Penn State CSRAI Research
Majumdar, A., Zhang, W., Prawal, K., & Yadav, A. (2026, June). The Hardness of achieving impact in AI for social impact research: a ground-level view of challenges & opportunities . In The 2026 ACM Conference on Fairness, Accountability, and Transparency (pp. 5886-5914). Mingole, B., Majumdar, A., Choudhury, F. A., Kraschnewski, J. L., Sundar, S. S., & Yadav, A . (2026, June). Dr. GPT Will See You Now, but Should It? Exploring the Benefits and Harms of Large Language Models for Health Advice using Crowdsourced Clinical Cases . In The 2026 ACM Conference on Fairness, Accountability, and Transparency (pp. 3274-3301). Li, Y., Saragih, I. D., Fick, D. M., Sundar, S. S ., & Abdullah, S . (2026, July). A Real-Time Voice Agent for Delivering Individual Cognitive Stimulation Therapy to Persons Living with Dementia. In Proceedings of the 8th ACM Conference on Conversational User Interfaces (pp. 1-6). Saragih, I. D., Hwang, P., Yadav, D., Qiu, L., Fick, D. M., Sundar, S. S ., & Abdullah, S . (2026). Efficacy of digital cognitive stimulation therapy for people with dementia: a systematic review and meta-analysis. BMC Geriatrics . Kim, D., Ju, S., Lee, H. M., Kang, M., Kim, M., Park, Y., Song, H., Sundar, S. S ., & Han, J. (2026, April). Using LLM for Conversational Assessment of Mental Health: Testing the Role of Message Contingency and User Agency . In Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (pp. 1-7). BN, S., Sherrill, A., Arriaga, R. I., Wiese, C., & Abdullah, S. (2026). Thousand Voices of Trauma: A Large-Scale Synthetic Dataset for Modeling Prolonged Exposure Therapy Conversations . Advances in Neural Information Processing Systems , 38 . Sherrill, A. M., Wiese, C. W., Abdullah, S. , & Arriaga, R. I. (2025). Teaming with artificial intelligence to learn and sustain psychotherapy delivery skills: Workplace, ethical, and research implications . Journal of Technology in Behavioral Science , 1-9. Domfeh, E. A., & Dancy, C. L. (2025, September). Human-AI Use Patterns for Decision-Making in Disaster Scenarios: A Systematic Review . In 2025 IEEE International Symposium on Technology and Society (ISTAS) (pp. 1-10). IEEE. Wang, X., Zhang, W., Zhang, S., Guo, H., Mingole, B., Sundar, S. S ., ... & Yadav, A . (2025, April). Have LLMs reopened the Pandora’s Box of AI-generated fake news? . In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 2795-2811). Wilkinson, K. M ., Sowers, D. J., Elko, L. R., Case, M., Redmon, S., Williams, K., ... & Billah, S. (2026). Consideration of artificial intelligence applications for interpreting communicative movements by individuals with visual and/or motor disabilities . Augmentative and Alternative Communication , 42 (1), 56-68.Xie, J., Yu, R., Zhang, H., Billah, S. M., Lee, S., & Carroll, J. M . (2025, April). Beyond Visual Perception: Insights from Smartphone Interaction of Visually Impaired Users with Large Multimodal Models. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (pp. 1-17). Swinger, N., Baseman, C. M., Ryu, M., Abdullah, S. , Wiese, C. W., Sherrill, A. M., & Arriaga, R. I. (2025). There's No" I" in TEAMMAIT: Impacts of Domain and Expertise on Trust in AI Teammates for Mental Health Work. In Proceedings of the ACM on Human-Computer Interaction, 9 (2), 1-36. Qiu, L., Saragih, I. D., Fick, D. M., Sundar, S. S. , & Abdullah, S. (2025, April). Voice Assistants to Deliver Cognitive Stimulation Therapy for Persons Living with Dementia. In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (pp. 