Data science for social justice is a present-day concept that is gaining more recognition and popularity in the world. Solving current social problems using available open-source data and data science skillsets will help many world populations and is an ongoing challenge. The article reviews an old case study about Cochabamba, Bolivia’s problem with water infrastructure privatization, and what happened in the year 2000. While the data science profession, open data sources, and current technologies were not around in 2000, this article is a speculative assessment on how a data scientist from two sides may retrospectively go about the work today given the skills and technologies available.
In one scenario, there is the data scientist team working with the Cochabamba people on the water project. The problem is the people need the existing water infrastructure fixed, and water access for everyone is essential. This problem is a challenge because of incomplete data, conflicting information from the government, and the actual requirements of the water infrastructure to provide 800,000 people with clean water. The data scientist should do a thorough investigation into the data resources and open dialog with all the parties involved. In 2000, the county would not have open data like today so that extensive surveys would build the necessary information. The ethical issues are including all social groups in the city from the more impoverished indigenous people to the city dwellers of all economic status in the discussion and gathering enough data from all populations. Care must be taken not to put anyone group of citizens at a further disadvantage.
In a second scenario is the data science team in the Bechtel corporation who installed the water infrastructure and managed it. It was decided by the lending company, International Monetary Fund (IMF), that the Bolivian government must sell this utility to Bechtel to receive funding and future backing as a condition of the loans given to Bolivia. IMF specifically said private corporations like Bechtel are motivated by profits and, as a result, will operate the water infrastructure; however, they are also highly qualified to run the water infrastructure for the Bolivian government (Rathburn and Baum, 2009, p.3). The data science team at Bechtel may be working on a problem related to the construction of the water infrastructure that is lowest in cost with a high-profit margin, and the second problem is the operations management of the water infrastructure and operating the facilities also with high margin. The data scientist would need to find data relating to system demand, both current and future demand, and use that data to build construction and maintenance models with the goal of efficient operations and maximizing profit margins. The ethical issue could be not using all the data that is available and focused too much on the bottom line without checking if the market can handle the costs and projected billing rates. An engineering company may not even know to look at the socio-economic variables in Bolivia. Their goal is to deliver the contracted work to the Bolivian government and benefitting corporate stakeholders.
In another hypothetical situation as a part of the data science team at Bechtel, the purpose is to identify what outcomes the team would want from the data science life cycle. The Bechtel Corporation is an engineering, construction, and project management firm in many different markets (Bechtel, 2019a). Bechtel is a different company today than twenty years ago when this case study happened. The company has strong ethics and compliance standards and does work throughout the world. Bechtel still does work in South America and has offices in neighboring Chile and Brazil. Bechtel has since committed to human rights and outlines engagement with communities on potential project risks and impacts, among other policies (Bechtel, 2019b). As a data scientist with Bechtel today, one would be bound by these standards to do their best work ethically and in compliance with all regulations within specific jurisdictions. The desired outcome would be to produce a water infrastructure system that will benefit the people of Bolivia and operated sustainably.
If a 2019 study was done to provide Bolivia with water infrastructure the data scientist, in addition to the engineering team, would do an analysis on the cost to operate, the cost to the Bolivian government, and the expense incurred by the Bolivian people, also a projected cost analysis, similar to how it is done in the U.S. when bidding on projects. Thirty-three percent of a Bolivian’s average income of $80 month goes to the water utility alone in the case study. The citizens had previously paid seventeen percent of the average monthly wages on the water utility alone, which is still high in today’s standards. Water costs may be acceptable in a very arid environment; however, Bolivia has adequate natural water resources, possibly there are savings for the people if a more efficient water delivery system becomes implemented. Ethically the data for the feasibility study should be included in the report provided to the Bolivian government in the preliminary stages of design and proposal generation. Reporting unethical practices is a possibility within Bechtel with data misrepresented unintentionally or intentionally. Intentionally misrepresenting the data can lead to the loss of a job at Bechtel.
Obtaining optimal data is always the best practice in engineering and construction. The proper data to acquire in this project is socio-economic, historical economic and projected economic data, trends in development within the city, regional mapping GIS data for geographic data, and environmental and sustainability-related data. Ethically some of this data has social justice implications, and the data scientist should be made aware of this and work through the data for the benefit of all parties in Bolivia.
The Cochabamba city had an aging water infrastructure, and the government was obligated by the International Monetary Fund to privatize the water utility. Bechtel Corporation, working with Agus del Tunari, constructed and managed the operations of the water infrastructure. The company was given the authority to profit on the management of the system; however, at the time, no regulations or ethical policies were governing international business like there are in 2019. Every engineering, construction, and project management firm today has implicit ethical standards and procedures as well as some social justice statements. The case study is old, but an excellent example of where the corporate business came from and lessons learned to act more internationally today. Data science didn’t exist during this case study, so the paper hypothetically discussed what should be done to do this project ethically with today’s standards. In only recent years has the engineering, construction, and project management corporations started using data science and are still at the infancy stages because traditionally, most of the work was done by business development and engineers in the corporations. Data science for social justice is also new, and the data scientists are taking on challenges that are difficult due to a lack of resources and available data. Many non-profits don’t know what it is they need until a data scientist with experience can show them the possibilities of what data can do for the non-profit’s cause. One item that wasn’t addressed in this paper is the International Monetary Fund. IMF specifically stated in the case study when they give loans to nations in financial trouble it is a policy that the privatization of the utility is a condition of the loan agreement and corporations that take the privatization role are skilled at managing and motivated by profit to keep the utility functional (Rathburn and Baum, 2009, p.3). It is curious that the IMF wasn’t singled out in this case, as it seems there were other stakeholders involved in addition to the Bechtel Corporation.
References
Bechtel. (2019a). About Bechtel. Retrieved from: https://www.bechtel.com/about-us/
Bechtel. (2019b). Human rights. Retrieved from: https://www.bechtel.com/about-us/ethics-compliance/human-rights/
Rathburn, K. , Baum, K. (2009). The wealth of water. Retrieved from: http://sciencecases.lib.buffalo.edu/cs/collection/detail.asp?case_id=219&id=219
The Democracy Center. (n.d.). Bechtel vs Bolivia: Details of the case and the campaign. Retrieved from: https://democracyctr.org/archive/the-water-revolt/bechtel-vs-bolivia-details-of-the-case-and-the-campaign/
Waterrisingfilm. (February 4, 2015). Water rising – Full documentary. Retrieved from: https://www.youtube.com/watch?v=LAR8eVqwUpw