The Shelf brings big-data and machine-learning to the world of influencer/word-of-mouth marketing

The Shelf

Real business problems require serious custom solutions at a reasonable price. Businesses that invest in pertinent technology to make informed decisions and automate manual tasks always witness notable bottom-line growth. The market has many resources to provide the required solutions. However, each solution is designed keeping in mind the needs of a whole industry or collection of industries. The products have many features and capabilities. On the other hand, the needs of a business are usually very specific. They usually make use of a limited number of features that a provider offers. Too many features make it difficult to deliver efficiency. To quote an example, a large chain of departmental stores would require an extensive CRM software to keep track of the usage patterns its many customers. On the other hand, CRM needs a private cash-and-carry store would be limited to, say, managing contact details and email integration. In this case, buying the same software and paying the same price would be unfair and inefficient. Clearly, the best solution would be to pay for what you actually use, instead of everything that is offered by a software. Custom Solutions Some software designers offer to curate solutions to cater to the specific needs of the client. They understand the business problem at hand, restructure their product, and deliver a solution that the clients can actually use. When business interests are aligned in this manner, end users’ delight becomes possible. With the spread of cloud computing, several providers offer intuitive ready-made cloud-based pay-as-you-go-solutions. Businesses must ensure that they know how such solutions can be customized to suit their own needs. Simple solutions that actually solve problems are of much more value than fancy softwares that do not. They will meet customers’ expectations and they will have the functionalities that are actually useful. This will make work easy and infuse agility into an organization. At the end of the day, that is exactly what users want. Data Training In the artificial intelligence space, data is king. How good a solution is will depend on the kind of data it has been trained on. Its ‘ability to predict’ is directly influenced by ‘what it knows’. The nature of data which a solution is trained on determines how good the solution will be. Obviously, ready-made solutions are made using data which is available in the public domain. Such data is generic. Results from a solution which is trained on generic data are not useful for the specific needs of a business. However, there are quite a few AI based developers who train the algorithm on the client’s data. 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In this case, clients will find it easy to solve their problems because help will be readily available. This feature is very particular to custom software providers and is a major boon to their clients. How ParallelDots is leading in the custom solutions space ParallelDots’ Enterprise Services team has successfully deployed AI projects across domains and industries. As leaders in the deep learning and NLP sphere, we are bringing AI right to your fingertips. Various features of ParallelDots’ Enterprise Services: EXTRAORDINARY TEAM OF DATA SCIENTISTS The ParallelDots data science team is a collection of the best lot of techies, from across the country. There is a dedicated team for data tagging as well. The purpose is to build proprietary data that can be readily used whenever a client comes aboard. DEDICATED CUSTOMER SUCCESS MANAGER The intent is to deliver solutions that actually help clients make their work easy. For this purpose, a dedicated team of customer success managers makes sure that right from integration to pilot to deployment, all processes are smoothly handled. COST-EFFECTIVE SOLUTIONS The positioning of the products is cost-effective and the pricing policy is ‘pay-as-you-go’. Users are billed only for the excess usage beyond free tier quota. The focus is on making products affordable and best-of-class. PRIMACY TO DATA PRIVACY Privacy of client data is given top priority. All of the data used to train the algorithms is proprietary and licensed. More data is constantly being generated and tagged for use by different clients. Trusted by the best Major FMCG giants including ITC are currently using , the market research product of , Inc, to automate manual tasks and get valuable insights from their data. Karna.AI ParallelDots When it comes to finding the right custom solutions, custom software development has plenty of benefits worth considering. Is it the right choice for your company? Only you will be able to decide. But, if any of the reasons we have laid out clicked in your brain as something you need, we believe your business has a great opportunity to improve and continue moving forward through the utilization of custom software development. If you want to explore or know about the custom solutions then please fill the form or simply mail at contact@paralleldots.com. We will be happy to help you! here Read more about the recently launched Google Sheets add-on for all your text analytics needs . here We hope you liked the article. Please for a free ParallelDots account to start your AI journey. You can also check demo’s of PrallelDots AI APIs . Sign Up here This article has been published after the permission of the author. You can read the original article . here

