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Your phone keeps a lot of information about you, including where you have been, what you buy, what games you play, etc. Here’s a video about the massive amounts of data our smart phones and computers collect about us. If you have used your phone to give you directions to go somewhere, it probably tracks your location. Follow the directions in https://www.lifewire.com/location-history-google-maps-iphone-1683392 to see if you have location history settings on. You can also turn off location tracking, but it is useful when you want directions and it’s free. Do the benefits of apps that provide driving directions outweigh the lack of privacy for you? In what situations would it be beneficial or harmful for the app to track your location?

As users, we often don’t realize how much personal data we are giving away. If you use a computer or phone, your personal privacy is at risk. As computer programmers, we must be aware of the risks to data privacy when our code collects and stores personal data on computer systems. Programmers should attempt to safeguard the personal privacy of the user. Legally and ethically, we must ask the users for permission to access and store their data. And if there are data breaches where the data is stolen, we must inform the affected users. The laws are slowly catching up to our technology, and many countries and states are passing laws to protect data privacy.

Computer use and programs have beneficial and/or harmful impacts on personal security. Software apps for maps and driving directions are very useful, but they have impacts on personal security and privacy if they keep track of your location. This information could be beneficial, for example if you are lost and need to be found, but could be harmful and unsafe if someone unauthorized gains access to your location.

Here are some interesting video resources about data collection and data privacy:

The fields of AI (Artificial Intelligence) and Machine Learning (ML) are rapidly growing and increasingly involve ethical questions about data collection, privacy, and resource use. Machine learning algorithms to create software like ChatGPT require massive amounts of data to learn from as well as massive amounts of energy use. But where does this data come from? Often the data is collected from the internet, and the internet is full of biases. For example, if you search for professions like “programmer”, “doctor”, “CEO” in https://images.google.com/, you will probably see mostly images of white men. This reflects a bias in our world that AIs may learn. An AI could then generate text or images that are biased against historically underrepresented groups. For example, a prominent tech company in 2014 started building an automated hiring tool, a resume filtering AI trained on their current employees resumes, and ended up with an AI system that was biased against women (https://www.aclu.org/news/womens-rights/why-amazons-automated-hiring-tool-discriminated-against). This is a problem because the AI is learning from biased data and then creating biased outcomes that could affect people’s lives.

Algorithmic bias describes systemic and repeated errors in a program that create unfair outcomes for a specific group of users. Bias in data can lead to unfair and unethical outcomes. For instance, facial recognition software has been shown to have higher error rates for people with darker skin tones. This is because the data used to train these algorithms often contains fewer examples of people with darker skin tones. As a result, the software is less accurate for these individuals, which can lead to discriminatory practices.

Watch the following Gender Shades video about gender and race bias in face recognition algorithms by computer scientist Joy Buolamwini (MIT Media Lab and Algorithmic Justice League).

Here are some other interesting videos to watch about bias in algorithms:

Programmers should be aware of the data set collection method and the potential for bias before using the data to extrapolate new information or drawing conclusions. Some data sets are incomplete or contain inaccurate data. Using such data in the development or use of a program can cause the program to work incorrectly or inefficiently. Or the contents of a data set might be related to a specific question or topic and might not be appropriate to give correct answers or extrapolate information for a different question or topic.

It is important for programmers and data scientists to take steps to mitigate bias in data collection and use. This can include using diverse and representative datasets, regularly testing algorithms for bias, and being transparent about the limitations of the software. Here are some steps that can be taken to address bias in machine learning: