|
on Artificial Intelligence |
| By: | Pletcher, Scott Nicholas |
| Abstract: | For most, job performance evaluations are often just another expected part of the employee experience. While these evaluations take on different forms depending on the occupation, the usual objective is to align the employee’s activities with the values and objectives of the greater organization. Of course, pursuing this objective involves a whole host of complex skills and abilities which sometimes pose challenges to leaders and organizations. Automation has long been a favored tool of businesses to help bring consistency, efficiency, and accuracy to various processes, including many human capital management processes. Recent improvements in artificial intelligence (AI) approaches have enabled new options for its use in the HCM space. One such use case is assisting leaders in evaluating their employees’ performance. While using technology to measure and evaluate worker production is not novel, the potential now exists through AI algorithms to delve beyond just piece-meal work and make inferences about an employee’s economic impact, emotional state, aptitude for leadership and the likelihood of leaving. Many organizations are eager to use these tools, potentially saving time and money, and are keen on removing bias or inconsistency humans can introduce in the employee evaluation process. However, these AI models often consist of large, complex neural networks where transparency and explainability are not easily achieved. These black-box systems might do a reasonable job, but what are the implications of faceless algorithms making life-changing decisions for employees? |
| Date: | 2023–04–28 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:29yej&r=ain |
| By: | Veale, Michael (University College London); Matus, Kira; Gorwa, Robert |
| Abstract: | Artificial intelligence (AI) is a salient but polarizing issue of recent times. Actors around the world are engaged in building a governance regime around it. What exactly the “it” is that is being governed, how, by who, and why—these are all less clear. In this review, we attempt to shine some light on those questions, considering literature on AI, the governance of computing, and regulation and governance more broadly. We take critical stock of the different modalities of the global governance of AI that have been emerging, such as ethical councils, industry governance, contracts and licensing, standards, international agreements, and domestic legislation with extraterritorial impact. Considering these, we examine selected rationales and tensions that underpin them, drawing attention to the interests and ideas driving these different modalities. As these regimes become clearer and more stable, we urge those engaging with or studying the global governance of AI to constantly ask the important question of all global governance regimes: Who benefits? |
| Date: | 2023–05–31 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:ubxgk&r=ain |
| By: | Fourberg, Niklas; Marques-Magalhaes, Katrin; Wiewiorra, Lukas |
| Abstract: | We analyze pricing patterns and price level effects of algorithms in the market segments for OTC-antiallergics and -painkillers in Germany. Based on a novel hourly dataset which spans over four months and contains over 10 million single observations, we produce the following results. First, price levels are substantially higher for antiallergics compared to the segment of painkillers, which seems to be reflective of a lower price elasticity for antial- lergics. Second, we find evidence that this exploitation of demand character- istics is heterogeneous with respect to the pricing technology. Retailers with a more advanced pricing technology establish even higher price premiums for antiallergics than retailers with a less advanced technology. Third, retailers with more advanced pricing technology post lower prices which contradicts previous findings from simulations but are in line with empirical findings if many firms compete in a market. Lastly, our data suggests that pricing algo- rithms take web-traffic of retailers' online-shops as demand side feedback into account when choosing prices. Our results stress the importance of a careful policy approach towards pricing algorithms and highlights new areas of risks when multiple players employ the same pricing technology. |
| Keywords: | Algorithmic pricing, Collusion, Artificial intelligence |
| JEL: | C13 D83 L13 L41 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:wikwps:6&r=ain |
| By: | Hirsch, Felix; Alizadeh, Armin; Wiener, Martin; Cram, W. Alec |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:138131&r=ain |
| By: | Alizadeh, Armin; Hirsch, Felix; Benlian, Alexander; Wiener, Martin; Cram, W. Alec |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:138128&r=ain |
| By: | Cobbe, Jennifer; Veale, Michael (University College London); Singh, Jatinder |
