INTEGRATING RESPONSIBLE AI IN ABUSE HELPLINE CONTACT CENTERS: A FRAMEWORK FOR ETHICAL IMPLEMENTATION AND HUMAN-CENTERED SUPPORT
Keywords:
Responsible Artificial Intelligence, Crisis Support Services, Human-AI Collaboration, Digital Ethics, Contact Center TechnologyAbstract
This article examines the integration of Artificial Intelligence (AI) technologies in abuse helpline contact centers, presenting a comprehensive framework for ethical implementation that prioritizes technological innovation and human-centered support. The article identifies critical considerations in AI deployment, analyzing existing literature and operational case studies, including privacy protection, bias mitigation, and emotional intelligence integration. The article establishes a novel human-AI collaboration model that emphasizes AI as a supportive tool rather than a replacement for human operators, incorporating real-time assistance capabilities while maintaining essential human oversight. Our article demonstrates that successful implementation requires a carefully balanced approach incorporating four key elements: (1) robust ethical guidelines for data handling and decision-making transparency, (2) clear protocols for AI-human collaboration that preserve the primacy of human empathy, (3) comprehensive safety and accountability measures, and (4) adaptable frameworks for emotional intelligence integration. The article concludes with practical recommendations for implementation, addressing regulatory compliance, training requirements, and quality assurance measures. These findings contribute to the growing body of literature on responsible AI implementation in sensitive support services while providing actionable insights for organizations seeking to enhance their abuse helpline operations through technological innovation without compromising the human-centered nature of crisis support.
References
T. Hagendorff, "The Ethics of AI Ethics: An Evaluation of Guidelines," Minds & Machines, vol. 30, pp. 99-120, 2020. https://link.springer.com/article/10.1007/s11023-020-09517-8
D. Følstad and P. B. Brandtzaeg, "Chatbots and the New World of HCI," Interactions, vol. 24, no. 4, pp. 38-42, 2017. https://dl.acm.org/doi/10.1145/3085558
Q. V. Liao, et al., "All Work and No Play? Conversations with a Question-and-Answer Chatbot in the Wild," Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Paper 3, pp. 1-13, 2018. https://dl.acm.org/doi/10.1145/3173574.3173577
M. Mitchell et al., "Model Cards for Model Reporting," Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 220-229, 2019. https://dl.acm.org/doi/10.1145/3287560.3287596
S. Amershi et al., "Guidelines for Human-AI Interaction," Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19), Paper 3, pp. 1–13, 2019. https://dl.acm.org/doi/10.1145/3290605.3300233
C. Rudin et al., "The Age of Secrecy and Unfairness in Recidivism Prediction," Harvard Data Science Review, 2020. https://hdsr.mitpress.mit.edu/pub/7z10o269
"FAccT '23: 2023 ACM Conference on Fairness, Accountability, and Transparency," Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, pp. 1-12, 2023. https://dl.acm.org/doi/proceedings/10.1145/3593013
Picard, R. W., "Affective Computing: From laughter to IEEE," IEEE Transactions on Affective Computing, vol. 1, no. 1, pp. 11-17, 2010.https://ieeexplore.ieee.org/document/5565330
L. Floridi and J. Cowls, "A Unified Framework of Five Principles for AI in Society," Harvard Data Science Review, vol. 1, no. 1, 2019. https://hdsr.mitpress.mit.edu/pub/l0jsh9d1
"The ACM Code of Ethics and Professional Conduct," Communications of the ACM, Version 3.0, 2018. https://www.acm.org/code-of-ethics