1-7). BN, S., Mattioli, D., Abdullah, S. , Arriaga, R. I., Wiese, C. W., & Sherrill, A. M. (2025). Thousand Voices of Trauma: A Large-Scale Synthetic Dataset for Modeling Prolonged Exposure Therapy Conversations. arXiv preprint arXiv:2504.13955. Sherrill, A. M., Wiese, C. W., Abdullah, S. , & Arriaga, R. I. (2025). Teaming with Artificial Intelligence to Learn and Sustain Psychotherapy Delivery Skills: Workplace, Ethical, and Research Implications. Journal of Technology in Behavioral Science, 1-9. de Chantal, P. L., Beaty, R. , Laverghetta Jr, A. , Pronchick, J., Patterson, J. D., Organisciak, P., ... & Karwowski, M. (2025). Artificial Intelligence Enhances Human Creativity Through Real-Time Evaluative Feedback . (preprint) Luchini, S. A., Maliakkal, N. T., DiStefano, P. V., Laverghetta Jr, A. , Patterson, J. D., Beaty, R. E. , & Reiter-Palmon, R. (2025). Automated scoring of creative problem solving with large language models: A comparison of originality and quality ratings. Psychology of Aesthetics, Creativity, and the Arts. Kabir, I., Reza, M. A., & Billah, S. (2025). Logic-RAG: Augmenting Large Multimodal Models with Visual-Spatial Knowledge for Road Scene Understanding. arXiv preprint arXiv:2503.12663. Srinath, S., & Dey, S. (2024). A Machine Learning Approach toward Cyber-Physical Security of Battery Systems. Journal of Dynamic Systems, Measurement, and Control, 146 (6). Chang, A., Huang, L., Bhatia, P., Kass-Hout, T., Ma, F. , & Xiao, C. (2025). MedHEval: Benchmarking Hallucinations and Mitigation Strategies in Medical Large Vision-Language Models. arXiv preprint arXiv:2503.02157. Pang, Y., Xiong, A. , Zhang, Y., & Wang, T. (2024). Towards understanding unsafe video generation. arXiv preprint arXiv:2407.12581. Han, H. J., Mendu, S., Jaworski, B. K., Owen, J. E., & Abdullah, S. (2024). Assessing acceptance and feasibility of a conversational agent to support individuals living with post-traumatic stress disorder. Digital Health, 10 , 20552076241286133. Zhang, H., Falletta, N. J., Xie, J., Yu, R., Lee, S., Billah, S. M. , & Carroll, J. M. (2024). Enhancing the Travel Experience for People with Visual Impairments through Multimodal Interaction: NaviGPT, A Real-Time AI-Driven Mobile Navigation System. arXiv preprint arXiv:2410.04005. Chen, T., & Kabir, M. F. (2024). Explainable machine learning approach for cancer prediction through binarilization of RNA sequencing data. PLOS One, 19 (5), e0302947. Schenker, Y., Abdullah, S. , Arnold, R., & Schmitz, K. H. (2024). Conversational agents in palliative care: potential benefits, risks, and next steps. Journal of Palliative Medicine, 27 (3), 296-300 Islam, M. T., Kabir, I., Pearce, E. A., Reza, M. A., & Billah, S. M. (2024). Identifying Crucial Objects in Blind and Low-Vision Individuals' Navigation. arXiv preprint arXiv:2408.13175 Kunchay, S., Linden-Carmichael, A. N., & Abdullah, S. (2024). Using a Smartwatch App to Understand Young Adult Substance Use: Mixed Methods Feasibility Study. JMIR Human Factors, 11 , e50795. Shah, R. S., Otchere, D., Petucci, J., Khursheed, A. M., Raco, J., Honavar, V. G. , & Maheshwari, A. (2024). Analysis of a Single Heart Beat with Deep Learning for Prediction of Atrial Fibrillation in Patients with Cryptogenic Stroke: A Novel Approach to Electrocardiogram Data Augmentation. Heart Rhythm, 21 (9), S784-S785. Mendu, S., Doyle Fosco, S. L., Lanza, S. T., & Abdullah, S. (2023). Designing voice interfaces to support mindfulness-based pain management . Digital Health, 9 , 20552076231204418. Lekoubou, A., Petucci, J., Femi Ajala, T., Katoch, A., Hong, J., Sen, S., Bonilha, L., Chinchilli, V. M., & Honavar, V. (2024). Can machine learning predict late seizures after intracerebral hemorrhages? Evidence from real-world data. Epilepsy & Behavior , 157 , 109835. Lucas, J., Uchendu, A., Yamashita, M., Lee, J., Rohatgi, S., & Lee, D. (2023). Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation Zokaeinikoo, M., Kazemian, P., & Mitra, P. (2023). Interpretable Hierarchical Deep Learning Model for Noninvasive Alzheimer’s Disease Diagnosis. INFORMS Journal on Data Science , 2 (2), 183–196. Li, X., Chen, L., & Wu, D. (2023). Adversary for Social Good: Leveraging Attribute-Obfuscating Attack to Protect User Privacy on Social Networks. In F. Li, K. Liang, Z. Lin, & S. K. Katsikas (Eds.), Security and Privacy in Communication Networks (pp. 710–728). Springer Nature Switzerland. Zhang, W., Guo, H., Ranganathan, P., Patel, J., Rajasekharan, S., Danayak, N., Gupta, M., & Yadav, A. (2023). TRIM-AI: Harnessing Language Models for Providing Timely Maternal & Neonatal Care in Low-Resource Countries . Tabar, M., Lee, D., Hughes, D. P., & Yadav, A. (2022). Mitigating Low Agricultural Productivity of Smallholder Farms in Africa: Time-Series Forecasting for Environmental Stressors. Proceedings of the AAAI Conference on Artificial Intelligence , 36 (11), Article 11. https://doi.org/10.1609/aaai.v36i11.21534 Cotter, K ., & Reisdorf, B. (2020). Algorithmic Knowledge Gaps: A New Dimension of (Digital) Inequality . International Journal of Communication, 14, 745–765. Cotter, K ., DeCook, J. R., & Kanthawala, S. (2022). Fact-Checking the Crisis: COVID-19, Infodemics, and the Platformization of Truth . Social Media+ Society, 8(1), 20563051211069050. Gupta, A., Badr, Y ., Negahban, A., & Qiu, R. G . (2021). Energy-efficient heating control for smart buildings with deep reinforcement learning . Journal of Building Engineering, 34, 101739. Hsu, Y. C., Huang, T. H. K ., Hu, T. Y., Dille, P., Prendi, S., Hoffman, R., ... & Nourbakhsh, I. (2021, May). Project RISE: recognizing industrial smoke emissions . In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 17, pp. 14813-14821). Li, Z. S., Li, Y.-F., Qiu, R. G ., & Zio, E. (2022). Guest Editorial: Special Section on AI Enhanced Reliability Assessment and Predictive Health Management . IEEE Transactions on Industrial Informatics, 18(10), 7196–7197. Masrur, A., Yu, M., Mitra, P ., Peuquet, D., & Taylor, A. (2022). Interpretable machine learning for analysing heterogeneous drivers of geographic events in space-time . International Journal of Geographical Information Science, 36(4), 692-719. Passonneau, R. J ., McNamara, D., Muresan, S., & Perin, D. (2017). Preface: Special Issue on Multidisciplinary Approaches to AI and Education for Reading and Writing . International Journal of Artificial Intelligence in Education, 27(4), 665–670. Shu, K., Sliva, A., Wang, S ., Tang, J., & Liu, H. (2017). Fake News Detection on Social Media: A Data Mining Perspective . ACM SIGKDD Explorations Newsletter, 19(1), 22–36. Singh, P., Gnesdilow, D., Cang, X., Baker, S., Goss, W., Kim, C., Passonneau, R. J ., & Puntambekar, S. (2022). Design of Real-time Scaffolding of Middle School Science Writing Using Automated Techniques . International Society for the Learning Sciences Conference. Peddinti, V., & Qiu, R . (2022). A Machine Learning Approach to Understanding the Progression of Alzheimer’s Disease . In H. Yang, R. Qiu, & W. Chen (Eds.), AI and Analytics for Public Health (pp. 381–392). Springer International Publishing. Wang, J., Yang, H., Shao, R., Abdullah, S., & Sundar, S. S . (2020). Alexa as coach: Leveraging smart speakers to build social agents that reduce public speaking anxiety . Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI ‘20), Paper 434. Wilder, B., Onasch-Vera, L., Diguiseppi, G., Petering, R., Hill, C., Yadav, A ., Rice, E., & Tambe, M. (2021). Clinical Trial of an AI-Augmented Intervention for HIV Prevention in Youth Experiencing Homelessness . Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14948–14956. Xie, J., & Wang, Q . (2020). Benchmarking Machine Learning Algorithms on Blood Glucose Prediction for Type I Diabetes in Comparison With Classical Time-Series Models . IEEE Transactions on Biomedical Engineering, 67(11), 3101–3124. Xie, J., & Wang, Q . (2019). A personalized diet and exercise recommender system for type 1 diabetes self-management: An in silico study . Smart Health, 13, 100069. Yang, H., Qiu, R. G ., & Chen, W. (2022). AI and Analytics for Public Health: Proceedings of the 2020 INFORMS International Conference on Service Science . Springer Nature. You, Y., Kou, Y ., Ding, X., & Gui, X . (2021). The Medical Authority of AI: A Study of AI-enabled Consumer-Facing Health Technology . In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-16).
Articles
Zhang, L., Carter Jr, R. A., Liu, Y., & Peng, P. (2026). Let’s CHAT about artificial intelligence for students with disabilities: A systematic literature review and meta-analysis . Review of Educational Research , 96 (1), 215-257. Dumitru, C., Muttashar Abdulsahib, G., Ibrahim Khalaf, O., & Bennour, A. (2026). Integrating artificial intelligence in supporting students with disabilities in higher education: An integrative review . Technology and Disability , 38 (1), 3-24. Shin, M., Deniz, F., Watson, L., Dieterich, C., Ewoldt, K. B., Johnson, F., ... & Whitehurst, A. (2026). Addressing the void of AI policies in education for students with specific learning disabilities . Learning Disability Quarterly , 49 (3), 134-146. Liu, C., Meng, H., Zhu, H., & Rong, C. (2026). Does AI-assisted Learning Improve Students' Learning Outcomes? Evidence From A Meta-analysis . The Asia-Pacific Education Researcher , 1-13. Vuilliomenet, A., Jones, K. E., & Wilson, D. (2026). Future of Edge AI in biodiversity monitoring . arXiv preprint arXiv:2602.13496 . Fergus, P., Stephens, P., Hill, R. A., Oliver, L., Appleby, K., Beatham, S., ... & Hitchcock, K. (2026). Democratising Camera Trap AI: An Open-Source Model for Detecting UK Mammals . arXiv preprint arXiv:2606.10940 . El Jarroudi, M. (2026). Artificial intelligence for food security or digital inequality? Quantifying the global trade-offs between agricultural productivity, social inclusion, sustainable livelihoods, and responsible land stewardship . Frontiers in Sustainable Food Systems , 10 , 1916378. Song, P., Jiang, D., Zhou, J., Zhu, Y., Manaf, R. A., Bojude, D. A., ... & Rudan, I. (2026). Research priorities for data science and artificial intelligence in global health: an international consensus exercise . The Lancet Global Health , 14 (3), e455-e465. Liu, S., Liu, Z., Ng, O. L., Yang, Y., & Dai, Y. (2026). AI Literacy Interventions in Education: A Meta-Analysis of Effects and Moderators . Educational Research Review , 100813. Ebrahimian, A., Mansourian, M., Vahidi, S., & Alavi, F. (2026). The role of artificial intelligence in promoting kindness in society: A