The 6th Muslims in ML (MusIML) Workshop
The 6th Muslims in ML (MusIML) Workshop at ICML 2026 aims to strengthen the visibility, participation, and long-term impact of Muslim researchers, students, engineers, and institutions in the global AI/ML community.
MusIML provides a community-centered forum for machine learning research by Muslim authors, as well as research addressing challenges relevant to Muslim communities and Muslim-majority regions. The workshop welcomes all ICML attendees interested in building stronger connections between the global AI community and the Muslim world.
The program will include invited talks, accepted paper posters, selected lightning talks, mentorship and networking activities, and community discussions. Topics represented in the workshop include modern AI/ML, multilingual and low-resource language technologies, AI for education and healthcare, responsible and trustworthy AI, human-centered AI, and applications that broaden participation in global AI research.
All submitted papers were reviewed through a double-blind review process. Accepted papers will be presented as posters, with selected papers invited for lightning talks. The workshop is non-archival and is intended to support research exchange, mentorship, collaboration, and long-term community-building within and beyond ICML.
GlobalSouthML @ ICML 2026
This workshop aims to advance Machine Learning (ML) research and practice in developing economies by fostering collaboration among organizers, speakers, and participants from Asia, Africa, and beyond, spanning academia and industry. It addresses how constraints in data, computation, and infrastructure reshape ML design and deployment. Topics include deployable ML under resource constraints, public health and social impact, learning under data scarcity, and ML for emerging industries. By positioning developing economies as sources of novel methodological challenges, the workshop promotes inclusive participation and highlights globally impactful, deployment-aware ML research within the ICML community.
From Behavioural Guardrails to Principled Agency
How can agents make safe autonomous decisions in complex, dynamic environments? While significant progress has been made in establishing post-training guardrails to enforce conversational compliance in generative models, these rigid constraints often prove brittle in open-world environments. I argue that achieving generalizable agentic safety requires Normative Alignment: a new paradigm that moves beyond passive harm avoidance to equip autonomous systems with Agentic Integrity. This approach provides agents with the structural capability to interpret, reason through, and dynamically apply abstract principles when literal instructions fail. Realizing this paradigm presents a triple challenge of capability, measurement, and governance. First, it requires a shift in model capability toward normative competence beyond generic reward maximization, moving toward the contextual reasoning needed to adjudicate complex trade-offs in non-verifiable domains. Second, it demands new metrics that move optimization targets beyond immediate preference satisfaction toward long-term human well-being. Third, it requires deliberative governance to ensure these systems avoid top-down paternalism by grounding alignment targets in pluralistic, representative societal input.
What will be left for us to work on?
Given rapid advances in AI, how should researchers and developers shift how we allocate our time? What new skills should we build so that we’re not obsolete in the future? I argue that there will be plenty for us to work on, grounded in the “AI as normal technology” thesis, which holds that there are many bottlenecks between AI capability improvements and automation of tasks or jobs. The evidence suggests that AI is better seen as an augmentation than an automation technology. The balance of human effort will shift towards tasks that are less verifiable — from developing models to scaffolds, and from building towards evaluation and monitoring. Over the long term, as purely technical skills are devalued, both researchers and developers will have to adapt. In research, human effort will migrate from problem solving to question asking and conceptual progress; in industry, relational skills, domain knowledge, aesthetic and normative judgment will gain in importance.
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