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On a more serious note, some studies have shown that people are less likely to use an algorithm for prediction if they have seen that it is can get things wrong. This is known as Algorithm Aversion[1]<\/a>. If they know the algorithm is not perfect, they are put off from using it.<\/p>\n\n\n\n Anecdotally, I see this with \u2014 for example \u2014 political polling. A lot of people seem to write off polls as nonsense, because they don\u2019t always get things 100% right. Either they are perfect and worth following, or they contain error and are rubbish.<\/p>\n\n\n\n Back to Algorithm Aversion: one way to overcome this [2]<\/a> is to allow people to adjust the output of the algorithm, in a controlled manner.<\/p>\n\n\n\n Dietworst, Simmons and Massey (2018) found that if people were allowed to adjust an algorithm\u2019s forecast, they were happier with it. Restricting the amount that users could adjust forecasts did not make a lot of difference to their satisfaction.<\/p>\n\n\n\n Of course, in a real life situation, it may make a lot of sense for someone in a business to adjust a forecast produced by an algorithm: if they know something the algorithm doesn\u2019t[3]<\/a>.<\/p>\n\n\n\n For example, if the business is about to launch a big advertising campaign or slash prices \u2014 or if a close competitor has just opened a shop right opposite yours.<\/p>\n\n\n\n This is a new area of research and so far relies on some limited field experiments, with sometimes seemingly contradictory results.<\/p>\n\n\n\n A paper published last year[4]<\/a> suggested that people were likely to choose an algorithm\u2019s advice over that of other people.<\/p>\n\n\n\n However, they were a little bit less likely to pick an algorithm\u2019s opinion over their own.<\/p>\n\n\n\n The paper also found that people they determined to be experts were much less likely to take algorithmic advice over their own opinion, and that that hurt the accuracy of their predictions.<\/p>\n\n\n\n <\/p>\n\n\n\n [1]<\/a> Dietvorst, B.J., Simmons, J.P. and Massey, C., (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology<\/em>: General, 144(1), p.114.<\/p>\n\n\n\n [2]<\/a> Dietvorst, B.J., Simmons, J.P. and Massey, C., (2018). Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Science<\/em>, 64(3):1155-1170.<\/p>\n\n\n\n![\"\"](\"http:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/tessa-wilkie\/wp-content\/uploads\/sites\/14\/2020\/04\/Markus6.jpg\")
Beware our own expertise<\/h4>\n\n\n\n
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