This readme file was generated on 2024-01-15 by Salvatore Romano

GENERAL INFORMATION

Title of Dataset: A dataset to assess Microsoft Copilot Answers in the Context of Swiss, Bavarian and Hesse Elections.

Author/Principal Investigator InformationName: Salvatore RomanoORCID: 0000-0003-0856-4989Institution: Universitat Oberta de Catalunya, AID4So.Address: Rambla del Poblenou, 154. 08018 Barcelona.Email: salvatore@aiforensics.org

Author/Associate or Co-investigator InformationName: Riccardo AngiusORCID: 0000-0003-0291-3332Institution: Ai ForensicsAddress: Paris, France.Email: riccardo@aiforensics.org

Date of data collection: from 2023-09-21 to 2023-10-02.

Geographic location of data collection: Switzerland and Germany.

Information about funding sources that supported the collection of the data: The data collection and analysis was supported by AlgorithmWatch's DataSkop project, funded by Germany’s Federal Ministry of Education and Research (BMBF) as part of the program “Mensch-Technik-Interaktion” (human-technology interaction). dataskop.netIn Switzerland, the investigation was realized with the support of Stiftung Mercator Schweiz.AI Forensics contribution was supported in part by the Open Society Foundations.AI Forensics data collection infrastructure is supported by the Bright Initiative.

SHARING/ACCESS INFORMATION

Licenses/restrictions placed on the data: This publication is licensed under a Creative Commons Attribution 4.0 International License.https://creativecommons.org/licenses/by/4.0/deed.en

Links to publications that cite or use the data: https://aiforensics.org//uploads/AIF_AW_Bing_Chat_Elections_Report_ca7200fe8d.pdf

Links to other publicly accessible locations of the data: NA

Links/relationships to ancillary data sets: NA

Was data derived from another source? NAIf yes, list source(s):

Recommended citation for this dataset: S. Romano, R. Angius, N. Kerby, P. Bouchaud, J. Amidei, A. Kaltenbrunner. 2024. A dataset to assess Microsoft Copilot Answers in the Context of Swiss, Bavarian and Hesse Elections. https://aiforensics.org//uploads/AIF_AW_Bing_Chat_Elections_Report_ca7200fe8d.pdf

DATA & FILE OVERVIEW

File List: Microsof-Copilot-Answers_in-Swiss-Bavarian-Hess-Elections.csvThe only dataset for this research. It includes rows with prompts and responses from Microsoft Copilot, along with associated metadata for each entry.

Relationship between files, if important: NA

Additional related data collected that was not included in the current data package: NA

Are there multiple versions of the dataset? NAIf yes, name of file(s) that was updated: Why was the file updated? When was the file updated?

METHODOLOGICAL INFORMATION

Description of methods used for collection/generation of data:In our algorithmic auditing research, we adopted for a sock-puppet audit methodology (Sandvig at Al., 2014). This method aligns with the growing interdisciplinary focus on algorithm audits, which prioritize fairness, accountability, and transparency to uncover biases in algorithmic systems (Bandy, 2021). Sock-puppet auditing offers a fully controlled environment to understand the behavior of the system.

Every sample was collected by running a new browser instance connected to the internet via a network of VPNs and residential IPs based in Switzerland and Germany, then accessing Microsoft Copilot through its official URL. Every time, the settings for Language and Country/Region were set to match those of potential voters from the respective regions (English, German, French, or Italian, and Switzerland or Germany). We did not simulate any form of user history or additional personalization. Importantly, Microsoft Copilot's default settings remained unchanged, ensuring that all interactions occurred in the Conversation Style" set asBalanced".

Sandvig, C.; Hamilton, K.; Karahalios, K.; and Langbort, C. 2014. Auditing algorithms: Research methods for detecting discrimination on internet platforms. Data and discrimination: converting critical concerns into productive inquiry,22(2014): 4349–4357.

Bandy, J. 2021. Problematic machine behavior: A systematic literature review of algorithm audits. Proceedings of the acm on human-computer interaction, 5(CSCW1): 1–34

Methods for processing the data: The process involved analyzing the HTML code of the web pages that were accessed. During this examination, key metadata were identified and extracted from the HTML structure. Once this information was successfully extracted, the rest of the HTML page, which primarily consisted of code and elements not pertinent to the needed information, was discarded. This approach ensured that only the most relevant and useful data was retained, while all unnecessary and extraneous HTML components were efficiently removed, streamlining the data collection and analysis process.

Instrument- or software-specific information needed to interpret the data: NA

Standards and calibration information, if appropriate: NA

Environmental/experimental conditions: NA

Describe any quality-assurance procedures performed on the data: NA

People involved with sample collection, processing, analysis and/or submission: Salvatore Romano, Riccardo Angius, Natalie Kerby, Paul Bouchaud, Jacopo Amidei, Andreas Kaltenbrunner.

