People have come to rely on the advice of algorithms for all aspects of their lives, from mundane tasks like choosing the most efficient navigation route home to financial decisions regarding how to invest retirement savings. Because of the ubiquity of algorithms, people have become increasingly comfortable relying on them--a tendency known as automation bias. This Article presents an empirical study that explores automation bias in the area of consumer finance. The study confirms that when making consumer finance decisions, including making significant investment decisions, Americans significantly prefer following the recommendations of algorithms to those of human experts. Moreover, even after poor performance as a result of following an algorithm's advice--or even outright mistakes by the algorithm--consumers continue to favor algorithms to human experts. This result demonstrates that we view algorithms--especially those rooted in big data--as a superior authority. Our increasing deference to algorithmic results is concerning because we are avoiding obtaining "a second opinion"--even when the first opinion comes from an algorithm that has made mistakes in the past. Although second opinions are costly, they are important--and even critical--in certain situations. By reducing the acceptability of seeking second opinions, our algorithm-dependent society is nudging us to tone down creativity, innovation and critical thinking, and instead to blindly rely on the new experts--the algorithms, whose biases are difficult to assess. Second opinions do not necessarily need to be human-formulated opinions. In the era of big data and AI, different algorithms that are based on dissimilar data and assumptions can offer second opinions that might be more objective than human-formulated second opinions, which are affected by a human automation bias. As a conclusion, this Article argues that institutions and individuals should implement cultural changes by hyper-nudging users to seek second opinions, including AI-based opinions, and by requiring algorithmic auditing.
|Number of pages||59|
|Journal||New York University Journal of Legislation & Public Policy|
|State||Published - 2020|
- Artificial intelligence in business
- Consumer finance companies
- Consumer credit