Ethical Considerations in Data Science
July 18, 2024
Ethical Considerations in Data Science
Have you ever seen movies such as The Terminator with robots gaining consciousness and taking over the world? This is actually a real phenomenon that computer scientists actively combat in order to make sure programs are being used safely and ethically. Data science and AI has transformed the way we understand and interact with the world. By leveraging these advanced algorithms, computers can directly identify relationships within data, make predictions, and inform extremely quick decision making across various fields of industry. However, with great power comes great responsibility. The ethical implications of data science are similar to a double-edged sword, requiring programmers to make good ethical decisions to keep computers from causing harm to society.
Privacy and Confidentiality
One of the biggest ethical concerns in data science—especially when actually performing real experiments and gathering data—is privacy. Data scientists work with sensitive information, including personal, financial, and health data, which causes issues when the subjects of this data are uncomfortable with this info being used. Ensuring that this information is kept confidential and secure is extremely important. Improper use of this data in the wrong hands could easily lead to identity theft, financial loss, and even physical harm. To prevent these problems, data scientists implement data security systems and techniques such as anonymization and encryption to safeguard data.
Data anonymization
Data Anonymization is the process of removing individual information from a dataset that could identify an individual. For instance, when a certain hospital shares patient data with a lab, they may withhold the patients’ name, address, or social security number to ensure a patient cannot be backtracked to their data entry. Encryption is a similar process of transforming the data into secret code in the case of when hackers breach into a database. This could be through a simple cypher such as a caesar cypher, or other complex automated encryption techniques.
Closely related to privacy is the issue of informed consent. Individuals whose data is being collected should be aware of how their information will be used. This is particularly applicable in situations where data is collected indirectly on a larger scale, such as through social media activity or online behavior tracking. You may have personally experienced this when using apps such as Instagram or YouTube, where they request to use your data to provide personalized ads.
Bias and Fairness
Bias in data science is another significant ethical challenge more commonly found in machine learning. Algorithms trained on biased data can find unintentional trends in data which can produce flawed AI models. For instance, facial recognition systems have been shown to have higher error rates for people of color, leading to concerns about racial bias. A couple of months ago an AI security system was proposed using cameras to identify criminals. The model was trained with a dataset containing more African American criminals than other races, leading to a higher rate of false positives due to the skewed data. Data scientists must work to control bias in their datasets to ensure that their algorithms are fair and equal.
Impact on Employment
The rise of automation and machine learning has sparked concerns about the future of employment. As algorithms become more capable of performing tasks usually done by humans, there is a risk of job displacement, particularly in easily automated low skill industries such as factories and construction. It is undeniably important for scientists to consider the broader impact of their work to maintain a smooth-flowing economy.
The ethical considerations of data science are complex and constantly evolving as technology evolves. As computers’ power continues to grow, it is crucial for data scientists to remain conscious of the ethical consequences of their work.
Sources:
https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
https://callingbullshit.org/case_studies/case_study_criminal_machine_learning.html