It's that time again for Americans to take to the polls. If you've lived long enough, you recognize the patterns… Each opposing political side, shades the other, scandals and leaks may pop, shortcomings are magnified, critics make the news, promises are doled out'rather-convincingly' and there's an overwhelming sense of'nationality and togetherness' touted by both sides… And often, we simply choose the'lesser of the two evils', because candidly the one is not significantly better than the other. So today, I'm going to analyze the presidential debates of President Trump and Vice-President Biden… The entire analysis is done by the Author, using scientific methods that do not assume faultlessness. This is a personal project devoid of any political affiliations, sentiments or undertones. The inferences expressed from this scientific process are entirely the Author's, based on the data.

How will AI strategy in the enterprise be changed by the widespread attention to systemic racism? Like a lot of complicated topics, the discussion of racism in AI systems tends to be filtered through events that make headline news -- the Microsoft chatbot that Twitter users turned into a racist, the Google algorithm that labeled images of Black people as gorillas, the photo-enhancing algorithm that changed a grainy headshot of former President Barack Obama into a white man's face. Less sensational but even more alarming are the exposés on race-biased algorithms that influence life-altering decisions on who should get loans and medical care or be arrested. Stories like these call attention to serious problems with society's application of artificial intelligence, but to understand racism in AI -- and form a business strategy for dealing with it -- enterprise leaders must get beneath the surface of the news and beyond the algorithm. "I think that racism and bias are rampant in AI and data science from inception," said Desmond Upton Patton, associate professor of sociology at Columbia University. "It starts with how we conceive a problem [for AI to solve]. The people involved in defining the problem approach it from a biased lens. It also reaches down into how we categorize the data, and how the AI tools are created. What is missing is racial inclusivity into who gets to develop AI tools."

Whether or not you're aware, your Google searches, questions posed to Siri, and Facebook timeline all rely on artificial intelligence (AI) to perform effectively. Artificial intelligence is the simulation of human intelligence processes by machines. The goal of artificial intelligence is to build models that can perform specific tasks as intelligently as humans can, if not better. Much of the AI you encounter on a daily basis uses a technique known as machine learning, which uses predictive modeling to generate accurate predictions when given random quantities of data. Because predictive models are built to find relational patterns in data, they learn to favor efficiency over fairness.

Left alone, algorithms will count a black defendant's race as a strike against them; yet, several data scientists in the community are supporting calls to turn off the safeguards and unleash the hells of computerized prejudice. Put yourself in the computer's "shoes" for a second; imagine yourself sitting across a person being evaluated for a loan or a job. When they ask you how you make your decision, you inform them, "Well for one, we docked you because you're black." In what logical sense should this sort of comment be tolerated. If humans are reprimanded for making such ignorant comments, why should a computer be allowed to? This simple understanding does not exist amongst a significant percentage of the larger data science, machine learning, and even political community.

'As a result of federal initiatives to "get tough on crime," such as the Reagan Administration's War on Drugs and the Clinton Administration's "Three Strikes" laws, the total number of incarcerated Americans more than quadrupled from roughly 500,000 inmates in 1980 to 2.2 million inmates in 2015. During these decades, black Americans were incarcerated at a rate five times higher than that of white Americans. Despite a new 2019 US Bureau of Justice Statistics (BJS) report, which suggests that the racial disparity between white and black incarceration rates is "narrowing," a Pew Research Center review of BJS stats reveals that this 2019 report "counts only inmates sentenced to more than a year."Moreover, Whites accounted for 64% of adults but 30% of prisoners. . . . In 2017, there were 1,549 black prisoners for every 100,000 black adults--nearly six times the imprisonment rate for whites (272 per 100,000)."

At a New York event celebrating the legacy of Martin Luther King Jr. held in the Riverside Church last week, Democratic Rep. Alexandria Ocasio-Cortez sparked a small firestorm when she argued that algorithms reflect human bias. "Algorithms are still made by human beings, and those algorithms are still pegged to basic human assumptions," she said. And if you don't fix the bias, then you are just automating the bias." "Socialist Rep. Alexandria Ocasio-Cortez (D-NY) claims that algorithms, which are driven by math, are racist," replied Daily Wire reporter Ryan Saavedra, kicking off the latest cycle of online conservative handwringing about something AOC has said. Ocasio-Cortez was right, though, and what she said should not be that controversial.