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Wednesday, April 20, 2022

The human black-box: The illusion of understanding human better than algorithmic decision-making

Bonezzi, A., Ostinelli, M., & Melzner, J. (2022). 
Journal of Experimental Psychology: General.

Abstract

As algorithms increasingly replace human decision-makers, concerns have been voiced about the black-box nature of algorithmic decision-making. These concerns raise an apparent paradox. In many cases, human decision-makers are just as much of a black-box as the algorithms that are meant to replace them. Yet, the inscrutability of human decision-making seems to raise fewer concerns. We suggest that one of the reasons for this paradox is that people foster an illusion of understanding human better than algorithmic decision-making, when in fact, both are black-boxes. We further propose that this occurs, at least in part, because people project their own intuitive understanding of a decision-making process more onto other humans than onto algorithms, and as a result, believe that they understand human better than algorithmic decision-making, when in fact, this is merely an illusion.

General Discussion

Our work contributes to prior literature in two ways. First, it bridges two streams of research that have thus far been considered in isolation: IOED (Illusion of Explanatory Depth) (Rozenblit & Keil, 2002) and projection (Krueger,1998). IOED has mostly been documented for mechanical devices and natural phenomena and has been attributed to people confusing a superÔ¨Ācial understanding of what something does for how it does it (Keil, 2003). Our research unveils a previously unexplored driver ofIOED, namely, the tendency to project one’s own cognitions on to others, and in so doing extends the scope of IOED to human deci-sion-making. Second, our work contributes to the literature on clinical versus statistical judgments (Meehl, 1954). Previous research shows that people tend to trust humans more than algorithms (Dietvorst et al., 2015). Among the many reasons for this phenomenon (see Grove & Meehl, 1996), one is that people do not understand how algorithms work (Yeomans et al., 2019). Our research suggests that people’s distrust toward algorithms may stem not only from alack of understanding how algorithms work but also from an illusion of understanding how their human counterparts operate.

Our work can be extended by exploring other consequences and psychological processes associated with the illusion of understand-ing humans better than algorithms. As for consequences, more research is needed to explore how illusory understanding affects trust in humans versus algorithms. Our work suggests that the illusion of understanding humans more than algorithms can yield greater trust in decisions made by humans. Yet, to the extent that such an illusion stems from a projection mechanism, it might also lead to favoring algorithms over humans, depending on the underly-ing introspections. Because people’s introspections can be fraught with biases and idiosyncrasies they might not even be aware of (Nisbett & Wilson, 1977;Wilson, 2004), people might erroneously project these same biases and idiosyncrasies more onto other humans than onto algorithms and consequently trust those humans less than algorithms. To illustrate, one might expect a recruiter to favor people of the same gender or ethnic background just because one may be inclined to do so. In these circumstances, the illusion to understand humans better than algorithms might yield greater trust in algorithmic than human decisions (Bonezzi & Ostinelli, 2021).