Volume 12 - Issue 64
/ April 2023
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http:// www.amazoniainvestiga.info ISSN 2322- 6307
DOI: https://doi.org/10.34069/AI/2023.64.04.0
How to Cite:
Vergara-Romero, A. (2023). Challenges and stakes of artificial intelligence in economic sciences. Amazonia Investiga, 12(64), 7-8.
https://doi.org/10.34069/AI/2023.64.04.0
Editorial
Challenges and stakes of artificial intelligence in economic sciences
Retos y desafíos de la inteligencia artificial en las ciencias económicas
Written by:
Arnaldo Vergara-Romero
https://orcid.org/0000-0001-8503-3685
Ph.D (c). Social and Legal Science. Research Professor at the Center for Sustainable Development.
Universidad Ecotec, Samborondón-Ecuador.
The increasing presence of artificial intelligence
(AI) in economics and finance brings promising
opportunities and formidable obstacles. It is vital
to conduct an in-depth analysis of the challenges
and opportunities that artificial intelligence
brings in this field as we get closer to becoming
a society increasingly driven by technology.
One of the critical challenges in applying
artificial intelligence in economic sciences is the
access and quality of data. As Acemoglu and
Autor (2011) point out in their article "Skills,
Tasks, and Technologies: Implications for
Employment and Earnings", artificial
intelligence relies on analyzing large data sets to
obtain valuable information (Matysiak et al.,
2023). However, economic data is often scattered
and difficult to collect, which can affect the
accuracy and effectiveness of AI models.
Additionally, data quality may be biased, raising
ethical concerns and potentially perpetuating
existing inequalities (O'Neil, 2016; Chiou & Lee,
2023).
Another significant obstacle to overcome in the
field of economic sciences is interpreting the
findings of models that use artificial intelligence.
Because economic occurrences are notoriously
challenging to predict, it is essential to
comprehend how AI models arrive at their
findings. On the other hand, many of these
models are considered black boxes, making their
interpretation and explanation challenging
(Makridakis et al., 2019). Artificial intelligence
models must be transparent and easily
interpretable to ensure trust and adoption by
economic specialists and decision-makers.
The issues posed by artificial intelligence in the
economic sciences are also heavily focused on
ethical considerations. Algorithms based on
artificial intelligence can considerably impact the
distribution of resources, the selection of
investments, and the regulation of markets.
Ensuring 4these algorithms are just and impartial
and obey fundamental ethical principles (Jobin et
al., 2019). In economics, a lack of appropriate
regulation can also lead to the exploitation and
abuse of artificial intelligence, which can have
negative consequences (Brynjolfsson & McAfee,
2017).
Despite the obstacles, artificial intelligence also
presents potential in the business world that has
never been seen before. It is possible to improve
decision-making and stimulate economic growth
by doing real-time analysis of enormous amounts
of data and making predictions about economic
trends (Varian, 2014; Yoo et al., 2023).
Additionally, artificial intelligence can assist in
optimizing resource allocation and identifying
previously concealed patterns and linkages in
economic data (Chui et al., 2016; Divedi et al.,
2023).
A strategy that emphasizes cooperation and
draws on expertise from various fields is required
to handle these issues effectively. In order to
ensure the growth of artificial intelligence in
economics in a responsible manner, a
collaboration between data scientists,
economists, ethicists, and policymakers is
necessary. In addition, continuing investments in
research and development are necessary in order
to enhance data collecting and analysis methods
and to construct AI algorithms that are more
ethical and transparent.
In addition, robust regulatory frameworks are
needed that address the ethical, legal, and privacy
issues associated with artificial intelligence in
economics. These frameworks should promote
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www.amazoniainvestiga.info ISSN 2322- 6307
equity and non-discrimination while fostering
innovation and economic progress (Nogueiro et
al., 2022). The active participation of
stakeholders, including citizens, in formulating
policies and regulations is also essential to ensure
fair and equitable implementation of artificial
intelligence in the economic sphere (Stilgoe et
al., 2013).
