One of the biggest concerns towards the improvement of AI is human worker replacement. Most people view the adoption of robots as a threat to human worker. However, the influence of AI to job market is complex and uncertain rather than a simple threat. Here are the reasons:
The influence is also complex because the different types of workers are affected by the robot’s adoption in different ways. The replacement occurs starting from lower tasks to higher tasks which requires softer skills, from mechanical, to analytical, to intuitive, to empathetic by order (Huang, 2018). Robots replaces workers who perform well-defined cognitive and manual tasks (Autor, 2003). Workers in transportation, logistics, administrative support and production occupations are at risk (Frey, 2017). However, the adoption of robots complements workers in performing nonroutine tasks such as problem-solving and complex communication activities (Autor, 2003).
The influence is uncertain because there are two types of job effects, displacement effect and productivity effect, and it is unclear which effect outweigh the other (Acemoglu, 2017). Displacement effect, as name suggests, means robots directly replacing workers from tasks that they were performing (Acemoglu, 2017), which affect employment and wages negatively. Productivity effect means other industries and tasks increasing their demand for labor, which suggests that the adoption of robots does not necessarily reduce the total amount of job opportunity but rather shift job opportunity from one industry to another (Acemoglu, 2017). It is true that robots may reduce the employment of low-skilled workers, but no significant relationship is found between the increased use of industrial robots and overall employment (Graetz, 2018). New technologies can complement labor by introducing new tasks in which labor has a comparative advantage, such as tasks require creative and social intelligence (Acemoglu, 2018). In the long run, if capital is significantly cheaper relative to labor, automation will advance rapidly and displace human labor. Otherwise, the framework yields a balanced growth path in which automaton and the creation of new tasks go hand-in-hand (Acemoglu, 2018).

Huang, M. and Rust, R. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2), pp.155-172.
Autor, D., Levy, F. and Murnane, R. (2003). The Skill Content of Recent Technological Change: An Empirical Exploration. The Quarterly Journal of Economics, 118(4), pp.1279-1333.
Frey, C. and Osborne, M. (2017). The future of employment: How susceptible are jobs to computerisation?. Technological Forecasting and Social Change, 114, pp.254-280.
Acemoglu, D. and Restrepo, P. (2017). Robots and Jobs: Evidence from US Labor Markets. SSRN Electronic Journal.
Acemoglu, D. and Restrepo, P. (2018). The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment. American Economic Review, 108(6), pp.1488-1542.
Graetz, G. and Michaels, G. (2018). Robots at Work. The Review of Economics and Statistics, 100(5), pp.753-768.

With the development of Artificial Intelligence (AI), more and more companies started adopting AI in their decision-making process. Human’s reliance on algorithm advice increases rapidly. However, an interesting phenomenon called “algorithm aversion”, which presents a negative effect on the adoption of the algorithm, is recently discovered.
There are two main methods in the decision-making process: human method and algorithm method (Dietvorst, 2014). With human method, human decision-makers review relevant information manually and make a forecast. With the algorithm method, human only need to enter historical data into the statistical model and the algorithm will generate the forecasting outcome automatically, which is more efficient than the human method.
If the algorithm consistently outperform human, then why not adopt the algorithm method in every decision-making process (e.g. university admission, merge and acquisition, etc.) However, people still often prefer humans’ forecasts to algorithms’ forecasts (Dietvorst, 2014), which refers to algorithm aversion. Dietvorst (2014) stated that people are more likely to exhibit algorithm aversion when they see the algorithm err and they tend to have a higher tolerance to human forecaster’s mistakes. Especially in uncertain decision domain, human is more likely the decision maker that they believe is more likely to provide a perfect answer, which in turn leads to a riskier decision-making method (such as human judgement) and results in the underused best possible algorithm (Dietvorst, 2015). Because people believe that human can get better after practicing and learning from the mistakes, though algorithms can improve as well (Frick, 2015).
Algorithm aversion should be overcome. The reason is twofold: First, human is subject to the influence of noise (irrelevant factors), which leads to significant negative effect in forecasting accuracy (Harrell, 2016). Second, comparing to algorithm, human is not good at giving input factors appropriate weight consistently (Harrell, 2016).
However, there are ways to tackle algorithm aversion. Dietvorst (2015) found that people are more willing to use algorithms when they can modify them, even they know the algorithms are not perfect (Dietvorst, 2015). Surprisingly, people are insensitive to the extent that they can modify the algorithms, which means people don’t mind modifying algorithms in a constrained manner (Dietvorst, 2015).

Dietvorst, B., Simmons, J. and Massey, C. (2014). Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them Err. SSRN Electronic Journal.
Dietvorst, B., Simmons, J. and Massey, C. (2015). Overcoming Algorithm Aversion: People Will Use Algorithms If They Can (Even Slightly) Modify Them. SSRN Electronic Journal.
Harvard Business Review. (2019). Here’s Why People Trust Human Judgment Over Algorithms. [online] Available at: https://hbr.org/2015/02/heres-why-people-trust-human-judgment-over-algorithms [Accessed 2 Oct. 2019].
Harvard Business Review. (2019). Managers Shouldn’t Fear Algorithm-Based Decision Making. [online] Available at: https://hbr.org/2016/09/managers-shouldnt-fear-algorithm-based-decision-making [Accessed 2 Oct. 2019].