![]() The context behind this is that creating this exhibition has made me think about my own biases. Object 3: Brendan Cole’s article “‘Little Red Riding Hood’ Banned from School over Sexism Concerns” (2019)įor my third object I am using Brendan Cole’s online article “‘Little Red Riding Hood’ Banned from School over Sexism Concerns” (2019) – this is a screenshot of the article. Once again, I found something that is widely believed to be unbiased, and then I found an element of bias in it. Unless we create technology that is infinitely powerful, we cannot recreate the infinite accuracy that is intrinsic in mathematics. This object has been included in the exhibition because it shows that even in something as certain as mathematics there could be systematic mistakes (biases). It is inevitable because technology cannot have infinite power. Therefore there is some truncation involved at some point somewhere. The reason for this error is that pi is infinite, but the calculator’s memory is not. This object also exemplifies the idea of inevitability of bias. It is only this particular model, and this particular problem, that produces a biased result. This object links to the concept of bias because it is a case of systematic deviation from the truth. It is only that produces an incorrect answer, and only on this calculator model (Parker, 2020). Curiously, other similar examples do not work this way. But pi is an irrational number, which means by definition that it cannot be expressed as a fraction. One curious fact about it is that this calculator appears to be biased against this particular mathematical expression. ![]() The second object of my exhibition is Casio FX-83GT PLUS calculator. When we use a calculator, there is no reason to believe that the result of our calculation is biased. What are some other examples of knowledge that are typically believed to be free of bias? Mathematical knowledge has this kind of aura around it, especially when it is aided by technology. License: Creative Commons Attribution-Share Alike 3.0 Unported ![]() ![]() Object 2: Casio FX-83GT PLUS calculator Credit: NobbiP, Wikimedia Commons. I tried to find an example of something that is believed to be unbiased, and then I found an element of bias in it. This object was included in the exhibition because it demonstrates the idea that even the most precise scientific measurements are done against a standard, but standards themselves may change, and when they do, our new measurements become biased. The object also illustrates the idea of inevitability of bias: the Big K has lost some mass due to natural fluctuations of matter, physical laws that are beyond human control. It is a case of systematic deviation (which, by my definition, is bias). This object links to the idea of bias because, since the prototype has become lighter, we are systematically overestimating how much a kilogram is. Since 1889 it has become approximately 50 micrograms lighter – that is the weight of an eyelash (Resnick, 2019). However, despite all precautions, it changed. Scientists have taken great efforts to ensure that the mass of this prototype kilogram does not change. All 1 kg weights existing in the world today are copies of copies of copies of copies of this boulder. Back then scientists agreed to define a kilogram as the mass equivalent to the mass of this object. It is a block of platinum-iridium alloy that has been housed at the International Bureau of Weights and Measures in France since 1889. This is the Big K, the prototype kilogram. Object 1: the Big K Credit: Japs 88, Wikimedia Commons, Creative Commons Attribution-Share Alike 3.0 Unported If we do succeed in finding such elements, we must agree that bias is inevitable. ![]() Then we should try to find elements of bias in this believed-to-be-unbiased knowledge. If we want to show that bias is inevitable in the production of knowledge, we should find examples of knowledge that, by common sense, is most definitely not biased. However, if all our measurements are systematically slanted in one direction – this is bias. If we measure something 100 times, we will get slightly different readings each time – this is random error. “Systematic” is what makes bias different from an error or a mistake. I will define bias as a systematic deviation. Is bias inevitable in the production of knowledge? ![]()
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