The Fairness Project: equipping students to maximise their employability through understanding and responding to inequality and unfair diversity

The Fairness Project is a collaborative project across three Universities equipping students to maximise their employability through understanding and responding to inequality and unfair diversity barriers in the legal profession and inspiring them to create a fairer legal profession for the future. This three-year project was initially developed in 2015/16 by UCLan in response to the UK relying on the legal professions to “heal themselves” in improving equality and diversity. This approach has faltered, in part due to the global economic climate, and stimulus is needed to make faster equality progress. Recent empirical evidence has identified specific diversity barriers, namely: the cost of training; the use of high school grades as a primary selection criteria; the significance of prior work experience in recruitment decisions; and recruitment from elite universities. Students from majority groups and affluent backgrounds are structurally advantaged over their counterparts. The project seeks to empower students to understand and thus to resist these phenomena. We aim to work with our students across three quite different institutions to raise awareness of structural inequalities and career hurdles; to help students develop personal strategies to overcome these; and thereby maximise their employability. Students are encouraged to address their own biases, both conscious and implicit, to enable them to become fairer employers and managers in the future, and thus to change the culture of the legal profession from within. The paper demonstrates the methods used in the Fairness Project; discusses their strengths and limitations; and reports on our comparative project findings to date. These are important for legal education, but also in other disciplines and professions beyond law. Broader issues for discussion include the implications for the metrication of employability data, including in the TEF.