qualitative study to advance well-being, equity, and positive social change . Health Psychology Open , 13 , 20551029251413309. Li, S., Razi, U., Almugren, H., Zhang, L., & Galgotia, A. (2026). Exploring the impact of AI investments on waste management and mitigation from the Lens of sustainable circular economy . Technological Forecasting and Social Change , 224 , 124468. Lythreatis, S., Acikgoz, F., & Yassine, N. (2026). Artificial intelligence in humanitarian aid: A review and future research agenda . Technovation , 151 , 103415. Campbell, E., Oyefolu, O., Gillani, S., Goodtree, H., Kelly, A., Rivers, C., & Watson, C. (2026). Integrating AI Into Governmental Public Health Decision Making: Challenges, Considerations, and a Path Forward . JMIR Public Health and Surveillance , 12 (1), e88470. Bokhari, S. A. A., Park, S. Y., & Manzoor, S. (2026). The future of AI in climate change mitigation: a critical perspective . Big Earth Data , 1-9. Stein, S. (2026). AI for climate: from technological solutions to relational accountability . npj Climate Action , 5 (1), 47. Nabavi, E., Maier, H. R., Razavi, S., Hindes, A., Howden, M., Grant, W., & Raman, S. (2026). On using large language models to support social research for climate action . npj Climate Action , 5 (1), 52. Odone, A., Barbati, C., Amadasi, S., Schultz, T., & Resnik, D. B. (2026). Artificial intelligence and infectious diseases: an evidence-driven conceptual framework for research, public health, and clinical practice . The Lancet Infectious Diseases , 26 (3), e152-e167. Almasi, M., & Anahideh, H. (2026, March). Bi-Level Contextual Bandits for Individualized Resource Allocation under Delayed Feedback . In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 40, No. 45, pp. 38184-38193). Balachandar, S., Sadhuka, S., Berger, B., Pierson, E., & Garg, N. (2026, March). Urban incident prediction with graph neural networks: Integrating government ratings and crowdsourced reports . In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 40, No. 45, pp. 38194-38204). Wasi, A. T. (2025, March). AI for Social Good and Public Impact in Marginalized Communities: A Cross-Cultural Framework . In International Conference on Social Impact of AI (pp. 1-12). Cham: Springer Nature Switzerland. Bazzano, A. N., Mantsios, A., Mattei, N., Kosorok, M. R., & Culotta, A. (2025). AI Can Be a Powerful Social Innovation for Public Health if Community Engagement Is at the Core . Journal of medical Internet research , 27 , e68198. Li, X., & Mostafavi, A. (2025). Machine learning approach for disaster risk and resilience assessment in coupled human infrastructure systems performance . npj Natural Hazards , 2 (1), 56. Raghavendra, A. H., Majhi, S. G., Mukherjee, A., & Bala, P. K. (2025). Role of artificial intelligence (AI) in poverty alleviation: A bibliometric analysis . VINE Journal of Information and Knowledge Management Systems , 55 (3), 710-729. Abdurahman, S., Salkhordeh Ziabari, A., Moore, A. K., Bartels, D. M., & Dehghani, M. (2025). A primer for evaluating large language models in social-science research. Advances in Methods and Practices in Psychological Science , 8 (2), 25152459251325174. Tessler, M. H., Bakker, M. A., Jarrett, D., Sheahan, H., Chadwick, M. J., Koster, R., ... & Summerfield, C. (2024). AI can help humans find common ground in democratic deliberation. Science, 386 (6719), eadq2852. Sachdeva, B., Ramjee, P., Fulari, G., Murali, K., & Jain, M. (2024). Learnings from a Large-Scale Deployment of an LLM-Powered Expert-in-the-Loop Healthcare Chatbot. arXiv preprint arXiv:2409.10354. Falk, S., & van Wynsberghe, A. (2024). Challenging AI for Sustainability: what ought it mean? . AI and Ethics, 4 (4), 1345-1355. Orzikulova, A., Xiao, H., Li, Z., Yan, Y., Wang, Y., Shi, Y., ... & Xu, X. (2024, May). Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-20). Hwang, A. H. C., Adler, D., Friedenberg, M., & Yang, Q. (2024, May). Societal-Scale Human-AI Interaction Design? How Hospitals and Companies are Integrating Pervasive Sensing into Mental Healthcare. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-16). Kolla, M., Salunkhe, S., Chandrasekharan, E., & Saha, K. (2024, May). LLM-Mod: Can Large Language Models Assist Content Moderation? . In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (pp. 1-8). Goldberg, C. B., Adams, L., Blumenthal, D., Brennan, P. F., Brown, N., Butte, A. J., ... & Kohane, I. S. (2024). To do no harm—and the most good—with AI in health care. NEJM AI, 1 (3), AIp2400036. Peng, W., Lee, H. R., & Lim, S. (2024). Leveraging Chatbots to Combat Health Misinformation for Older Adults: Participatory Design Study. JMIR Formative Research, 8 (1), e60712. Wang, J., Qu, J. G., & Leo-Liu, J. (2023). An Algorithmically Woven Community: Disclosing Mental Illness and Connecting with Similar Others on an Algorithm-Mediated Platform in China. Social Media + Society, 9 (4). Lin, H., Karusala, N., Okolo, C. T., D'Ignazio, C., & Gajos, K. Z. (2024). "Come to us first": Centering Community Organizations in Artificial Intelligence for Social Good Partnerships. In Proceedings of the ACM Conference on Human-Computer Interaction, 8 (470). Ma, Z., Mei, Y., Long, Y., Su, Z., & Gajos, K. Z. (2024, May). Evaluating the Experience of LGBTQ+ People Using Large Language Model Based Chatbots for Mental Health Support. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-15). Mühl, L., Stecker, L., Herter, E., Szczuka, J. M., Wischnewski, M., & Krämer, N. (2024). Integrating AI in Psychotherapy: An Investigation of Trust in Voicebot Therapists. In Proceedings of the 13th Nordic Conference on Human-Computer Interaction (pp. 1-9). Costello, T. H., Pennycook, G., & Rand, D. G. (2024). Durably reducing conspiracy beliefs through dialogues with AI. Science, 385 (6714), eadq1814. Graham, S. S., & Hopkins, H. R. (2022). AI for Social Justice: New Methodological Horizons in Technical Communication . Technical Communication Quarterly, 31(1), 89–102. Vecchione, B., Levy, K., & Barocas, S. (2021). Algorithmic Auditing and Social Justice: Lessons from the History of Audit Studies. Equity and Access in Algorithms, Mechanisms, and Optimization, 1–9. Margetts, H. (2022). Rethinking AI for Good Governance. Daedalus , 151 (2), 360–371. von Richthofen, G., Siebold, N., & Gümüsay, A. A. (2022, February 23). The promises and perils of applying AI for social good in entrepreneurship [Online resource]. Vraga, E. K., Bode, L., & Tully, M. (2022). Creating News Literacy Messages to Enhance Expert Corrections of Misinformation on Twitter. Communication Research , 49 (2), 245–267. Züger, T., & Asghari, H. (2022). AI for the public. How public interest theory shifts the discourse on AI. AI & SOCIETY . Baum, S. D. (2020). Social choice ethics in artificial intelligence. AI & SOCIETY , 35 (1), 165–176. Bondi, E., Xu, L., Acosta-Navas, D., & Killian, J. A. (2021). Envisioning Communities: A Participatory Approach Towards AI for Social Good. