1. Ethical considerations in decision-making
Welcome back to Demystifying Decision Science.
2. The responsibility of Data Science
Data science has revolutionized how we approach problem-solving and decision-making in diverse fields, from healthcare and finance to criminal justice and education. However, the power of data-driven insights comes with a significant responsibility to ensure these technologies are used ethically.
3. Case Study: Optum
For example, Optum, a health services company, developed an algorithm to predict which patients would likely need extra medical care. But the algorithm was biased, as it prioritized healthier patients over sicker ones, perpetuating racial inequities in health care. This bias stemmed from the algorithm's reliance on healthcare spending data, inadvertently favoring those with better care access and resources.
Organizations need to be committed to ensuring that data is used responsibly with processes that ensure the ethical use of data.
4. Key ethical principles
On an individual basis, there are a number of different oaths and checklists that have been focused on ethics in data usage that tend to have four key concepts: fairness, privacy and security, transparency and reproducibility, and the social impact of data.
5. Bias in machine learning models
Machine learning models are designed by humans, so it's not a surprise that human biases could be baked into the data used to train models and the output of machine learning models. In other words, the biases and underrepresentation present in the input data get baked into the machine learning model.
One way to quantify the fairness of a machine learning model is to measure the model's performance across groups of interest. In examining fairness, we are interested in the performance of a model across certain demographic groups, such as race, gender, or income.
6. Protecting data
Decision scientists, the organizations that employ them, and companies have an ethical obligation to be vigilant about protecting their data.
This involves considering the data infrastructure, ensuring that it is secure, has restricted access, and has the latest protections installed.
Adopting a corporate requirement that people only access the minimum information needed for their role is an appropriate security step. Data collection is another important consideration. It is critical to obtain consent for data collection and use when involving personal data.
Data breaches in any industry can decrease trust, lead to lawsuits, and hurt the bottom line.
7. Transparency and reproducibility
Decision scientists need to use data transparently and reproducibly. Transparency refers to reporting all aspects of the analysis, including the data collection methods, data cleaning steps, analysis techniques, measurements collected, and the level of uncertainty in the results.
Reproducibility refers to someone else being able to independently repeat the study using the same methods and data to produce the same results. In a corporate setting, it means that another decision scientist, given the same dataset and method, would get the same results.
8. Ethics at the heart of decision science
Ethical considerations are integral to responsible decision science. As decision science continues to shape our world, it is imperative to prioritize ethical considerations in every stage of the data lifecycle, ensuring that data-driven decisions are fair, just, and beneficial for all. By committing to ethical practices, you contribute to building a future where data serves humanity responsibly and equitably.
9. Let's practice!
Stay dedicated, and let your work make a positive impact!