Why custom solutions work better than off-the-shelf solutions for businesses

Human Augmentation in Meme Analysis: Learnings and Challenges

Authors:
(1) Vasiliki Kougia, University of Vienna, Faculty of computer science, Vienna, Austria & UniVie Doctoral School Computer Science, Vienna, Austria;
(2) Simon Fetze, University of Vienna, Faculty of computer science, Vienna, Austria;
(3) Thomas Kirchmair, University of Vienna, Faculty of computer science, Vienna, Austria;
(4) Erion Çano, University of Vienna, Faculty of computer science, Vienna, Austria;
(5) Sina Moayed Baharlou, Boston University, Department of Electrical and Computer Engineering, Boston, MA, USA;
(6) Sahand Sharifzadeh, Ludwig Maximilians University of Munich, Faculty of Computer Science, Munich, Germany;
(7) Benjamin Roth, University of Vienna, Faculty of computer science, Vienna, Austria. Table of Links Abstract and Introduction Related Work MemeGraphs Benchmarking Analysis Conclusion, Acknowledgments, and References 3 MemeGraphs In this section, we describe our method for automatic augmentation of a memes dataset, an approach we call MemeGraphs. Our proposed method consists of three steps: 1. Scene graph construction, 2. Knowledge linking to detected entities and 3. The construction of the final MemeGraphs input. The first two steps are performed automatically by using off-the-shelf models. Hence, the result of MemeGraphs are knowledge graphs representing the meme as a whole, which can be used to classify them, e.g., for hate detection. We build on the MultiOFF dataset, which contains memes extracted from social media during the 2016 U.S. Presidential Election [28]. In what follows, we present the three individual steps of MemeGraphs in detail and how the final result is constructed. 3.1 Scene graphs For 67 memes, Schemata did not produce any output. For the remaining 676 memes, 10,426 relations and 9,666 objects were detected in total. The number of unique objects and relations was 142 and 970 respectively. And while the number of objects detected in an image ranged from 2 to 16, the number of detected relations ranged from 1 to 40. The top 15 most frequently detected object and relation types are shown in Fig. 2. 3.2 Knowledge linking 3.3 Knowledge graph input [6] This number was chosen after observation of the scene graphs resulting from the memes in order to avoid having an overcrowded scene graph with multiple objects. [7] https://spacy.io/universe/project/spacy-transformers [8] https://www.wikidata.org/wiki/Wikidata:Main_Page [9] All the texts are concatenated with a full stop. This paper is available on arxiv under CC 4.0 license. Authors: (1) Vasiliki Kougia, University of Vienna, Faculty of computer science, Vienna, Austria & UniVie Doctoral School Computer Science, Vienna, Austria; (2) Simon Fetze, University of Vienna, Faculty of computer science, Vienna, Austria; (3) Thomas Kirchmair, University of Vienna, Faculty of computer science, Vienna, Austria; (4) Erion Çano, University of Vienna, Faculty of computer science, Vienna, Austria; (5) Sina Moayed Baharlou, Boston University, Department of Electrical and Computer Engineering, Boston, MA, USA; (6) Sahand Sharifzadeh, Ludwig Maximilians University of Munich, Faculty of Computer Science, Munich, Germany; (7) Benjamin Roth, University of Vienna, Faculty of computer science, Vienna, Austria. Authors: Authors: (1) Vasiliki Kougia, University of Vienna, Faculty of computer science, Vienna, Austria & UniVie Doctoral School Computer Science, Vienna, Austria; (2) Simon Fetze, University of Vienna, Faculty of computer science, Vienna, Austria; (3) Thomas Kirchmair, University of Vienna, Faculty of computer science, Vienna, Austria; (4) Erion Çano, University of Vienna, Faculty of computer science, Vienna, Austria; (5) Sina Moayed Baharlou, Boston University, Department of Electrical and Computer Engineering, Boston, MA, USA; (6) Sahand Sharifzadeh, Ludwig Maximilians University of Munich, Faculty of Computer Science, Munich, Germany; (7) Benjamin Roth, University of Vienna, Faculty of computer science, Vienna, Austria. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work MemeGraphs MemeGraphs Benchmarking Benchmarking Analysis Analysis Conclusion, Acknowledgments, and References Conclusion, Acknowledgments, and References 3 MemeGraphs In this section, we describe our method for automatic augmentation of a memes dataset, an approach we call MemeGraphs. Our proposed method consists of three steps: 1. Scene graph construction, 2. Knowledge linking to detected entities and 3. The construction of the final MemeGraphs input. The first two steps are performed automatically by using off-the-shelf models. Hence, the result of MemeGraphs are knowledge graphs representing the meme as a whole, which can be used to classify them, e.g., for hate detection. We build on the MultiOFF dataset, which contains memes extracted from social media during the 2016 U.S. Presidential Election [28]. In what follows, we present the three individual steps of MemeGraphs in detail and how the final result is constructed. 3.1 Scene graphs For 67 memes, Schemata did not produce any output. For the remaining 676 memes, 10,426 relations and 9,666 objects were detected in total. The number of unique objects and relations was 142 and 970 respectively. And while the number of objects detected in an image ranged from 2 to 16, the number of detected relations ranged from 1 to 40. The top 15 most frequently detected object and relation types are shown in Fig. 2. 3.2 Knowledge linking 3.3 Knowledge graph input [6] This number was chosen after observation of the scene graphs resulting from the memes in order to avoid having an overcrowded scene graph with multiple objects. [7] https://spacy.io/universe/project/spacy-transformers [8] https://www.wikidata.org/wiki/Wikidata:Main_Page [9] All the texts are concatenated with a full stop. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

