Decisions on where to send police patrol cars, which foster parents to investigate, and who gets released on bail before trial are some of the most important, life-or-death decisions made by our government. And, increasingly, those decisions are being automated. The last eight years have seen an explosion in the capability of artificial intelligence, which is now used for everything from arranging your news feed on Facebook to identifying enemy combatants for the U.S. military. The automated decisions that affect us the most are somewhere in the middle. A.I.'s big feature is essentially pattern matching.

The best way to understand a machine learning algorithm is to understand how machine learning and its algorithms work.

Artificial intelligence (AI) has the potential to help every sector of the economy. There is a challenge, though, in sectors that have fuzzier analysis and the potential to train with data that can continue human biases. A couple of years ago, I described the problem with bias in an article about machine learning (ML) applied to criminal recidivism. It's worth revisiting the sector as time have changed in how bias is addressed. One way is to look at sectors in the legal profession where bias is a much smaller factor.

They help us check out at the grocery store, target us with timely ads on Instagram for a new pair of shoes, turn off our lights at a simple voice command and even determine the songs we're most apt to enjoy on our favorite music streaming platforms. Though technology has given us more convenience, connection and access than ever before, the algorithms hidden beneath its seemingly harmless code, the algorithms shaping our lives, are also grossly discriminating against our community--and all too often with impunity. If you think this doesn't affect you, think again. For us, unchecked technology shows up as police departments disproportionately deploying facial recognition software within marginalized communities to target criminal behavior, or Black people being tagged as gorillas in Google image searches, or Facebook approving housing ads that are filtered to prevent them being marketed to minorities. These practices are what Princeton University associate professor Ruha Benjamin, Ph.D., refers to as "the New Jim Code."

Our lives are increasingly affected by algorithms. People may be denied loans, jobs, insurance policies, or even parole on the basis of risk scores that they produce. Yet algorithms are notoriously prone to biases. For example, algorithms used to assess the risk of criminal recidivism often have higher error rates in minority ethic groups. As ProPublica found, the COMPAS algorithm – widely used to predict re-offending in the US criminal justice system – had a higher false positive rate in black than in white people; black people were more likely to be wrongly predicted to re-offend.

Opaque and potentially biased mathematical models are remaking our lives--and neither the companies responsible for developing them nor the government is interested in addressing the problem. This week a group of researchers, together with the American Civil Liberties Union, launched an effort to identify and highlight algorithmic bias. The AI Now initiative was announced at an event held at MIT to discuss what many experts see as a growing challenge. Algorithmic bias is shaping up to be a major societal issue at a critical moment in the evolution of machine learning and AI. If the bias lurking inside the algorithms that make ever-more-important decisions goes unrecognized and unchecked, it could have serious negative consequences, especially for poorer communities and minorities. The eventual outcry might also stymie the progress of an incredibly useful technology (see "Inspecting Algorithms for Bias").

However, algorithms can be discriminatory in a second, and more troubling sense. If data scientists are not careful in how they constructed their algorithms, their algorithms can actually be discriminatory in the sense of being biased towards certain races, ages, and sexes. How can an algorithm be biased and discriminatory? Are there any solutions to this conundrum? Very rarely is it the intent of an algorithm's creator to create an algorithm that is biased or discriminatory.

Opaque and potentially biased mathematical models are remaking our lives--and neither the companies responsible for developing them nor the government is interested in addressing the problem. This week a group of researchers, together with the American Civil Liberties Union, launched an effort to identify and highlight algorithmic bias. The AI Now initiative was announced at an event held at MIT to discuss what many experts see as a growing challenge. Algorithmic bias is shaping up to be a major societal issue at a critical moment in the evolution of machine learning and AI. If the bias lurking inside the algorithms that make ever-more-important decisions goes unrecognized and unchecked, it could have serious negative consequences, especially for poorer communities and minorities.

I read "When Computers Learn Human Languages, They Also Learn Human Prejudices." The write up makes a point which seems obvious to me and the goslings. Numbers may be neutral in the ivory tower of a mathematician in Minsk or Midland. But in the world of smart software, the human influence may be inescapable like death. Oh, Google will solve death, and I suppose at some point Google will eliminate the human element in its fancy math.