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society , 425–436. Cowls, J., King, T., Taddeo, M., & Floridi, L. (2019). Designing AI for Social Good: Seven Essential Factors Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2021). A definition, benchmark and database of AI for social good initiatives. Nature Machine Intelligence , 3 (2), 111–115. Eliot, D. L. B. (n.d.). The Neglected Dualism Of Artificial Moral Agency And Artificial Legal Reasoning In AI For Social Good . 4. Floridi, L. (2019). Establishing the rules for building trustworthy AI. Nature Machine Intelligence , 1 (6), 261–262. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2021). An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. In L. Floridi (Ed.), Ethics, Governance, and Policies in Artificial Intelligence (pp. 19–39). Springer International Publishing. Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2020). How to Design AI for Social Good: Seven Essential Factors. Science and Engineering Ethics , 26 (3), 1771–1796. Hager, G. D., Drobnis, A., Fang, F., Ghani, R., Greenwald, A., Lyons, T., Parkes, D. C., Schultz, J., Saria, S., Smith, S. F., & Tambe, M. (2019). Artificial Intelligence for Social Good Kshirsagar, M., Robinson, C., Yang, S., Gholami, S., Klyuzhin, I., Mukherjee, S., Nasir, M., Ortiz, A., Oviedo, F., Tanner, D., Trivedi, A., Xu, Y., Zhong, M., Dilkina, B., Dodhia, R., & Lavista Ferres, J. M. (2021). Becoming Good at AI for Good . Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society , 664–673. Li, V. O. K., Lam, J. C. K., & Cui, J. (2021). AI for Social Good: AI and Big Data Approaches for Environmental Decision-Making. Environmental Science & Policy , 125 , 241–246. Madianou, M. (2021). Nonhuman humanitarianism: When “AI for good” can be harmful. Information, Communication & Society , 24 (6), 850–868. Mccarthy, S. (2018, November 1). Google pledges $25 million to AI for social good. University Wire . Moore, J. (2019). AI for Not Bad. Frontiers in Big Data , 2 . Selwyn, N., Cordoba, B. G., Andrejevic, M., & Campbell, L. (2020). AI For Social Good: Australian Public Attitudes Toward AI and Society . Shi, Z. R., Wang, C., & Fang, F. (2020). Artificial Intelligence for Social Good: A Survey Slota, S. C., Fleischmann, K. R., Greenberg, S., Verma, N., Cummings, B., Li, L., & Shenefiel, C. (2020). Good systems, bad data?: Interpretations of AI hype and failures. Proceedings of the Association for Information Science and Technology , 57 (1), e275. Taddeo, M., & Floridi, L. (2018). How AI can be a force for good. Science , 361 (6404), 751–752. Theodorou, A., & Virginia, D. (2020). Towards ethical and socio-legal governance in AI. Nature Machine Intelligence , 2 (1), 10–12. Tomašev, N., Cornebise, J., Hutter, F., Mohamed, S., Picciariello, A., Connelly, B., Belgrave, D. C. M., Ezer, D., Haert, F. C. van der, Mugisha, F., Abila, G., Arai, H., Almiraat, H., Proskurnia, J., Snyder, K., Otake-Matsuura, M., Othman, M., Glasmachers, T., Wever, W. de, … Clopath, C. (2020). AI for social good: Unlocking the opportunity for positive impact. Nature Communications , 11 (1), 2468. Wearn, O. R., Freeman, R., & Jacoby, D. M. P. (2019). Responsible AI for conservation. Nature Machine Intelligence , 1 (2), 72–73. Wilder, B., Onasch-Vera, L., Diguiseppi, G., Petering, R., Hill, C., Yadav, A., Rice, E., & Tambe, M. (2021). Clinical Trial of an AI-Augmented Intervention for HIV Prevention in Youth Experiencing Homelessness . Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14948–14956.
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