MemeGraphs: Automated Augmentation for Hate Detection in Memes

Authors:
(1) Vasiliki Kougia, University of Vienna, Faculty of computer science, Vienna, Austria & UniVie Doctoral School Computer Science, Vienna, Austria;
(2) Simon Fetze, University of Vienna, Faculty of computer science, Vienna, Austria;
(3) Thomas Kirchmair, University of Vienna, Faculty of computer science, Vienna, Austria;
(4) Erion Çano, University of Vienna, Faculty of computer science, Vienna, Austria;
(5) Sina Moayed Baharlou, Boston University, Department of Electrical and Computer Engineering, Boston, MA, USA;
(6) Sahand Sharifzadeh, Ludwig Maximilians University of Munich, Faculty of Computer Science, Munich, Germany;
(7) Benjamin Roth, University of Vienna, Faculty of computer science, Vienna, Austria. Table of Links Abstract and Introduction Related Work MemeGraphs Benchmarking Analysis Conclusion, Acknowledgments, and References ABSTRACT Memes are a popular form of communicating trends and ideas in social media and on the internet in general, combining the modalities of images and text. They can express humor and sarcasm but can also have offensive content. Analyzing and classifying memes automatically is challenging since their interpretation relies on the understanding of visual elements, language, and background knowledge. Thus, it is important to meaningfully represent these sources and the interaction between them in order to classify a meme as a whole. In this work, we propose to use scene graphs, that express images in terms of objects and their visual relations, and knowledge graphs as structured representations for meme classification with a Transformer-based architecture. We compare our approach with ImgBERT, a multimodal model that uses only learned (instead of structured) representations of the meme, and observe consistent improvements. We further provide a dataset with human graph annotations that we compare to automatically generated graphs and entity linking. Analysis shows that automatic methods link more entities than human annotators and that automatically generated graphs are better suited for hatefulness classification in memes. Keywords hate speech · internet memes · knowledge graphs · multimodal representations. 1 Introduction Internet memes are items such as images, videos, or twitter posts that are widely shared on social media and typically relate to several subjects, such as politics, social, news, and current internet trends.[1] Memes are a popular form of communication and they are often used as a means to express an opinion or stance in a humorous or sarcastic manner, but they can also be hateful and promote problematic content that is likely to hurt specific groups of people and hence be harmful to society in general [2]. Thus, analyzing trending memes can provide insight into people’s reactions and opinions to important societal matters, as well as to the traits of different groups. This can help in tasks like filtering out harmful memes from internet platforms or extracting user opinions for socioeconomic studies. This work focuses on memes in the form of images with some form of superimposed text (sometimes referred to with the technical term image macros) with the goal of detecting hateful content. Lately, there has been increased interest in deep learning models for analyzing memes and classifying them [23, 10, 11], for example in the context of the Hateful Memes competition organized by Facebook [13] and the shared task in the Workshop on Online Abuse and Harms (WOAH) 2021 that was the continuation of the first competition [21]. Another shared task in Semeval 2022 aimed at detecting misogyny in memes [11]. Alongside these shared tasks, the corresponding datasets were published with human annotations capturing important properties such as hatefulness and misogyny. Recent works use multimodal representation learning based on image features from Convolutional Neural Networks (CNNs), Transformer-based language models for the text [13, 3, 14] or multimodal (vision+language) Transformer models in order to classify memes [13]. Some works additionally incorporate specifically extracted image  features such as race [33], person attributes [1, 23], and automatic image captioning output [7, 4]. However, none of these works have included the relations between objects in the form of scene graphs or background knowledge in the form of a text description for the objects depicted in the image. In this paper, we address the issue of classifying hateful memes by performing an automated augmentation in order to represent the visual information and background knowledge (Fig. 1). We built on the MultiOFF dataset, which contains memes extracted from social media during the 2016 U.S. Presidential Election [28]. An off-the-shelf scene graph generation model was employed to produce scene graphs, which contain detected visual objects and relations between them for each meme, and a NER model to detect entities that we linked to background knowledge. The scene graphs were serialized as text resulting in a unified way to represent all the modalities expressed in a meme. This allows using a (unimodal) text classifier to classify the multimodal memes. Hence, we incorporated the automatically produced augmentations to classify the memes using a text-based Transformer model and show that they can improve its performance. The explicit representation of the image as serialized tokens also provides a more interpretable intermediate representation (compared to hidden layers in multimodal models such as ImgBERT [13, 14]). Furthermore, in order to examine how the results of the automatic augmentation would deviate from human ones, we performed manual augmentation. Two human evaluators corrected the automatically produced scene graphs and manually added background knowledge. We compare our automatic MemeGraphs method with models operating on the manually augmented data (only for training or for both training and inference) and find that automatic augmentations assist in achieving better results than manual ones. Contribution Our contributions can be summarized as follows: • We propose MemeGraphs, a novel method for classifying memes utilizing scene graphs augmented with knowledge and providing insights for processing multimodal documents. • We show that adding this kind of knowledge to a text-based Transformer model can improve its classification performance. Furthermore, we show that this yields improvements compared to a simple model only using learned representations to classify the memes, such as ImgBERT. • We conduct extensive experiments with manual and automatic settings for obtaining this knowledge and show that the automatic setting of our MemeGraphs method provides more meaningful information. In the following, we discuss related work and present our MemeGraphs method. Subsequently, in Section 4, we describe the models we implemented and report their results. Finally, we provide a qualitative analysis, including a discussion of the findings of human augmentation, and compare this with the automatic MemeGraphs method.[2] [1] Disclaimer: This paper contains examples of hateful content. [2] The code and data for the MemeGraphs method are available on: https://github.com/vasilikikou/memegraphs. This paper is available on arxiv under CC 4.0 license. Authors: (1) Vasiliki Kougia, University of Vienna, Faculty of computer science, Vienna, Austria & UniVie Doctoral School Computer Science, Vienna, Austria; (2) Simon Fetze, University of Vienna, Faculty of computer science, Vienna, Austria; (3) Thomas Kirchmair, University of Vienna, Faculty of computer science, Vienna, Austria; (4) Erion Çano, University of Vienna, Faculty of computer science, Vienna, Austria; (5) Sina Moayed Baharlou, Boston University, Department of Electrical and Computer Engineering, Boston, MA, USA; (6) Sahand Sharifzadeh, Ludwig Maximilians University of Munich, Faculty of Computer Science, Munich, Germany; (7) Benjamin Roth, University of Vienna, Faculty of computer science, Vienna, Austria. Authors: Authors: (1) Vasiliki Kougia, University of Vienna, Faculty of computer science, Vienna, Austria & UniVie Doctoral School Computer Science, Vienna, Austria; (2) Simon Fetze, University of Vienna, Faculty of computer science, Vienna, Austria; (3) Thomas Kirchmair, University of Vienna, Faculty of computer science, Vienna, Austria; (4) Erion Çano, University of Vienna, Faculty of computer science, Vienna, Austria; (5) Sina Moayed Baharlou, Boston University, Department of Electrical and Computer Engineering, Boston, MA, USA; (6) Sahand Sharifzadeh, Ludwig Maximilians University of Munich, Faculty of Computer Science, Munich, Germany; (7) Benjamin Roth, University of Vienna, Faculty of computer science, Vienna, Austria. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work MemeGraphs MemeGraphs Benchmarking Benchmarking Analysis Analysis Conclusion, Acknowledgments, and References Conclusion, Acknowledgments, and References ABSTRACT Memes are a popular form of communicating trends and ideas in social media and on the internet in general, combining the modalities of images and text. They can express humor and sarcasm but can also have offensive content. Analyzing and classifying memes automatically is challenging since their interpretation relies on the understanding of visual elements, language, and background knowledge. Thus, it is important to meaningfully represent these sources and the interaction between them in order to classify a meme as a whole. In this work, we propose to use scene graphs, that express images in terms of objects and their visual relations, and knowledge graphs as structured representations for meme classification with a Transformer-based architecture. We compare our approach with ImgBERT, a multimodal model that uses only learned (instead of structured) representations of the meme, and observe consistent improvements. We further provide a dataset with human graph annotations that we compare to automatically generated graphs and entity linking. Analysis shows that automatic methods link more entities than human annotators and that automatically generated graphs are better suited for hatefulness classification in memes. Keywords hate speech · internet memes · knowledge graphs · multimodal representations. Keywords Keywords 1 Introduction Internet memes are items such as images, videos, or twitter posts that are widely shared on social media and typically relate to several subjects, such as politics, social, news, and current internet trends.[1] Memes are a popular form of communication and they are often used as a means to express an opinion or stance in a humorous or sarcastic manner, but they can also be hateful and promote problematic content that is likely to hurt specific groups of people and hence be harmful to society in general [2]. Thus, analyzing trending memes can provide insight into people’s reactions and opinions to important societal matters, as well as to the traits of different groups. This can help in tasks like filtering out harmful memes from internet platforms or extracting user opinions for socioeconomic studies. This work focuses on memes in the form of images with some form of superimposed text (sometimes referred to with the technical term image macros) with the goal of detecting hateful content. Lately, there has been increased interest in deep learning models for analyzing memes and classifying them [23, 10, 11], for example in the context of the Hateful Memes competition organized by Facebook [13] and the shared task in the Workshop on Online Abuse and Harms (WOAH) 2021 that was the continuation of the first competition [21]. Another shared task in Semeval 2022 aimed at detecting misogyny in memes [11]. Alongside these shared tasks, the corresponding datasets were published with human annotations capturing important properties such as hatefulness and misogyny. Recent works use multimodal representation learning based on image features from Convolutional Neural Networks (CNNs), Transformer-based language models for the text [13, 3, 14] or multimodal (vision+language) Transformer models in order to classify memes [13]. Some works additionally incorporate specifically extracted image  features such as race [33], person attributes [1, 23], and automatic image captioning output [7, 4]. However, none of these works have included the relations between objects in the form of scene graphs or background knowledge in the form of a text description for the objects depicted in the image. In this paper, we address the issue of classifying hateful memes by performing an automated augmentation in order to represent the visual information and background knowledge (Fig. 1). We built on the MultiOFF dataset, which contains memes extracted from social media during the 2016 U.S. Presidential Election [28]. An off-the-shelf scene graph generation model was employed to produce scene graphs, which contain detected visual objects and relations between them for each meme, and a NER model to detect entities that we linked to background knowledge. The scene graphs were serialized as text resulting in a unified way to represent all the modalities expressed in a meme. This allows using a (unimodal) text classifier to classify the multimodal memes. Hence, we incorporated the automatically produced augmentations to classify the memes using a text-based Transformer model and show that they can improve its performance. The explicit representation of the image as serialized tokens also provides a more interpretable intermediate representation (compared to hidden layers in multimodal models such as ImgBERT [13, 14]). Furthermore, in order to examine how the results of the automatic augmentation would deviate from human ones, we performed manual augmentation. Two human evaluators corrected the automatically produced scene graphs and manually added background knowledge. We compare our automatic MemeGraphs method with models operating on the manually augmented data (only for training or for both training and inference) and find that automatic augmentations assist in achieving better results than manual ones. Contribution Our contributions can be summarized as follows: Contribution • We propose MemeGraphs, a novel method for classifying memes utilizing scene graphs augmented with knowledge and providing insights for processing multimodal documents. • We show that adding this kind of knowledge to a text-based Transformer model can improve its classification performance. Furthermore, we show that this yields improvements compared to a simple model only using learned representations to classify the memes, such as ImgBERT. • We conduct extensive experiments with manual and automatic settings for obtaining this knowledge and show that the automatic setting of our MemeGraphs method provides more meaningful information. In the following, we discuss related work and present our MemeGraphs method. Subsequently, in Section 4, we describe the models we implemented and report their results. Finally, we provide a qualitative analysis, including a discussion of the findings of human augmentation, and compare this with the automatic MemeGraphs method.[2] [1] Disclaimer: This paper contains examples of hateful content. [2] The code and data for the MemeGraphs method are available on: https://github.com/vasilikikou/memegraphs. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

MemeGraphs: Linking Memes to Knowledge Graphs

Authors:
(1) Rui Cao, Singapore Management University;
(2) Ming Shan Hee, Singapore University of Design and Technology;
(3) Adriel Kuek, DSO National Laboratories;
(4) Wen-Haw Chong, Singapore Management University;
(5) Roy Ka-Wei Lee, Singapore University of Design and Technology
(6) Jing Jiang, Singapore Management University. Table of Links Abstract and Introduction Related Work Preliminary Proposed Method Experiment Conclusion and References Appendix ABSTRACT Hateful meme detection is a challenging multimodal task that requires comprehension of both vision and language, as well as cross-modal interactions. Recent studies have tried to fine-tune pre-trained vision-language models (PVLMs) for this task. However, with increasing model sizes, it becomes important to leverage powerful PVLMs more efficiently, rather than simply fine-tuning them. Recently, researchers have attempted to convert meme images into textual captions and prompt language models for predictions. This approach has shown good performance but suffers from noninformative image captions. Considering the two factors mentioned above, we propose a probing-based captioning approach to leverage PVLMs in a zero-shot visual question answering (VQA) manner. Specifically, we prompt a frozen PVLM by asking hateful contentrelated questions and use the answers as image captions (which we call Pro-Cap), so that the captions contain information critical for hateful content detection. The good performance of models with Pro-Cap on three benchmarks validates the effectiveness and generalization of the proposed method.[1] CCS CONCEPTS • Computing methodologies → Natural language processing; Computer vision representations. KEYWORDS memes, multimodal, semantic extraction ACM Reference Format: Rui Cao, Ming Shan Hee, Adriel Kuek, Wen-Haw Chong, Roy Ka-Wei Lee, and Jing Jiang. 2023. Pro Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection. In Proceedings of the 31st ACM International Conference on Multimedia (MM ’23), October 29-November 3, 2023, Ottawa, ON, Canada. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3581783.3612498 Disclaimer: This paper contains violence and discriminatory content that may be disturbing to some readers. 1 INTRODUCTION Memes, which combine images with short texts, are a popular form of communication in online social media. Internet memes are often intended to express humor or satire. However, they are increasingly being exploited to spread hateful content across online platforms. Hateful memes attack individuals or communities based on their identities such as race, gender, or religion [5, 8, 12, 27]. The propagation of hateful memes can lead to discord online and may potentially result in hate crimes. Therefore, it is urgent to develop accurate hateful meme detection methods. The task of hateful meme detection is challenging due to the multimodal nature of memes. Detection involves not only comprehending both the images and the texts but also understanding how these two modalities interact. Previous work [14, 28, 35, 36] learns cross-modal interactions from scratch using hateful meme detection datasets. However, it may be difficult for models to learn complicated multimodal interactions with the limited amount of data available from these datasets. With the development of Pretrained Vision-Language Models (PVLMs) such as VisualBERT [18] and ViLBERT [23], recent work leverage these powerful PVLMs to facilitate the hateful meme detection task. A common approach is to fine-tune PVLMs with task-specific data [9, 20, 26, 34, 37]. However, it is less feasible to fine-tune the larger models such as BLIP-2 [15] and Flamingo [1] on meme detection because there are billions of trainable parameters. Therefore, computationally feasible solutions other than direct fine-tuning are needed to leverage large PVLMs in facilitating hateful meme detection. Different from the approach above using PVLMs, PromptHate[2] is a recently proposed model that converts the multimodal meme detection task into a unimodal masked language modeling task. It first generates meme image captions with an off-the-shelf image caption generator, ClipCap [25]. By converting all input information into text, it can prompt a pre-trained language model along with two demonstrative examples to predict whether or not the input is hateful by leveraging the rich background knowledge in the language model. Although PromptHate achieves state-of-the-art performance, it is significantly affected by the quality of image captions, as shown in Table 1. Image captions that are merely generic descriptions of images may omit crucial details [14, 37], such as the race and gender of people, which are essential for hateful content detection. But with additional image tags, such as entities found in the images and demographic information about the people in the images, the same model can be significantly improved, as shown in Table 1. However, generating these additional image tags is laborious and costly. For instance, entity extraction is usually conducted with the Google Vision Web Entity Detection API [2], which is a paid service. Ideally, we would like to find a more affordable way to obtain entity and demographic information from the images that is critical for hateful content detection. Both above-mentioned approaches (i.e., one using PVLMs and the other converting the task to a unimodal task) have their pros and cons. In this paper, we combine the ideas from these two approaches and design a hateful meme detection method that leverages the power of a frozen PVLM to complement the unimodal approach of PromptHate. Specifically, we use a set of “probing” questions to query a PVLM (BLIP-2 [15] in our experiments) for information related to common vulnerable targets in hateful content. The answers obtained from the probing questions will be treated as image captions (denoted as Pro-Cap) and used as input to a trainable hateful meme detection model. Figure 1 illustrates the overall workflow of the method. We refer to the step of using probing questions to generate the captions as probing-based captioning. Our proposed method fills existing research gaps by: 1) Leverage a PVLM without any adaptation or fine-tuning, thereby reducing computational cost; 2) Instead of explicitly obtaining additional image tags with costly APIs, we utilize the frozen PVLM to generate captions that contain information useful for hateful meme detection. To the best of our knowledge, this is the first work that to leverage PVLMs in a zero-shot manner through question answering to assist in the hateful meme detection task. To further validate our method, we test the effect of the generated Pro-Cap on both PromptHate[2] and a BERT-based[4] hateful meme detection model. Based on the experimental results, we observe that PromptHate with Pro-Cap (denoted as Pro-CapPromptHate) significantly surpasses the original PromptHate without additional image tags (i.e., about 4, 6, and 3 percentage points of absolute performance improvement on FHM [12], MAMI [5], and HarM [28] respectively). ProCapPromptHate also achieves comparable results with PromptHate with additional image tags, indicating that probing-based captioning can be a more affordable way of obtaining image entities or demographic information. Case studies further show that Pro-Cap offers essential image details for hateful content detection, enhancing the explainability of models to some extent. Meanwhile, ProCapBERT clearly surpasses multimodal BERT-based models of similar sizes (i.e., about 7 percentage points of absolute improvement with VisualBERT on FHM [12]), proving the generalization of the probing-based captioning method. [1] Code is available at: https://github.com/Social-AI-Studio/Pro-Cap [2] https://cloud.google.com/vision/docs/detecting-web This paper is available on arxiv under CC 4.0 license. Authors: (1) Rui Cao, Singapore Management University; (2) Ming Shan Hee, Singapore University of Design and Technology; (3) Adriel Kuek, DSO National Laboratories; (4) Wen-Haw Chong, Singapore Management University; (5) Roy Ka-Wei Lee, Singapore University of Design and Technology (6) Jing Jiang, Singapore Management University. Authors: Authors: (1) Rui Cao, Singapore Management University; (2) Ming Shan Hee, Singapore University of Design and Technology; (3) Adriel Kuek, DSO National Laboratories; (4) Wen-Haw Chong, Singapore Management University; (5) Roy Ka-Wei Lee, Singapore University of Design and Technology (6) Jing Jiang, Singapore Management University. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Preliminary Preliminary Proposed Method Proposed Method Experiment Experiment Conclusion and References Conclusion and References Appendix Appendix ABSTRACT Hateful meme detection is a challenging multimodal task that requires comprehension of both vision and language, as well as cross-modal interactions. Recent studies have tried to fine-tune pre-trained vision-language models (PVLMs) for this task. However, with increasing model sizes, it becomes important to leverage powerful PVLMs more efficiently, rather than simply fine-tuning them. Recently, researchers have attempted to convert meme images into textual captions and prompt language models for predictions. This approach has shown good performance but suffers from noninformative image captions. Considering the two factors mentioned above, we propose a probing-based captioning approach to leverage PVLMs in a zero-shot visual question answering (VQA) manner. Specifically, we prompt a frozen PVLM by asking hateful contentrelated questions and use the answers as image captions (which we call Pro-Cap), so that the captions contain information critical for hateful content detection. The good performance of models with Pro-Cap on three benchmarks validates the effectiveness and generalization of the proposed method.[1] CCS CONCEPTS • Computing methodologies → Natural language processing; Computer vision representations. • Computing methodologies → Natural language processing; Computer vision representations. KEYWORDS memes, multimodal, semantic extraction ACM Reference Format: Rui Cao, Ming Shan Hee, Adriel Kuek, Wen-Haw Chong, Roy Ka-Wei Lee, and Jing Jiang. 2023. Pro Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection. In Proceedings of the 31st ACM International Conference on Multimedia (MM ’23), October 29-November 3, 2023, Ottawa, ON, Canada. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3581783.3612498 Disclaimer: This paper contains violence and discriminatory content that may be disturbing to some readers. Disclaimer: This paper contains violence and discriminatory content that may be disturbing to some readers. Disclaimer: This paper contains violence and discriminatory content that may be disturbing to some readers. 1 INTRODUCTION Memes, which combine images with short texts, are a popular form of communication in online social media. Internet memes are often intended to express humor or satire. However, they are increasingly being exploited to spread hateful content across online platforms. Hateful memes attack individuals or communities based on their identities such as race, gender, or religion [5, 8, 12, 27]. The propagation of hateful memes can lead to discord online and may potentially result in hate crimes. Therefore, it is urgent to develop accurate hateful meme detection methods. The task of hateful meme detection is challenging due to the multimodal nature of memes. Detection involves not only comprehending both the images and the texts but also understanding how these two modalities interact. Previous work [14, 28, 35, 36] learns cross-modal interactions from scratch using hateful meme detection datasets. However, it may be difficult for models to learn complicated multimodal interactions with the limited amount of data available from these datasets. With the development of Pretrained Vision-Language Models (PVLMs) such as VisualBERT [18] and ViLBERT [23], recent work leverage these powerful PVLMs to facilitate the hateful meme detection task. A common approach is to fine-tune PVLMs with task-specific data [9, 20, 26, 34, 37]. However, it is less feasible to fine-tune the larger models such as BLIP-2 [15] and Flamingo [1] on meme detection because there are billions of trainable parameters. Therefore, computationally feasible solutions other than direct fine-tuning are needed to leverage large PVLMs in facilitating hateful meme detection. Different from the approach above using PVLMs, PromptHate[2] is a recently proposed model that converts the multimodal meme detection task into a unimodal masked language modeling task. It first generates meme image captions with an off-the-shelf image caption generator, ClipCap [25]. By converting all input information into text, it can prompt a pre-trained language model along with two demonstrative examples to predict whether or not the input is hateful by leveraging the rich background knowledge in the language model. Although PromptHate achieves state-of-the-art performance, it is significantly affected by the quality of image captions, as shown in Table 1. Image captions that are merely generic descriptions of images may omit crucial details [14, 37], such as the race and gender of people, which are essential for hateful content detection. But with additional image tags, such as entities found in the images and demographic information about the people in the images, the same model can be significantly improved, as shown in Table 1. However, generating these additional image tags is laborious and costly. For instance, entity extraction is usually conducted with the Google Vision Web Entity Detection API [2], which is a paid service. Ideally, we would like to find a more affordable way to obtain entity and demographic information from the images that is critical for hateful content detection. Both above-mentioned approaches (i.e., one using PVLMs and the other converting the task to a unimodal task) have their pros and cons. In this paper, we combine the ideas from these two approaches and design a hateful meme detection method that leverages the power of a frozen PVLM to complement the unimodal approach of PromptHate. Specifically, we use a set of “probing” questions to query a PVLM (BLIP-2 [15] in our experiments) for information related to common vulnerable targets in hateful content. The answers obtained from the probing questions will be treated as image captions (denoted as Pro-Cap) and used as input to a trainable hateful meme detection model. Figure 1 illustrates the overall workflow of the method. We refer to the step of using probing questions to generate the captions as probing-based captioning. Our proposed method fills existing research gaps by: 1) Leverage a PVLM without any adaptation or fine-tuning, thereby reducing computational cost; 2) Instead of explicitly obtaining additional image tags with costly APIs, we utilize the frozen PVLM to generate captions that contain information useful for hateful meme detection. To the best of our knowledge, this is the first work that to leverage PVLMs in a zero-shot manner through question answering to assist in the hateful meme detection task. To further validate our method, we test the effect of the generated Pro-Cap on both PromptHate[2] and a BERT-based[4] hateful meme detection model. Based on the experimental results, we observe that PromptHate with Pro-Cap (denoted as Pro-CapPromptHate) significantly surpasses the original PromptHate without additional image tags (i.e., about 4, 6, and 3 percentage points of absolute performance improvement on FHM [12], MAMI [5], and HarM [28] respectively). ProCapPromptHate also achieves comparable results with PromptHate with additional image tags, indicating that probing-based captioning can be a more affordable way of obtaining image entities or demographic information. Case studies further show that Pro-Cap offers essential image details for hateful content detection, enhancing the explainability of models to some extent. Meanwhile, ProCapBERT clearly surpasses multimodal BERT-based models of similar sizes (i.e., about 7 percentage points of absolute improvement with VisualBERT on FHM [12]), proving the generalization of the probing-based captioning method. [1] Code is available at: https://github.com/Social-AI-Studio/Pro-Cap [2] https://cloud.google.com/vision/docs/detecting-web This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection

প্রো-ক্যাপ: বিদ্বেষপূর্ণ মেম সনাক্তকরণের জন্য একটি হিমায়িত দৃষ্টি-ভাষা মডেল ব্যবহার করা

Pro-Cap: Nutzung eines Frozen Vision-Language-Modells zur Erkennung hasserfüllter Memes

Pro-Cap: Aprovechamiento de un modelo de visión-lenguaje congelado para la detección de memes de odio

Pro-Cap : exploiter un modèle de langage de vision figé pour la détection des mèmes haineux

प्रो-कैप: घृणास्पद मीम का पता लगाने के लिए फ्रोज़न विज़न-लैंग्वेज मॉडल का लाभ उठाना

Pro-Cap: 凍結された視覚言語モデルを活用して憎悪的なミームを検出する

Pro-Cap: 혐오스러운 밈 탐지를 위해 고정된 비전-언어 모델 활용

Pro-Cap: aproveitando um modelo de linguagem de visão congelada para detecção de memes odiosos

Pro-Cap: использование модели Frozen Vision-Language для обнаружения ненавистных мемов

Pro-Cap: Nefret Dolu Memlerin Tespiti için Donmuş Görüş-Dil Modelinden Yararlanmak

Pro-Cap: Tận dụng mô hình ngôn ngữ tầm nhìn đông lạnh để phát hiện meme đáng ghét

Pro-Cap：利用冻结视觉语言模型进行仇恨模因检测

Performance Analysis of Diverse Hateful Meme Detection Datasets

From Chat-Bots to Killer-Bots?

Governance, Risk, and Compliance (GRC) policies in digital business encompass integrated practices that ensure that your processes align with regulations, industry standards, and internal policies. Most commonly it refers to governance structures, risk management, and compliance measures. The regulatory part of the digital landscape evolves dynamically and leans towards tightening measures, at the same time, risks and error costs continue to rise. This is why creating a specific GRC strategy is essential for digital businesses. In this article, we will talk about key components and best practices for implementing a successful and suitable GRC strategy for a digital business. Key Components of an Effective GRC Strategy GRC is a complex and nuanced domain where the same strategy may work perfectly for one organization and be ruinous for another. However, there are common guidelines to follow, which allow for the creation of a successful GRC strategy that is tailored to your specific business needs. Establishing governance frameworks Governance frameworks play a vital role in defining responsibilities within your organization and empowering decision-makers at different levels. This typically involves clearly outlining roles and responsibilities, creating relevant documentation and company regulations, aligning these regulations with business goals and ensuring all employees are familiar with them. Identifying and Assessing Risks Risk management is a crucial aspect of a GRC strategy for various reasons. Conducting proper risk assessments and pondering their mitigation can save businesses significant costs by preventing potential accidents, rather than dealing with their consequences. For effectiveness, risk assessments need to focus on high-impact risks, and be conducted regularly and systematically. You can use AI tools to enhance risk analysis and assessment by identifying potential blind spots, gaps and increased human factor-related risks. This proactive approach helps in safeguarding the organization against potential threats and ensures resilience and sustainability. Implementing Compliance Programs Implementing compliance programs is about ensuring that the organization adheres to relevant laws, regulations, standards, and ethical practices. The important factor here is not to make these processes inflexible which renders them unable to accommodate frequent and ever changing legal requirements . It is recommended not only to consider the current regulations but also stay alert for forthcoming ones, preparing for them in advance and gradually, to ensure that any changes don't come as sudden disruptions for your company. Integrating GRC into Organizational Culture Integrating GRC into organizational culture is essential. Merely creating a GRC policy is insufficient if it isn’t integrated into daily routines and behaviors of employees at all levels of your organization. Only when GRC becomes a part of the organizational DNA does it become truly effective. The key point here is to understand how regulations translate into actual activities. Aligning GRC Efforts with Business Objectives A GRC strategy should never be detached from your company’s business goals, mission and values. In fact, it should actually support and add value to them. This alignment means that setting GRC priorities needs to be directly linked to key business objectives, ensuring resources are focused on areas of highest impact. 8 Steps to a Successful GRC Strategy Now, from theory to practice. The following steps aid us and our clients in developing a functional GRC strategy that doesn’t deplete the security budget, remains transparent for performance assessment and is scalable and flexible. Decide on the use of GRC Platform This is where it is absolutely crucial to do your homework. Research and compare off-the-shelf solutions that are available on the market from the point of view of whether or not they cover all your GRC needs and determine if additional complementary tools are needed. Sometimes, it is worth investing in a custom GRC solution that is tailored to fit your particular GRC demands rather than try and make do with generic solutions that may leave certain areas uncovered. Consult with compliance and GRC experts and involve your solution architects in the decision-making process to select the best option. Create a GRC Project Roadmap Set your priorities and present them in the form of defined milestones and timelines. Make sure they are realistic and are broken down into manageable phases. Then assign responsibilities for the tasks on the timeline you’ve created. Make them transparent and avoid situations where too many cooks can spoil the broth. Communicate the plan to everyone involved. Perform a Gap Analysis This stage involved identifying the existing GRC processes and evaluating them against your roadmap objectives. Then compare your current practices against industry standards, regulatory requirements, and best practices. Once you’ve identified the gaps, prioritize them based on their risk and impact on your business, and plan actions to address them. Determine and Align Stakeholder Expectations Stakeholder engagement is essential for carrying out effective GRC policies. Make sure it starts at the earliest stages. If you have to (and you probably will), plan meetings to discuss and align on the GRC strategy's goals, priorities, and outcomes. Don’t stop there, you need to keep the stakeholders in the loop of all the GRC-related developments, gather and analyze their feedback and ensure their active participation in decision-making. Define the Objectives Use the SMART objectives-setting technique. SMART stands for Specific, Measurable, Achievable, Relevant, and Time-bound and this abbreviation perfectly reflects the qualities that should define your GRC tasks. Incorporating regular reviews and feedback loops into GRC objectives is crucial for ensuring their effectiveness. Conduct a Comprehensive Organizational Risk Assessment Use a structured framework to conduct risk assessment activities. Another great tip here would be to maximize the use of existing resources such as leveraging the cross-functional expertise within your team. You may already have a compliance expert within your organization potentially working on other projects. Keep a comprehensive log or a documentation of all identified risks with set priorities and mitigation options. Standardization Standardization is key to maintaining consistency and effectiveness in your GRC policies. By standardizing procedures, training, and onboarding processes, you ensure that rules are interpreted uniformly across the organization. Develop a comprehensive set of GRC training materials and procedures making it mandatory for employees to be familiar with them.  Consider incorporating GRC clauses into employment contracts to reinforce compliance. Additionally, adopt and implement industry best practices tailored to your business structure. Conduct regular training and workshops to increase awareness of your GRC policies and keep employees updated on its developments. Management and Revision The process of refining your GRC policies is ongoing. This ensures its scalability, flexibility and alignment with industry standards. So, monitor it, conduct performance checks, gather feedback and identify improvement areas on a regular basis. Best Practices in Creating a Working GRC Policies Besides understanding the steps for creating and implementing GRC policies tailored to your business, here are some tips to ensure its effective execution. Starting small, simple, and focused No need to roll-out a huge-scale system that will complicate matters for your company. Start small, try out, build on it and expand gradually. Keep the focus on high-impact factors. Leadership commitment Cultivate initiative within your leadership by openly communicating GRC principles throughout the organization. Emphasize that these principles are fundamental to the company’s long-term success and reputation. Prioritize risk and compliance activities At an enterprise level, this is what quantifies the value achieved at each stage of your GRC policy implementation. Engage your employees This is where you can get creative. It could be interactive training sessions on ethical decision-making, workshops on identifying and mitigating risks in your workers’ operations, and forums for discussing compliance challenges. Do not forget to encourage feedback and show you value it. Introduce proper incentives You can revise your company’s performance appraisal system to include GRC-related metrics. Your employees can be rewarded not just for achieving business targets but also for demonstrating adherence to compliance standards, ethical behavior, and effective risk management in their tasks. Conclusion Well-planned and implemented GRC strategy is crucial for success in today's digital business landscape. By establishing a robust operational framework, organizations can achieve regulatory compliance, effectively manage risks, improve efficiency, safeguard their reputation, and make informed strategic decisions. Furthermore, fostering a culture of GRC awareness among stakeholders promotes a sense of collective responsibility and accountability, encouraging an environment of cohesion and resilience. Governance, Risk, and Compliance (GRC) policies in digital business encompass integrated practices that ensure that your processes align with regulations, industry standards, and internal policies. Most commonly it refers to governance structures, risk management, and compliance measures. The regulatory part of the digital landscape evolves dynamically and leans towards tightening measures, at the same time, risks and error costs continue to rise. This is why creating a specific GRC strategy is essential for digital businesses. In this article, we will talk about key components and best practices for implementing a successful