Legal and ethical issues in social capital analysis
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Slides developed as part of DEVELOP project, focusing on political and sociological analysis of social capital, by David Barnard-Wills.
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Legal and ethical issues in 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
- 2. Overview The general politics & sociology of social capital Issues arising from the social capital analysis Issues arising from the results of the analysis The question of external social media data Recommendations 2Funded by the EU (Horizon 2020 Programme) | © DEVELOP – Developing Careers through Social Networks and Transversal Competencies
- 3. The politics & sociology of social capital Social capital is complex A metaphor Not necessarily a property of any given individual (but of a network/culture/context). Workplace social capital is already riddled with political/ethical issues before we do any analysis . For example: organizational mechanisms can channel women and racial/ethnic minority men into less prestigious tracks with resulting intra-organizational job segregation (Collins 1997; DiPrete and Soule 1988). Differential access to training can resulting in the "de-skilling" of individuals in these groups (Knoke & Ishio 1998; Mueller, Parcel, and Tanaka 1989; Nkomo and Cox 1990), resulting in truncated opportunities for mobility. Informal processes can exclude these groups from key networks and from mentoring relationships (Baldi and McBrier 1997; Kanter 1977), relegating them as outsiders to organisational culture. 3Funded by the EU (Horizon 2020 Programme) | © DEVELOP – Developing Careers through Social Networks and Transversal Competencies
- 4. The politics & sociology of social capital Men and women, and people of different ethnicities: Experience work networks, both social and professional in different ways, Tend to have different work networks Face different challenges in building and using those networks, and gain differential benefits from the networks The idea that there is a “best/perfect/ideal/required” social network or social capital for a particular job role is therefore likely to be discriminatory. 4Funded by the EU (Horizon 2020 Programme) | © DEVELOP – Developing Careers through Social Networks and Transversal Competencies
- 5. Then some issues arise from the analysis... Accuracy and validity Do social networks capture social capital? What types of capital are excluded from the analysis? How comprehensive is our initial data set, and is discrimination already “baked in”? (e.g all senior staff are male and have “male” social networks) Privacy and data protection concerns SNA is re-use of data provided for another purpose SNA includes the analysis of data that may not be transparent to the users (this being the point) Therefore consent for the analysis should be separate from consent to use the social network Particularly if its a business-critical network, - no opportunity not to use. Consent is individual, network is not How to communicate the SNA to participants? Gaming social networks for social capital, & unwanted changes to behaviour 5Funded by the EU (Horizon 2020 Programme) | © DEVELOP – Developing Careers through Social Networks and Transversal Competencies
- 6. Then some issues arise from the results of the analysis... Harm to standing Management decisions (jobs/personnel changes ) on the basis of social network analysis E.g. From this social network graph, it looks like you are a bottleneck Psychological harm Negative reflections Further privacy risks How are analysis results accessible? (to user, to manager? To HR?) Risk of Re-identification, -e.g. through location in a network, and commonly known demographics 6Funded by the EU (Horizon 2020 Programme) | © DEVELOP – Developing Careers through Social Networks and Transversal Competencies
- 7. Non-workplace social networks? Should the DEVELOP tool draw upon non-workplace (personal) social networks to inform social capital analysis? Motivation – “personal social networks have more information about individuals’ social capital. Which might be relevant to their career development. Some might be highly relevant (e.g. LinkedIn connections with peers in industry). PIA report D4.2 argues no. Employment contracts and policies + proprietary work social networks provided for work purposes simplifies many data protection concerns. People use social networks in vastly multiple ways, likely highly non-comparable. Users would have to trust employers with access to social media accounts. Increases risk of workplace surveillance. complexityIncreases risk of users not understanding privacy risks. 7Funded by the EU (Horizon 2020 Programme) | © DEVELOP – Developing Careers through Social Networks and Transversal Competencies
- 8. Suggestions Analytical strategies should expect differences in social network/capital opportunities to counter-act gender and ethnicity-based "channeling" and use more-organisationally relevant characteristics to: guide career choice; monitor access to training and ensure that it is egalitarian; expand access to mentorship Consent process – Borgatti and Molina propose to expand the standard consent form (which binds researcher and respondent) to include the organization’s management as well, committing them not to use the resulting data to evaluate individual performance. Provide clarity on the how SN data will be collected and analysed (visualisation?) Interpreting and communicating the output Social capital analysis is a tool for the user to reflect upon their career, development NOT a management metric. 8Funded by the EU (Horizon 2020 Programme) | © DEVELOP – Developing Careers through Social Networks and Transversal Competencies
- 9. Resources Stephen Borgatti & Jose-Luis Molina,, Towards ethical guidelines for network research in organisations, http://www.analytictech.com/borgatti/papers/ethics2.pdf Solon Barocas & Andrew Selbst, Big Data’s disparate impact. California Law Review, 2016 Pauline T. Kim, Data driven discrimination at work, William and Mary Law Review, 2017. Cynthia Dwork & Deirdre K. Mulligan, It’s not Privacy, and iIts Not Fair, 2013. https://www.stanfordlawreview.org/online/privacy-and-big-data-its-not-privacy-and-its-not- fair/ O'Neil, C. (2016) Weapons of Math Destruction: How big data increases inequality and threatens democracy. St Ives: Allen Lane. Frank Pasquale (2015) Black Box Society: The secret algorithms that control money and information. Harvard University Press. Gandy. O. (2009) Coming to Terms with Chance: Engaging Rational Discrimination and Cumulative Disadvantage. Oxford: Routledge 9Funded by the EU (Horizon 2020 Programme) | © DEVELOP – Developing Careers through Social Networks and Transversal Competencies
Editor's Notes
- 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.