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Legal and Ethical Issues of Social Capital Analysis
1. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688127
Legal and Ethical issues in Social Capital Analysis
Dr. David Barnard-Wills, david.barnard-wills@trilateralresearch.com
Senior Research Analyst
Trilateral Research
www.trilateralresearch.com
@trilateral_UK, @dbarnardwills
Developing Careers through Social Networks and Transversal Competencies
Gaughan & Buzeman (2016) found both similarities and differences in the collaborative activity of men and women (in this case in the field of academic science). Some of these they related to power dynamics, including gender, but also status dynamics.
A study of 1000 employees in the financial services industry found that even women with substantially developed work networks received less work-related assistance than their male peers. (McGuire 2002).
A third study found that (in the project-based labour market of the US film industry) women and men received differing benefits from different types of work-related social networks. In that context men received greater benefits from cohesive, homogenous teams, whilst women received greater benefits from more open networks with high diversity and high rates of information flow (Lutter 2015).
This has been described by Burt (1998) as the "gender puzzle" in which the entrepreneurial networks linked to early promotion for senior men do not work for women.
The studies on workplace social capital have several implications for DEVELOP. From a positive perspective, DEVELOP offers opportunities to take measures to counter-act gender and ethnicity-based "channeling" and use more-organisationally relevant characteristics to guide career choice; to monitor access to training and ensure that it is egalitarian; and to expand access to mentorship.
However, this is not to advocate a "gender- or ethnicity-blind" approach within DEVELOP - DEVELOP should expect that people of different genders, ethnicities and likely economic status (class) are likely to have different social networks, in turns of shape, composition and character. Secondly, different employees will receive different benefits from their social capital, as mediated through their gendered or racial identities. Thirdly, the strategies required to expand or develop social capital will also be mediated by gender, ethnicity and class (Cullen-Lester et al 2016 present a framework which may support such strategies).
This means DEVELOP should be very careful when assessing social networks of individuals and especially when comparing between individuals. It should also exercise caution when recommending any action to employees about the sort of social capital they should be developing. This relates to the fundamental question of what the system will do with any insights generated from SNA of social capital.
The outputs from SNA and AI planning should both encourage self-reflection on the part of the learner when provided with additional information, rather than be presented as (pseduo)objective metrics. The literature and exploitation efforts around these solutions should reflect this. Any HR or management use of the tool should be adopt a similar position.
The project should avoid using concepts such as "required social capital for a particular role", understanding that social capital is often not commensurable between individuals.
If social network data are used as training data for a machine-learning based career planning tool, then is important that this is done with a consideration of the ways in which the data may already include implicit bias.