Under United States securities laws corporations must disclose material risks to their operations. Human rights issues, especially in authoritarian countries, rarely show up in the information that data providers offer to investors, in part due to the risks to those subject to these abuses. The result is a dearth of data on human rights materiality and the tendency of investors to overlook human rights risks of the companies that they finance.

During the period 2011-2015, the nonprofit Sustainability Accounting Standards Board (SASB), an initiative supported by the world’s largest institutional investors, examined the impact of human rights on corporate performance as part of an effort to assess the materiality of “social and environmental” issues. Issues that turned up solid relationships to company performance were incorporated into the sustainability disclosure standards that were published in 2018. At the time, big data analytics were in their infancy and few strong correlations between financial loss and human rights risks were found. In addition, in the time since the standards were being developed new concerns about what constitutes a corporation’s responsibility to human rights have been brought to the public’s attention. For example, the positions of social media companies on content moderation is highly topical, however not strictly deemed material for the purposes of sustainability.

These standards for what counts as a financially material disclosure will be updated in 2021. It is important that a wider and more sophisticated view of how human rights outcomes are associated with changes in company performance be derived. This project’s aim is to improve upon the SASB human rights sustainability standards by first replicating the 2011-2015 analysis with modern data and then bring it up-to-date with modern machine learning methods. There is the potential for future access to private corporate news tracking data services which can be paired with social media data using natural language processing. This work promises to have a significant positive impact on the individuals and communities who are at risk of harm from business operations.

The Data For Good program is designed primarily for volunteers, however one candidate will be selected as a project coordinator and will receive a stipend via the Data For Good Scholars program. In addition to the responsibilities of a team member, the selected candidate will be responsible for keeping up-to-date notes on the project’s status, writing an end-of-period report, and attending bi-weekly meetings with a DFG program director. The project coordinator should strive to keep the group of volunteers in sync with the needs of the project owner.

Project Owners

  • Joanne Bauer, Rights CoLab and School of International and Public Affairs. Joanne Bauer teaches Corporations and Human Rights as well as the year-long Business and Human Rights Clinic at SIPA. She is Senior Researcher for the Business and Human Rights Program, at Columbia’s Center for the Study of Human Rights and co-leads an international initiative on Teaching Business and Human Rights based at Columbia. In October 2018 she launched Rights CoLab, an independent initiative that develops strategies to advance human rights through business, technology and finance.

  • Paul Rissman. Paul helped found Rights CoLab in 2018. He has served on various non-profit and for-profit boards, including the Archaeological Institute of America, and he was chair of Cinetic Rights Management, a digital media distributor. Prior to his retirement in 2008, he was Executive Vice President of AllianceBernstein L.P. and Chief Investment Officer of Alliance Growth Equities. Paul has also conducted scholarly research on sustainability reporting. He is an Open Society Fellow.

Project timeline

  • Earliest starting date: 10/01/2019
  • End date: 12/20/2019
  • Number of hours per week of research expected during Spring 2019: ~6-8, ~10 for coordinator
  • Project is ongoing and will be reviewed for future directions at the end of the semester

Candidate requirements

  • Skill sets: familiarity with machine learning concepts; natural language processing experience is a plus; ability to program in a language like R or Python that has a robust data analytic toolset is required.
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • For coordinator position, international students on F1 or J1 visa: eligible