Salem State Targets AI Accountability Gaps with Leadership Lab
Artificial intelligence is accelerating academic and professional workloads. This speed frequently introduces unclear accountability and blurred decision ownership.
To address these growing organizational challenges, a session titled AI Without Chaos: A Leadership Lab for Decision Rights, Risk, and Accountability is being hosted. The workshop approaches artificial intelligence adoption primarily as a leadership and decision-making challenge rather than a purely technological choice.
Yuval Gonen Guides Session on Operational Risk
Participants examine how automated tools alter the pace of work, shift human roles, and test the limits of responsibility.
According to session materials, deploying new software without clear frameworks often increases operational risk, redundant work, and leadership escalation. Yuval Gonen leads the session by sharing a practical framework designed to guide responsible decision-making around artificial intelligence, drawing on real-world experience working with senior executives.
The Green, Yellow, and Red Risk Model
Attendees learn to categorize tasks by determining whether AI should support, inform, recommend, or never handle.
The curriculum incorporates a straightforward green, yellow, and red risk model to clarify review processes, approval chains, and ultimate ownership of AI-assisted outputs.
Target Audience and Cross-Sector Governance
The program targets leaders, educators, and students working through governance, organizational design, and the human dimensions of automation.
Specifically, the curriculum is structured for business leaders and executive teams overseeing strategy and operations, as well as professionals in operations, human resources, finance, and customer experience. Educators, workforce development specialists, public sector leaders, and nonprofit leaders seeking practical governance approaches also form the core audience.
Hybrid Delivery Awards CPE Credits and Certificate
Delivered in a hybrid format, the session awards 4 CPEs credits alongside an SSU certificate upon successful completion.
No prior technical background in artificial intelligence is required for attendance.
