第1章コンピューターサイエンス入門

Computer science can advance social good by benefiting many people in many different areas, including public health, agricultural sustainability, climate sustainability, and education.

Computer technologies accelerate medical treatments for public and personal health from initial research and development to clinical trials to large-scale production and distribution. In January 2020, Chinese officials posted the genetic sequence of the coronavirus SARS-CoV-2. This helped pharmaceutical companies to begin developing potential vaccines for the virus at a significantly faster rate than for any other virus in the past (Figure 1.11).

Computational science enables the miracles of modern medicine. Viral sequences can be digitized and rapidly shared between researchers across the world via the Internet. Computer algorithms and models can simulate the human immune system responses to particular treatments within hours rather than years. The first treatments can then be produced at a small scale using computer-engineered cells in less than a month from the initial sequencing. To ensure the treatments are safe and effective, clinical trials are held at disease transmission “hot spots” predicted using data science methods drawing on data aggregated and monitored from across the world. Once a treatment is proven safe and effective, it is mass-produced with the help of computer-controlled robots and automated assembly lines. Algorithms manage the inventory supply and demand and control the transportation of treatments on trucks and planes guided by computer navigation systems. Web apps and services notify people throughout the process.

Yet the use of computer technology throughout modern medicine is anything but politically neutral. Computers, algorithms, and mathematical models solve the problems that their creators wish to solve and encode the assumptions of their target populations. Supply and demand data for the data models are determined by various factors, at least partly in response to the money and relationships between countries that control the technology, the Global North, and countries that don’t, the Global South. Within local communities, the uptake of medical treatments is often inequitable, reflecting and reinforcing historical inequities and disparities in public health. Computer technology alone often doesn’t address these issues. In fact, without people thinking about these issues, computer technologies can often amplify disparities. Consider datasets, which can be biased if they overrepresent or underrepresent specific groups of people. If decisions are made on the basis of biased data, people in the groups that are not represented fairly may receive inequitable treatment. For example, if a local government agency is working with a biased dataset, political leaders may make decisions that result in certain citizens receiving inadequate funding or services. This is an example of why responsible computing, which we will cover in Chapter 14 Cyber Resources Qualities and Cyber Computing Governance, is so important.

These problematic histories are not only aggravated in medicine and public health, but also reflected in housing. Redlining refers to the inequitable access to basic public services based on residents’ neighborhoods and communities, which includes the practice of withholding financial services from areas with a large underrepresented population. In the United States, these communities reflect the histories of racial segregation and racial wealth inequalities. Fair housing laws are intended to prevent property owners from discriminating against buyers or renters because of race, color, ability, national origin, and other protected classes. But computer technologies also present new kinds of challenges. Microtargeted ads on social media platforms contribute to not only political polarization, but also discrimination in housing. This can be a particular problem when combined with redlining. Even if the ad targeting is not explicitly designed to discriminate, microtargeted ads can still reinforce historical redlining by incorporating data such as zip codes or neighborhoods. This may result in digital redlining, which is the practice of using technology, such as targeted ads, to promote discrimination. In 2021, a Facebook user filed a class-action lawsuit that argued nine companies in the Washington, D.C., area deliberately excluded people over the age of 50 from seeing their advertisements for housing because they wanted to attract younger people to live in their apartments.¹⁶ This is an example of an issue in technology that should be addressed by responsible computing with an emphasis on ethical behavior.

With good intentions and attention to personal biases, technologies can be designed for social good. For example, a hypothetical algorithm for fair housing could evenly distribute new housing to people across protected classes and marginalized identities, such as older populations. Of course, algorithmic control and automated decision-making is challenged to consider the underlying conditions behind social problems. Still, algorithms can be important tools to enable us to distribute outcomes more fairly from a statistical perspective, and this can be an important step in addressing the larger societal systems and inequities that produce social problems.