DATA-SPECIFIC INFORMATION FOR:Microsof-Copilot-Answers_in-Swiss-Bavarian-Hess-Elections.csv

Number of variables: Number of Variables: 33

Number of cases/rows: 5562

Variable List:prompt - (object) Text of the prompt.answer - (object) Text of the answer.country - (object) Country information.language - (object) Language of the text.input_conversation_id - (object) Identifier for the conversation.conversation_group_ids - (object) Group IDs for the conversation.conversation_group_names - (object) Group names for the conversation.experiment_id - (object) Identifier for the experiment group.experiment_name - (object) Name of the experiment group.begin - (object) Start time.end - (object) End time.datetime - (int64) Datetime stamp.week - (int64) Week number.attributions - (object) Link quoted in the text.attribution_links - (object) Links for attributions.search_query - (object) Search query used by the chatbot.unlabelled - (int64) Unlabelled flag.exploratory_sample - (int64) Exploratory sample flag.very_relevant - (int64) Very relevant flag.needs_review - (int64) Needs review flag.misleading_factual_error - (int64) Misleading factual error flag.nonsense_factual_error - (int64) Nonsense factual error flag.rejects_question_framing - (int64) Rejects question framing flag.deflection - (int64) Deflection flag.shield - (int64) Shield flag.wrong_answer_language - (int64) Wrong answer language flag.political_imbalance - (int64) Political imbalance flag.refusal - (int64) Refusal flag.factual_error - (int64) Factual error flag.evasion - (int64) Evasion flag.absolutely_accurate - (int64) Absolutely accurate flag.macrocategory - (object) Macro-category of the content.

Missing data codes:NA

Specialized formats or other abbreviations used: NA

In 2024, OpenAI's ChatGPT was by far the most widely used AI-powered tool among developers over the past year, with 82 percent of developers reporting regular usage. GitHub Copilot ranked second at 44 percent, while Google Gemini came in third at 22 percent. Other notable tools included Bing AI and Visual Studio Intellicode, both of which are owned by Microsoft. Tools such as Claude and Perplexity AI saw lower but still notable usage rates. Traditional tools like WolframAlpha maintained a steady user base at four percent, overtaking newer tools such as Meta AI and Amazon Q.

The size of the Artificial Intelligence In Retail Market was valued at USD 8.13 Billion in 2023 and is projected to reach USD 33.98 Billion by 2032, with an expected CAGR of 22.67% during the forecast period. Recent developments include: January 2024: Microsoft unveiled new generative AI and data solutions and capabilities for retailers that cover the whole retail customer journey. These solutions and capabilities help businesses more effectively engage their audiences, unlock, and unify retail data, and enable personalized shopping experiences and store associate empowerment. Microsoft Cloud for Retail now gives retailers more options to integrate copilot experiences throughout the shopper journey, including new copilot templates on Azure OpenAI Service that enable retailers to create personalized shopping experiences and support store operations, retail data solutions in Microsoft Fabric, new copilot features in Microsoft Dynamics 365 Customer Insights, and the introduction of Retail Media Creative Studio in the Microsoft Retail Media Platform., January 2024: IBM and SAP announced their partnership to build solutions that help customers in the retail and consumer packaged goods industries use generative AI to improve their supply chain, finance operations, sales, and services. IBM and SAP are collaborating to develop new generative and traditional AI solutions that will be concentrated on addressing the complexities of the direct store delivery business process and product portfolio management. This is due to the companies' shared legacy of technological expertise and the successful integration of IBM Watsonx, an enterprise-ready AI and data platform, and AI assistants into SAP solutions., January 2023: Google unveiled four new and updated AI technologies to assist businesses in transforming their in-store shelf monitoring operations and improving their e-commerce sites by providing customers with smoother and more natural online shopping experiences. A new shelf-checking AI solution based on Google Cloud's Vertex AI Vision uses Google's library of facts about people, places, and things, allowing businesses to identify billions of products to guarantee in-store shelves are properly proportioned and stocked. Additionally, Google Cloud updated its Discovery AI solutions with a new browsing feature powered by AI and a new customization AI capability to assist retailers in modernizing their digital storefronts with more dynamic and user-friendly purchasing experiences.. Key drivers for this market are: Data security and privacy concerns Lack of skilled AI professionals High cost of AI implementation Regulatory complexities. Potential restraints include: Growing customer demand for personalized experiences Need for increased efficiency and automation Technological advancements in AI and cloud computing Government initiatives to promote AI adoption. Notable trends are: Generative AI: AI that creates original content, such as personalized recommendations and product designs. Metaverse: Virtual and augmented reality technologies that enhance customer experiences. Edge AI: AI processed on-device, enabling real-time insights and decision-making..

