Strategic Best Response Fairness in Fair Machine Learning

Shimao, Hajime; Khern-am-nuai, Warut; Kannan, Karthik; Cohen, Maxime C.

doi:10.1145/3514094.3534194

Cited by 12 publications

(5 citation statements)

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“…The discovery and control of novel target types, such as protein–protein interactions, targets with large contact areas, protein–nucleic acid interactions, and next‐generation targets, like utilizing the cell's protein degradation machinery, is made possible by the use of advanced modeling tools. To link drug response to genetic variation, understand stratified clinical efficacy and safety, explain the differences between medications in the same therapeutic class, and predict drug utility in patient subgroups, advanced modeling methods based on AI help redefine the very definition of biological targets (Shimao et al, 2022).…”

Section: Key Industry Observations and Trends Of Ai In Drug Discoverymentioning

confidence: 99%

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

Agu,

Obulose

2024

Drug Development Research

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The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and expedient options to conventional approaches. The paper opens with an overview of well‐known information sources for drug development. The discussion then moves on to molecular representation techniques that make it possible to convert data into representations that computers can understand. The paper also gives a general overview of the algorithms used in the creation of drug discovery models based on AI. In particular, the paper looks at how AI algorithms might be used to forecast drug toxicity, drug bioactivity, and drug physicochemical properties. De novo drug design, binding affinity prediction, and other AI‐based models for drug–target interaction were covered in deeper detail. Modern applications of AI in nanomedicine design and pharmacological synergism/antagonism prediction were also covered. The potential advantages of AI in drug development are highlighted as the evaluation comes to a close. It underlines how AI may greatly speed up and improve the efficiency of drug discovery, resulting in the creation of new and better medicines. To fully realize the promise of AI in drug discovery, the review acknowledges the difficulties that come with its uses in this field and advocates for more study and development.

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Section: Key Industry Observations and Trends Of Ai In Drug Discoverymentioning

confidence: 99%

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

Agu,

Obulose

2024

Drug Development Research

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“…For example, if the data used to train an ML algorithm are biased or unrepresentative, the resulting predictions may be inaccurate or unfair [ 44 ]. Ensuring the ethical and fair use of AI for the development of new therapeutic compounds is an important consideration that must be addressed [ 45 ]. Several strategies and approaches can be used to overcome the obstacles faced by AI in the context of chemical medicine.…”

Section: Challenges and Limitations Of Using Ai In Drug Discoverymentioning

confidence: 99%

The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies

et al. 2023

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Artificial intelligence (AI) has the potential to revolutionize the drug discovery process, offering improved efficiency, accuracy, and speed. However, the successful application of AI is dependent on the availability of high-quality data, the addressing of ethical concerns, and the recognition of the limitations of AI-based approaches. In this article, the benefits, challenges, and drawbacks of AI in this field are reviewed, and possible strategies and approaches for overcoming the present obstacles are proposed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, as well as the potential advantages of AI in pharmaceutical research, are also discussed. Overall, this review highlights the potential of AI in drug discovery and provides insights into the challenges and opportunities for realizing its potential in this field. Note from the human authors: This article was created to test the ability of ChatGPT, a chatbot based on the GPT-3.5 language model, in terms of assisting human authors in writing review articles. The text generated by the AI following our instructions (see Supporting Information) was used as a starting point, and its ability to automatically generate content was evaluated. After conducting a thorough review, the human authors practically rewrote the manuscript, striving to maintain a balance between the original proposal and the scientific criteria. The advantages and limitations of using AI for this purpose are discussed in the last section.

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“…This raises several concerns over the social impact of AI, especially regarding how AI interacts with people (Shin 2023), how people perceive the credibility of AI (Shin 2022), and user awareness of privacy in AI-mediated environments (Shin et al 2022b). Similarly, consumers, businesses, and policymakers have all expressed concerns over the fairness of decisions made based on AI predictions (Shimao et al 2022;Shin et al 2022a). The current paper connects to this stream of research by proposing an AI model that can help retailers improve the distribution of essential products during periods when demand anomalies occur (e.g., during the panic-buying periods at the beginning of the COVID-19 pandemic).…”

Section: Social Impact Of Artificial Intelligencementioning

confidence: 99%

Using AI to detect panic buying and improve products distribution amid pandemic

Adulyasak

Benomar²,

Chaouachi³

et al. 2023

AI & Soc

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The COVID-19 pandemic has triggered panic-buying behavior around the globe. As a result, many essential supplies were consistently out-of-stock at common point-of-sale locations. Even though most retailers were aware of this problem, they were caught off guard and are still lacking the technical capabilities to address this issue. The primary objective of this paper is to develop a framework that can systematically alleviate this issue by leveraging AI models and techniques. We exploit both internal and external data sources and show that using external data enhances the predictability and interpretability of our model. Our data-driven framework can help retailers detect demand anomalies as they occur, allowing them to react strategically. We collaborate with a large retailer and apply our models to three categories of products using a dataset with more than 15 million observations. We first show that our proposed anomaly detection model can successfully detect anomalies related to panic buying. We then present a prescriptive analytics simulation tool that can help retailers improve essential product distribution in uncertain times. Using data from the March 2020 panic-buying wave, we show that our prescriptive tool can help retailers increase access to essential products by 56.74%.

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Strategic Best Response Fairness in Fair Machine Learning

Cited by 12 publications

References 0 publications

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies

Using AI to detect panic buying and improve products distribution amid pandemic

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