An “AI Bill of Rights” is all well and good. Who’s going to enforce it? Will that enforcement come in time? Hmmm… I wonder!<\/p>\n\n\n\n
We have cases where an AI <\/strong>has incorrectly identified a person of interest, or where AI<\/strong> vision has more problems identifying people of color, or the case of the Kentucky court system, using an AI <\/strong>algorithm to assess a person\u2019s risk to determine the possibility of bail only to find out later that the system has disproportionately determined blacks as higher risk, whereas previously, there was little difference. We have seen AI <\/strong>algorithms discard people\u2019s resumes based on their age. There is also the case of Tay, Microsoft\u2019s AI<\/strong>, which in less than 16 hours, Twitter taught to be a racist jerk where it began posting inflammatory and offensive tweets through its Twitter account, causing Microsoft to shut it down.<\/p>\n\n\n\n The difference with AI<\/strong> and previous coding methods is that AI<\/strong> is, for the most part, is a statistical algorithm, whereas previous coding methods or languages are deterministic, if-then-else flows. Traditionally, we have seen the practice of coding evolve into something more rigorous, and several methodologies and practices have evolved: waterfall, Agile, Scrum, etc. practices and regulations have also evolved to protect information such as PCI (to protect a card holder\u2019s information), or HIPAA (to protect a patient\u2019s information), etc.\u00a0<\/p>\n\n\n\n The purpose of these methodologies and practices is precisely to bring order to the chaos of development, to force planning and design practices and to bring rigorous testing methods to any development underway. The end objective is to have solid, resilient software solutions that solve needs and also protect people\u2019s and businesses interests.<\/p>\n\n\n\n As mentioned, artificial intelligence algorithms are different. Pedro Domingos, a professor at the University of Washington, very well put it in his book, The Master Algorithm: \u201cLearning algorithms are the seeds, data is the soil, and the learned programs are the grown plants. The machine-learning expert is like a farmer, sowing the seeds, irrigating and fertilizing the soil, and keeping an eye on the health of the crop but otherwise staying out of the way.\u201d There is, as of today, no commonly accepted methodology to feed data to the current machine learning algorithms. There are also no guardrails that help determine right from wrong in these algorithms. Read On:<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"