Introduction to the thematic issue on digitalisation, big data, and artificial intelligence

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The aim of the paper is to analyse the likely implications of Generative AI (GAI) on various aspects of business and the economy. Amid the rapid growth and maturing of Generative AI technologies such as Large Language Models (like ChatGPT by OpenAI) a rapid growth of both immediate and potential applications can be seen. The implications for the economy and industries of this technological shift will be discussed. The foreseeable scenarios for the level and types of adoption that GAI might achieve—from useful analytical tool, invaluable assistant to the white-collar workers of the world to being trusted with a wide array of business and life-critical decision making. Both disruptive and premium service opportunities are foreseen. For instance, general purpose models may provide quality service—such as copywriting—to overserved customers leaving human writers as the premium option. In this context, overserved customers would be those who would be satisfied with a non-human, potentially less creative content. On the other hand highly specialized models—specifically trained in a given domain and with access to proprietary knowledge can possibly provide a premium service over that provided by human experts. It is expected that some jobs will be replaced by new AI applications. However, new workplaces will emerge. Not only the obvious expert-level data scientist roles but also low grade, “model supervisors”—people training the models, assessing the quality of responses given and handling escalations. Lastly new cybercrime risks emerging from the rise of GAI are discussed.
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The rise of Generative AI and possible effects on the economy

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Over the last years the use of big data became increasingly relevant also for macroeconomic topics and specifically the conduct and analysis of monetary policy. The aim of this paper is to provide a survey of these applications and the relevant methods. The rationale for doing so is twofold. First, there is no straightforward definition of “big data”. Since macroeconomics and monetary policy analysis has a long tradition in quite sophisticated and data-intensive empirical applications the nature of the innovation big data is indeed bringing to the field is reflected upon. Second, concerning statistical / empirical methods the analysis of big data necessitates the use of different tools relative to traditional empirical macroeconomics which are in some cases a complement to more traditional methods. Hence big data in monetary policy is not just the application of well-established methods to larger data sets.
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Big data in monetary policy analysis—a critical assessment

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In this paper the impact of large language models (LLM) on the fake news phenomenon is analysed. On the one hand decent text‐generation capabilities can be misused for mass fake news production. On the other, LLMs trained on huge volumes of text have already accumulated information on many facts thus one may assume they could be used for fact‐checking. Experiments were designed and conducted to verify how much LLM responses are aligned with actual fact‐checking verdicts. The research methodology consists of an experimental dataset preparation and a protocol for interacting with ChatGPT, currently the most sophisticated LLM. A research corpus was explicitly composed for the purpose of this work consisting of several thousand claims randomly selected from claim reviews published by fact‐ checkers. Findings include: it is difficult to align the respons‐ es of ChatGPT with explanations provided by fact‐checkers; prompts have significant impact on the bias of responses. ChatGPT at the current state can be used as a support in fact‐checking but cannot verify claims directly.
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Poznań University of Economics and Business

Artificial intelligence—friend or foe in fake news campaigns

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The aim of this paper is to discuss the role and impact of Generative Artificial Intelligence (AI) systems in higher education. The proliferation of AI models such as GPT-4, Open Assistant and DALL-E presents a paradigm shift in information acquisition and learning. This transformation poses substantial challenges for traditional teaching approaches and the role of educators. The paper explores the advantages and potential threats of using Generative AI in education and necessary changes in curricula. It further discusses the need to foster digital literacy and the ethical use of AI. The paper’s findings are based on a survey conducted among university students exploring their usage and perception of these AI systems. Finally, recommendations for the use of AI in higher education are offered, which emphasize the need to harness AI’s potential while mitigating its risks. This discourse aims at stimulating policy and strategy development to ensure relevant and effective education in the rapidly evolving digital landscape.
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Challenges for higher education in the era of widespread access to Generative AI

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The introduction of ChatGPT has fuelled a public debate on the appropriateness of using Generative AI (large language models; LLMs) in work, including a debate on how they might be used (and abused) by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust researchers and devalues their scientific work. Participants (<italic>N</italic> = 402) considered a researcher who delegates elements of the research process to a PhD student or LLM and rated three aspects of such delegation. Firstly, they rated whether it is morally appropriate to do so. Secondly, they judged whether—after deciding to delegate the research process—they would trust the scientist (that decided to delegate) to oversee future projects. Thirdly, they rated the expected accuracy and quality of the output from the delegated research process. Our results show that people judged delegating to an LLM as less morally acceptable than delegating to a human (<italic>d</italic> = –0.78). Delegation to an LLM also decreased trust to oversee future research projects (<italic>d</italic> = –0.80), and people thought the results would be less accurate and of lower quality (<italic>d</italic> = −0.85). We discuss how this devaluation might transfer into the underreporting of Generative AI use.
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Poznań University of Economics and Business, Humans & AI Laboratory (HAI Lab)

Department of Psychology, B44 University Road

Judgements of research co-created by Generative AI: Experimental evidence

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Accidentally parallel at the beginning, the transition to value-based pricing and transition to pricing data science have blended harmoniously, changing the pricing landscape. Using the marketing capability approach, I show that the introduction of pricing data science is costly and requires higher management support. Despite its cost, algorithmic price optimisation allows one to react swiftly to changes in demand. The optimisation process is applied to inherently non-linear, multimodal, and right-skewed pricing data. Presenting the interactions between new computational techniques and value-data pricing, I concentrate on altered perceptions of price elasticity, value-driver estimations, and contract opportunity analysis.
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Pricing and data science: The tale of two accidentally parallel transitions

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This paper introduces and examines a novel realized volatility forecasting model that makes use of Long Short-Term Memory (LSTM) neural networks and the risk metric financial turbulence (FT). The proposed model is compared to five alternative models, of which two incorporate LSTM neural networks and the remaining three include GARCH(1,1), EGARCH(1,1), and HAR models. The results of this paper demonstrate that the proposed model yields statistically significantly more accurate and robust forecasts than all other studied models when applied to stocks with middle-to-high volatility. Yet, considering low-volatility stocks, it can only be confidently affirmed that the proposed model yields statistically significantly more robust forecasts relative to all other models considered.
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International School of Business at HAN University of Applied Sciences

Forecasting realized volatility through financial turbulence and neural networks

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For most active investors treasury bonds (govs) provide diversification and thus reduce the risk of a portfolio. These features of govs become particularly desirable in times of elevated risk which materialize in the form of the flight-to-safety (FTS) phenomenon. The FTS for govs provides a shelter during market turbulence and is exceptionally beneficial for portfolio drawdown risk reduction. However, what if the unsatisfactory expected return from treasuries discourages higher bonds allocations? This research proposes a solution to this problem with Deep Target Volatility Equity-Bond Allocation (DTVEBA) that dynamically allocate portfolios between equity and treasuries. The strategy is driven by a state-of-the-art recurrent neural network (RNN) that predicts next-day market volatility. An analysis conducted over a twelve year out-of-sample period found that with DTVEBA an investor may reduce treasury allocation by two (three) times to get the same Sharpe (Calmar) ratio and overper-forms the S&amp;P500 index by 43% (115%).
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Department of Investment and Financial Markets, Institute of Finance

How to fly to safety without overpaying for the ticket

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