and suitable GRC strategy for a digital business. Key Components of an Effective GRC Strategy GRC is a complex and nuanced domain where the same strategy may work perfectly for one organization and be ruinous for another. However, there are common guidelines to follow, which allow for the creation of a successful GRC strategy that is tailored to your specific business needs. Establishing governance frameworks Establishing governance frameworks Establishing governance frameworks Governance frameworks play a vital role in defining responsibilities within your organization and empowering decision-makers at different levels. This typically involves clearly outlining roles and responsibilities, creating relevant documentation and company regulations, aligning these regulations with business goals and ensuring all employees are familiar with them. Identifying and Assessing Risks Identifying and Assessing Risks Identifying and Assessing Risks Identifying and Assessing Risks Risk management is a crucial aspect of a GRC strategy for various reasons. Conducting proper risk assessments and pondering their mitigation can save businesses significant costs by preventing potential accidents, rather than dealing with their consequences. For effectiveness, risk assessments need to focus on high-impact risks, and be conducted regularly and systematically. You can use AI tools to enhance risk analysis and assessment by identifying potential blind spots, gaps and increased human factor-related risks. This proactive approach helps in safeguarding the organization against potential threats and ensures resilience and sustainability. Implementing Compliance Programs Implementing Compliance Programs Implementing Compliance Programs Implementing compliance programs is about ensuring that the organization adheres to relevant laws, regulations, standards, and ethical practices. The important factor here is not to make these processes inflexible which renders them unable to accommodate frequent and ever changing legal requirements . It is recommended not only to consider the current regulations but also stay alert for forthcoming ones, preparing for them in advance and gradually, to ensure that any changes don't come as sudden disruptions for your company. Integrating GRC into Organizational Culture Integrating GRC into Organizational Culture Integrating GRC into Organizational Culture Integrating GRC into organizational culture is essential. Merely creating a GRC policy is insufficient if it isn’t integrated into daily routines and behaviors of employees at all levels of your organization. Only when GRC becomes a part of the organizational DNA does it become truly effective. The key point here is to understand how regulations translate into actual activities. Aligning GRC Efforts with Business Objectives Aligning GRC Efforts with Business Objectives Aligning GRC Efforts with Business Objectives A GRC strategy should never be detached from your company’s business goals, mission and values. In fact, it should actually support and add value to them. This alignment means that setting GRC priorities needs to be directly linked to key business objectives, ensuring resources are focused on areas of highest impact. 8 Steps to a Successful GRC Strategy Now, from theory to practice. The following steps aid us and our clients in developing a functional GRC strategy that doesn’t deplete the security budget, remains transparent for performance assessment and is scalable and flexible. Decide on the use of GRC Platform Decide on the use of GRC Platform Decide on the use of GRC Platform This is where it is absolutely crucial to do your homework. Research and compare off-the-shelf solutions that are available on the market from the point of view of whether or not they cover all your GRC needs and determine if additional complementary tools are needed. Sometimes, it is worth investing in a custom GRC solution that is tailored to fit your particular GRC demands rather than try and make do with generic solutions that may leave certain areas uncovered. Consult with compliance and GRC experts and involve your solution architects in the decision-making process to select the best option. Create a GRC Project Roadmap Create a GRC Project Roadmap Create a GRC Project Roadmap Set your priorities and present them in the form of defined milestones and timelines. Make sure they are realistic and are broken down into manageable phases. Then assign responsibilities for the tasks on the timeline you’ve created. Make them transparent and avoid situations where too many cooks can spoil the broth. Communicate the plan to everyone involved. Perform a Gap Analysis Perform a Gap Analysis Perform a Gap Analysis This stage involved identifying the existing GRC processes and evaluating them against your roadmap objectives. Then compare your current practices against industry standards, regulatory requirements, and best practices. Once you’ve identified the gaps, prioritize them based on their risk and impact on your business, and plan actions to address them. Determine and Align Stakeholder Expectations Determine and Align Stakeholder Expectations Determine and Align Stakeholder Expectations Stakeholder engagement is essential for carrying out effective GRC policies. Make sure it starts at the earliest stages. If you have to (and you probably will), plan meetings to discuss and align on the GRC strategy's goals, priorities, and outcomes. Don’t stop there, you need to keep the stakeholders in the loop of all the GRC-related developments, gather and analyze their feedback and ensure their active participation in decision-making. Define the Objectives Define the Objectives Define the Objectives Use the SMART objectives-setting technique. SMART stands for Specific, Measurable, Achievable, Relevant, and Time-bound and this abbreviation perfectly reflects the qualities that should define your GRC tasks. Incorporating regular reviews and feedback loops into GRC objectives is crucial for ensuring their effectiveness. Conduct a Comprehensive Organizational Risk Assessment Conduct a Comprehensive Organizational Risk Assessment Conduct a Comprehensive Organizational Risk Assessment Use a structured framework to conduct risk assessment activities. Another great tip here would be to maximize the use of existing resources such as leveraging the cross-functional expertise within your team. You may already have a compliance expert within your organization potentially working on other projects. Keep a comprehensive log or a documentation of all identified risks with set priorities and mitigation options. Standardization Standardization Standardization Standardization is key to maintaining consistency and effectiveness in your GRC policies. By standardizing procedures, training, and onboarding processes, you ensure that rules are interpreted uniformly across the organization. Develop a comprehensive set of GRC training materials and procedures making it mandatory for employees to be familiar with them.  Consider incorporating GRC clauses into employment contracts to reinforce compliance. Additionally, adopt and implement industry best practices tailored to your business structure. Conduct regular training and workshops to increase awareness of your GRC policies and keep employees updated on its developments. Management and Revision Management and Revision Management and Revision The process of refining your GRC policies is ongoing. This ensures its scalability, flexibility and alignment with industry standards. So, monitor it, conduct performance checks, gather feedback and identify improvement areas on a regular basis. Best Practices in Creating a Working GRC Policies Besides understanding the steps for creating and implementing GRC policies tailored to your business, here are some tips to ensure its effective execution. Starting small, simple, and focused Starting small, simple, and focused Starting small, simple, and focused No need to roll-out a huge-scale system that will complicate matters for your company. Start small, try out, build on it and expand gradually. Keep the focus on high-impact factors. Leadership commitment Leadership commitment Leadership commitment Cultivate initiative within your leadership by openly communicating GRC principles throughout the organization. Emphasize that these principles are fundamental to the company’s long-term success and reputation. Prioritize risk and compliance activities Prioritize risk and compliance activities Prioritize risk and compliance activities At an enterprise level, this is what quantifies the value achieved at each stage of your GRC policy implementation. Engage your employees Engage your employees Engage your employees This is where you can get creative. It could be interactive training sessions on ethical decision-making, workshops on identifying and mitigating risks in your workers’ operations, and forums for discussing compliance challenges. Do not forget to encourage feedback and show you value it. Introduce proper incentives Introduce proper incentives Introduce proper incentives You can revise your company’s performance appraisal system to include GRC-related metrics. Your employees can be rewarded not just for achieving business targets but also for demonstrating adherence to compliance standards, ethical behavior, and effective risk management in their tasks. Conclusion Well-planned and implemented GRC strategy is crucial for success in today's digital business landscape. By establishing a robust operational framework, organizations can achieve regulatory compliance, effectively manage risks, improve efficiency, safeguard their reputation, and make informed strategic decisions. Furthermore, fostering a culture of GRC awareness among stakeholders promotes a sense of collective responsibility and accountability, encouraging an environment of cohesion and resilience.

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