We increasingly place our trust in algorithms, whether it’s applying for a home loan, a new job; or make personal health decisions. But what about the security system that uses facial recognition and excludes a 55-year-old office guard from her night shift? Or groups of people automatically deleted photos on social networks? These are the unintended, and often unfair, consequences of amplified data science tools on millions of users. They are also highly preventable.
This is the lesson that lawyer and epidemiologist M. Elizabeth Karns incorporates into every data science and statistics course she teaches in the Department of Statistics and Data Science. His students will decide how to use data in the future, and while bad decision-making in business is nothing new, Karns says it’s the accelerated, aggregated effect of today’s data science applications. hui that is so dangerous: individual, team or even a whole company’s value of decisions, can instantly affect the lives of millions of people. Additionally, the torrent of new technologies is moving faster than our regulatory systems, leaving an accountability vacuum. Even data scientists themselves often don’t know exactly what is going on in their algorithms.
This little magic box [the algorithm] determines our life choices, often without any transparency, due process or means of appeal,” says Karns. “That’s why ethics are so important. We don’t have to further marginalize certain groups and individuals shouldn’t have to worry about their safety because of poorly designed and unethical data apps.
To combat the current “wait and see” approach to algorithms, Karns has partnered with eCornell to launch a new Data Ethics online certificate program to equip data science practitioners with tools to embed ethics into every data science project phase and workplace. The program includes four two-week courses that offer data scientists or anyone managing data projects a structured “break” to consider the ethical implications of their work. Karns begins with an overview of the macro-level data science issues of fairness, justice, security, and privacy, then focuses on individual choices.
These choices, Karns says, are rooted in virtue ethics — the personal values or virtues that guide our behavior. The certificate program guides participants to identify and clarify their virtues, then offers low-stakes mechanisms to address ethical concerns when those virtues do not match those of a science project, team, or organization. Datas.
“With virtue ethics, we can identify what is going on that makes us uncomfortable,” says Karns. “Is this the process? The personalities? The data collection method? Then we use our moral imagination to play out future ethical considerations and consider alternatives.
These alternatives do not necessarily require compromise, since ethical best practices, which seek to reduce harm, are also exceptional business practices. Documentation is a valuable tool, as is referencing company values, risk management and reputation. This is why the new Data Ethics certificate program is not just for practitioners; Karns says data scientist managers often don’t understand the kinds of demands they make, and the choices and risks they entail. She hopes that the increased accessibility of data ethics education through online courses will be an important step towards shaping the ethical data science workplace of the future – where managers s expect discussions of potential ethical issues and incorporate ethical reflection at every stage of the development process.
“The ability to recognize and mitigate harm resulting from our actions is key to creating fair, just, and safe applications,” Karns says. “Ethical thinking is essential training, and we are currently in a particularly advantageous time to provide practitioners with the language and tools they need to improve data science outcomes and our world.”
Sarah Thompson is a writer for eCornell.