  This paper investigates the prospects of using directive explanations to
assist people in achieving recourse of machine learning decisions. Directive
explanations list which specific actions an individual needs to take to achieve
their desired outcome. If a machine learning model makes a decision that is
detrimental to an individual (e.g. denying a loan application), then it needs
to both explain why it made that decision and also explain how the individual
could obtain their desired outcome (if possible). At present, this is often
done using counterfactual explanations, but such explanations generally do not
tell individuals how to act. We assert that counterfactual explanations can be
improved by explicitly providing people with actions they could use to achieve
their desired goal. This paper makes two contributions. First, we present the
results of an online study investigating people's perception of directive
explanations. Second, we propose a conceptual model to generate such
explanations. Our online study showed a significant preference for directive
explanations ($p<0.001$). However, the participants' preferred explanation type
was affected by multiple factors, such as individual preferences, social
factors, and the feasibility of the directives. Our findings highlight the need
for a human-centred and context-specific approach for creating directive
explanations.
