distinguishing between items with similar global perception and patterns, but that differ by minute details. Our primary challenges come from both small inter-class variations and large intra-class variations. In this article, we propose to combine several innovations to improve fine-grained classification within the use-case of wildlife, which is of practical interest for experts. We utilize geo-spatiotemporal data to enrich the picture information and further improve the performance. We also investigate state-of-the-art methods for handling the imbalanced data issue.

Our grand challenge is to create emotionally-sensitive AI embedded in social humanoid robots and avatars in order to help individuals advance in the hierarchy of human development. The peak of this hierarchy is self-transcendence, including expansive feelings of love. In this paper we present results of the first experiments of which we are aware in which AI-driven, audiovisual , interactive android technology is successfully used to support the experience of self-transcendence. Specifically, we designed two studies in which people had brief, constrained AI-driven conversations with emotionally responsive AI embedded in a humanoid robot, its audiovisual avatar, or audio-alone avatar. These conversations were based on exercises reported to induce self-transcendence in humans. In experiment 1, we tested an initial version of this AI using brief, constrained interactions with Sophia the humanoid robot and no emotion detection (N=26). In experiment 2, we tested a more sophisticated version of this AI including deep-learning-based emotion detection deployed in the context of a slightly longer and slightly less constrained interaction, in a between-groups design: conversations were with either Sophia or one of two avatars (one with a face and voice, the other with only a voice; N=35). By the time we submitted our first report last year, we had planned but not completed the first study, so in this report we summarize the hypotheses, methods, and results of both studies. The results suggest that conversations between humans and a humanoid robot or its audiovisual avatar, controlled by emotionally responsive AI, are accompanied by self-transcendent emotions, and that objective correlates of those feelings are detectable by a deep learning network.

Has Technology got us under a spell-Are people becoming machine junkies? Is this technology making us Digitally Demented or Digitally enhanced? Pre COVID we can all see how technology and AI has increased our digital dependency like never before. Using it to stay connected, shop, and work. It has redesigned our lives and our behaviours influencing what we experience feel and see. Leading, many to ask themselves some very profound questions on the nature and future of lifestyle and mental and physical health.