For developing human-centred trustworthy autonomous systems and ensuring their safe and effective integration with the society, it is crucial to enrich autonomous agents with the capacity to represent and reason about their accountability. This is, on one hand, about their accountability as collaborative teams and, on the other hand, their individual degree of accountability in a team. In this context, accountability is understood as being responsible for failing to deliver a task that a team was allocated and able to fulfil. To that end, the semantic (strategic reasoning) machinery of the Alternating-time Temporal Logic (ATL) is a natural modelling approach as it captures the temporal, strategic, and coalitional dynamics of the notion of accountability. This allows focusing on the main problem on: “Who is accountable for an unfulfilled task in multiagent teams: when, why, and to what extent?” We apply ATL-based semantics to define accountability in multiagent teams and develop a fair and computationally feasible procedure for ascribing a degree of accountability to involved agents in accountable teams. Our main results are on decidability, fairness properties, and computational complexity of the presented accountability ascription methods in multiagent teams.