(Clipart from MS Office)
It seems that just as quickly as Artificial Intelligence systems show promise in transforming how we work, live, drive, and even get treated by law enforcement, scholars and others question the ethics that surround these autonomous decision-making systems. The ethics of AI focuses on whether or not decisions are being made that discriminate against people on the basis of race, religion, sex, or other criteria.
AI’s profound bias problems have become public in recent years, thanks to researchers like Joy Buolamwini and Timnit Gebru, authors of a 2018 study that showed that face-recognition algorithms nearly always identified white males but recognized black women only two-thirds of the time. Consequences of that flaw can be serious if the algorithms cause law enforcement to discriminate when identifying suspects, or doctors use the algorithms to decide who to treat.
The challenge for developers is to remove bias from AI, which is complicated because the system depends upon the data that goes into the system. Training data must be vast, diverse, and reflective of the population so that the AI system has a strong sample.
Use this forum to discuss two examples of situations where bias can skew the data causing an AI system to discriminate against certain groups of people. How can fairness be built into the AI systems? Are the advantages that AI bring to a system worth the bias, if uncorrected?
REMEMBER- every post (New Thread or Reply) must be supported by relevant information. Prove the point you are making by a) citing external research, b) citing readings from the class content, or c) providing examples or personal experiences that are relevant and support your position on the topic. It is always better to begin your reflection on the topic by doing some research/reading, either a) or b) or both, before considering personal experience. This research, reflection and subsequent writing is an essential part of the learning process, framing your personal experience against and alongside more general theories, concepts and writing on the topic. Grading of your participation will be according to the table outlined in the Grading Policy/Rubric for Class Participation (Weeks 1 – 8)” You can see the rubric by clicking on Discussions in the top nav bar, then scrolling down to the Weekly Discussions area.