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Could the FTC Order to Delete Biased Algorithms in the Near Future?

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Continuing with my endeavor to further understand algorithmic bias, as well as learning about what is being done about it, I recently stumbled upon a fascinating article in Protocol that talked about how the Federal Trade Commission (FTC) had instructed the company WW International to delete all the illegally obtained private data and delete all algorithms derived from this data. The subject was intriguing, “a new standard for penalizing tech companies that violate privacy and use deceptive data practices: algorithmic destruction” (Kaye, 2022). Started looking further into the details by reading the FTC’s press release on the settlement resulting from a complaint filed by the Department of Justice (DOJ) on behalf of the FTC on WW International (formerly Weight Watchers). The FTC press release indicated that WW International collected personal information from children as young as eight without parental permission. The settlement instructed not only for the illegally collected data to b...
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Relying On, but Not Trusting Algorithms to Prevent Bias

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     One critical aspect of understanding how to treat algorithms is to understand the nature of our relationship with them. Algorithmic logic is math at its core. Given the objective nature of math, we tend to associate its objectiveness and describe algorithms as objective as well. Nevertheless, algorithms are not objective by nature. Quite the opposite, they are opinions written in code, glued together with math. In other words, we cannot claim a slice of apple pie to be fresh and nutritious as fruit just because it was made with apples, no matter how hard some of us may wish it to fall into that category. Figure 1 . Computers vs Humans. (Source: xkcd.com, n.d.)      The result of this, sometimes unfortunate, instinct to define by association may build a false sense of trust in algorithms. These logics are great tools to guide us through processes, but we must be careful with concluding a relationship of trust from those interactions. As algorithms get m...
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AI Bias: Human Bias in Artificial-Intelligence-Developed Algorithms

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Wondering how smart assistants like Siri or Alexa keep getting better at understanding voice instructions over time, or how Netflix seems to keep getting better at suggesting content? Or perhaps you wonder sometimes how you are getting all these ads about something you never searched for but was exactly what you needed? These are only some of the most visible and least critical interactions we have with Artificial-Intelligence-developed algorithms every day of our lives. They are designed to learn from us, analyze our interests and decision-making thinking, and ultimately outsmart us. The Wall Street Journal has a very interesting article describing how the use of AI is expanding: “We are witnessing a turning point for artificial intelligence, as more of it comes down from the clouds and into our smartphones and automobiles (…) Shield AI, a contractor for the Department of Defense, has put a great deal of AI into quadcopter-style drones which have already carried out—and continue to be...
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