What is bias?

摘要： The AI bias trouble starts — but doesn’t end — with definition. “Bias” is an overloaded term which means remarkably different things in different contexts.

Here are just a few definitions of bias for your perusal.

In statistics: Bias is the difference between the expected value of an estimator and its estimand. That’s awfully technical, so allow me to translate. Bias refers to results that are systematically off the mark. Think archery where your bow is sighted incorrectly. High bias doesn’t mean you’re shooting all over the place (that’s high variance), but may cause a perfect archer to hit below the bullseye all the time. In this usage, the word carries little emotional connotation.

▲圖片來源：medium

In data collection (and also statistics): When you fumble your data collection so your sample isn’t representative of your population of interest. “Sampling bias” is the formal name here. This kind of bias means you can’t trust your statistical results. Follow this link for my article on it.

In cognitive psychology: Systematic deviation from rationality. Every word in that pithy definition except “from” is loaded with field-specific nuance. Translation to layman’s terms? Surprise, your brain evolved some ways of reacting to stuff and psychologists initially found those reactions surprising. The list of cataloged cognitive biases is eye-popping.

In neural network algorithms: Essentially, an intercept term. (Bias sounds cooler than that high-school-math word, right?)

In the social and physical sciences: Any of a host of phenomena involving excessive influence of past/irrelevant conditions on present decisions. Examples include cultural bias and infrastructure bias.

In electronics: A fixed DC voltage or current applied in a circuit with AC signals.

In geography: A place in West Virginia. (I hear the French also have some Bias.)

In mythology: Any one of these ancient Greeks.

The one most AI experts think of: Algorithmic bias occurs when a computer system reflects the implicit values of the humans who created it. (Isn’t everything humans create a reflection of implicit values?)

The one most people think of: The way our past experiences distort our perception of and reaction to information, especially in the context of treating other humans unfairly and other generalized badness. Some folks use the word synonymously with prejudice.文

Oh dear. There are quite a few meanings here, and some of them are spicier than others.

轉貼自： medium.com

若喜歡本文，請關注我們的臉書 Please Like our Facebook Page： Big Data In Finance