| Abstract: | Academic and policy proposals on algorithmic accountability often seek to understand algorithmic systems in their socio-technical context, recognising that they are produced by ‘many hands’. Increasingly, however, algorithmic systems are also produced, deployed, and used within a supply chain comprising multiple actors tied together by flows of data between them. In such cases, it is the working together of an algorithmic supply chain of different actors who contribute to the production, deployment, use, and functionality that drives systems and produces particular outcomes. We argue that algorithmic accountability discussions must consider supply chains and the difficult implications they raise for the governance and accountability of algorithmic systems. In doing so, we explore algorithmic supply chains, locating them in their broader technical and political economic context and identifying some key features that should be understood in future work on algorithmic governance and accountability (particularly regarding general purpose AI services). To highlight ways forward and areas warranting attention, we further discuss some implications raised by supply chains: challenges for allocating accountability stemming from distributed responsibility for systems between actors, limited visibility due to the accountability horizon, service models of use and liability, and cross-border supply chains and regulatory arbitrage |
| Date: | 2023–04–24 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:p4sey&r=ain |
| By: | Occhini, Giulia; Tranos, Emmanouil; Wolf, Levi John (University of Bristol) |
| Abstract: | This paper investigates whether and how occupational segregation affects the digital economy. Despite the continuous growth of entrepreneurial activity in the digital, little is known about the demographic characteristics of people actively engaging with it and bene ting from it. Further, while popular discourse portrays the digital as a \level playing eld" for economic engagement, the literature has yet to empirically test these claims. Gaining a better understanding of whether occupational segregation is replicated in the digital can assist us in bridging new types of digital inequalities and demystify meritocratic narratives around success in the digital economy. To address this question, we use textual data extracted from UK commercial websites and model digital economic activities through Natural Language Processing techniques. We compare our findings across different gender and ethnicity groups, adopting a research framework informed by intersectionality theory. Our results indicate that occupational segregation persists in the digital economy, as male and female entrepreneurs tend to engage with economic activities stereotypically associating with their gender. However, we do not find the same results when comparing entrepreneurial outputs of female and male entrepreneurs of colour. Our results pave the way for more research in entrepreneurship using Natural Language Processing, textual data and analyses at the intersectional level. |
| Date: | 2023–05–18 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:z8xta&r=ain |
| By: | Michael Allan Ribers; Hannes Ullrich |
| Abstract: | Inefficient human decisions are driven by biases and limited information. Health care is one leading example where machine learning is hoped to deliver efficiency gains. Antibiotic resistance constitutes a major challenge to health care systems due to human antibiotic overuse. We investigate how a policy leveraging the strengths of a machine learning algorithm and physicians can provide new opportunities to reduce antibiotic use. We focus on urinary tract infections in primary care, a leading cause for antibiotic use, where physicians often prescribe prior to attaining diagnostic certainty. Symptom assessment and rapid testing provide diagnostic information with limited accuracy, while laboratory testing can diagnose bacterial infections with considerable delay. Linking Danish administrative and laboratory data, we optimize policy rules which base initial prescription decisions on machine learning predictions and delegate decisions to physicians where these benefit most from private information at the point-of-care. The policy shows a potential to reduce antibiotic prescribing by 8.1 percent and overprescribing by 20.3 percent without assigning fewer prescriptions to patients with bacterial infections. We find human-algorithm complementarity is essential to achieve efficiency gains. |
| Date: | 2023–06–05 |
| URL: | http://d.repec.org/n?u=RePEc:bdp:dpaper:0019&r=ain |
| By: | Sinan Deng; John Inekwe; Vladimir Smirnov; Andrew Wait; Chao Wang |
| Abstract: | In this paper, we propose a seasonal attention mechanism, the effectiveness of which is evaluated via the Bidirectional Long Short-Term Memory (BiLSTM) model. We compare its performance with alternative deep learning and machine learning models in forecasting the balancing settlement prices in the electricity market of Great Britain. Critically, the Seasonal Attention-Based BiLSTM framework provides a superior forecast of extreme prices with an out-of-sample gain in the predictability of 25-37% compared with models in the literature. Our forecasting techniques could aid both market participants, to better manage their risk and assign their assets, and policy makers, to operate the system at lower cost. |
| Keywords: | forecasting; electricity; balance settlement prices; Long Short-Term Memory; machine learning. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:syd:wpaper:2023-03&r=ain |