Giacomini & White (2006) conducted a relevant
study in this field entitled "Tests of Conditional
Predictive Ability". The authors applied artificial
intelligence techniques, such as neural networks,
to predict GDP growth in the United States. They
compared the performance of artificial
intelligence models with traditional econometric
models and found that neural networks provided
more accurate and reliable forecasts. This
example demonstrates how artificial intelligence
can overcome conventional approaches'
limitations and improve economic forecasting's
accuracy.
Another use of artificial intelligence successfully
implemented in economics are algorithmic
trading and high-frequency trading. The
application of artificial intelligence algorithms in
algorithmic trading allows for split-second
judgments to be made regarding the purchase and
sale of financial assets. Artificial intelligence
algorithms can potentially improve market
liquidity and efficiency, but they also pose
regulatory and financial stability challenges,
according to a study conducted by Hendershott et
al. (2011). This study examined the impact that
high-frequency trading has on the stock market.
By way of final considerations, artificial
intelligence presents fundamental challenges and
stakes in the economic sciences, but it also offers
exciting opportunities to improve decision-
making and economic development. It is
essential to address the challenges related to data,
interpretation of results, and ethics while making
the most of the potential of artificial intelligence
in this field. With a collaborative and
multidisciplinary approach, we can guarantee a
responsible development of artificial intelligence
in economic sciences and move towards a more
efficient, equitable, and sustainable economy.
Bibliographic references
Acemoglu, D., & Autor, D. H. (2011). Skills,
Tasks, and Technologies: Implications for
Employment and Earnings. Handbook of
Labor Economics, 4, 1043-1171.
Brynjolfsson, E., & McAfee, A. (2017). The
business of artificial intelligence. Harvard
Business Review, 95(1), 237-250.
Chiou, E. K., & Lee, J. D. (2023). Trusting
automation: Designing for responsivity and
resilience. Human factors, 65(1), 137-165.
Chui, M., Manyika, J., & Miremadi, M. (2016).
Where machines could replace humans—and
where they can’t (yet). McKinsey Quarterly.
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade,
E. L., Jeyaraj, A., Kar, A. K., ... &
Wright, R. (2023). “So what if ChatGPT
wrote it?” Multidisciplinary perspectives on
opportunities, challenges, and implications of
generative conversational AI for research,
practice, and policy. International Journal of
Information Management, 71, 102642.
Giacomini, R., & White, H. (2006). Tests of
conditional predictive ability. Econometrica,
74(6), 1545-1578.
Hendershott, T., Jones, C. M., & Menkveld, A. J.
(2011). Does Algorithmic Trading Improve
Liquidity?. Journal of Finance, 66(1), 1-33.
Jobin, A., Ienca, M., & Vayena, E. (2019). The
global landscape of AI ethics guidelines.
Nature Machine Intelligence, 1(9), 389-399.
Makridakis, S., Spiliotis, E., &
Assimakopoulos, V. (2019). The forthcoming
artificial intelligence (AI) revolution: Its
impact on society and firms. Futures, 111, 26-
38.
Matysiak, A., Bellani, D., & Bogusz, H. (2023).
Industrial robots and regional fertility in
European countries. European Journal of
Population, 39(1), 11.
Nogueiro, T., Saraiva, M., Jorge, F., &
Chaleta, E. (2022). The Erasmus+
Programme and Sustainable Development
Goals—Contribution of mobility actions in
higher education. Sustainability, 14(3), 1628.
O'Neil, C. (2016). Weapons of Math Destruction:
How Big Data Increases Inequality and
Threatens Democracy. Broadway Books.
Stilgoe, J., Owen, R., & Macnaghten, P. (2013).
Developing a framework for responsible
innovation. Research Policy, 42(9), 1568-
1580.
Varian, H. R. (2014). Big data: New tricks for
econometrics. Journal of Economic
Perspectives, 28(2), 3-28.
Yoo, H. S., Jung, Y. L., & Jun, S. P. (2023).
Prediction of SMEs’ R&D performances by
machine learning for project selection.
Scientific Reports, 13(1), 7598