The Web Analytics market in Retail and CPG is experiencing robust growth, projected to reach $1.22 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 18.19% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing need for data-driven decision-making within retail and consumer packaged goods (CPG) companies is paramount. Businesses are leveraging web analytics to gain deeper insights into consumer behavior, optimize marketing campaigns, personalize customer experiences, and improve operational efficiency. The rising adoption of e-commerce and omnichannel strategies further accelerates market growth, demanding sophisticated analytics to track customer journeys across multiple touchpoints. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are enhancing the capabilities of web analytics platforms, enabling more accurate predictions and proactive adjustments to business strategies. The market is segmented by offering (solutions and services), organization size (SMEs and large enterprises), and application (SEO/ranking, online marketing, customer profiling, application performance management, social media management, and others). Large enterprises currently dominate the market due to their greater resources and sophisticated analytics requirements, but the SME segment is expected to witness significant growth driven by the accessibility of cloud-based analytics solutions. Geographic distribution shows strong growth potential across regions, particularly in the Asia-Pacific region fueled by rapid e-commerce adoption and digital transformation initiatives. North America and Europe maintain substantial market shares due to early adoption and mature digital infrastructure. Competition in the market is intense, with major players like Google, IBM, Meta, Adobe, Microsoft, and Salesforce offering a wide range of analytics solutions and services. However, the market also accommodates smaller, specialized providers catering to niche needs. The future growth of the Web Analytics market in Retail and CPG will depend on factors like continued innovation in analytics technologies, the increasing complexity of customer data, the need for enhanced data security and privacy, and the evolving regulatory landscape around data usage. Companies that can effectively address these factors and deliver comprehensive, user-friendly, and insightful analytics platforms are poised to capture significant market share in the coming years. The focus will continue to shift toward predictive analytics, real-time dashboards, and integrated solutions that provide a holistic view of the customer journey. Recent developments include: April 2024 - IBM Consulting and Microsoft have unveiled the opening of the IBM-Microsoft Experience Zone in Bangalore, India. The Experience Zone is designed as an exclusive venue where clients can delve into the potential of generative AI, hybrid cloud solutions, and other advanced Microsoft offerings. The goal is to expedite their business transformations and secure a competitive edge., January 2024 - Microsoft Corp. announced a suite of generative AI and data solutions tailored for retailers. These solutions cover every touchpoint of the retail shopper journey, from crafting personalized shopping experiences and empowering store associates to harness and consolidating retail data, ultimately aiding brands in better connecting with their target audiences. Microsoft's initiatives include introducing copilot templates on Azure OpenAI Service, enhancing retailers' ability to craft personalized shopping experiences, and streamlining store operations. Microsoft Fabric hosts advanced retail data solutions, while Microsoft Dynamics 365 Customer Insights boasts new copilot features. Microsoft also rolled out the Retail Media Creative Studio within the Microsoft Retail Media Platform. These advancements collectively bolster Microsoft Cloud for Retail, providing retailers with diverse tools to integrate copilot experiences across the entire shopper journey seamlessly.. Key drivers for this market are: Growing Demand for Online Shopping Trends, Rising Adoption of Analytics Tools to Understand Customer Preferences; Increasing Customer Centric Approach and Use of Recommendation Engines. Potential restraints include: Growing Demand for Online Shopping Trends, Rising Adoption of Analytics Tools to Understand Customer Preferences; Increasing Customer Centric Approach and Use of Recommendation Engines. Notable trends are: Search Engine Optimization and Ranking Sector Significantly Driving the Market Growth.

The Enterprise Resource Planning (ERP) Software Market size was valued at USD 71.41 USD Billion in 2023 and is projected to reach USD 183.12 USD Billion by 2032, exhibiting a CAGR of 14.4 % during the forecast period. The increasing adoption of cloud-based ERP solutions, rising demand for integrated business solutions, and growing need for data-driven insights are driving the market growth. Enterprise resource planning (ERP) is a type of software system that helps organisations automate and manage core business processes for optimal performance. ERP software coordinates the flow of data between a company’s business processes, providing a single source of truth and streamlining operations across the enterprise. It’s capable of linking a company’s financials, supply chain, operations, commerce, reporting, manufacturing, and human resources activities on one platform. ERP systems tie together a multitude of business processes and enable the flow of data between them. By collecting an organization’s shared transactional data from multiple sources, ERP systems eliminate data duplication and provide data integrity with a single source of truth. Recent developments include: February 2024: AwanBiru Technology entered a partnership with U.K.-based Sage Group PLC for promoting, distributing, marketing, and reselling the Sage X3 products and services in Malaysia., December 2023: The Finnish Defense Forces collaborated with Accenture for a digital transformation program designed to modernize its existing legacy ERP system. This program is expected to improve user experience, enhance system performance, and deliver advanced reporting & analytics capabilities., September 2023: SAP Africa extended its alliance with Microsoft to provide RISE with SAP to clientele across several global markets, along with Africa. It is a complete solution of ERP software and result-driven services intended to aid enterprises in transforming the core SAP ERP to the cloud, leveraging the cloud hosting abilities of hyper-scalers, such as Microsoft., May 2023: SAP, in partnership with IBM, extended its ERP offerings by incorporating the IBM Watson AI smarts into its ERP systems. IBM Watson will be integrated with various offerings, which includes its digital assistant SAP Start, which serves as a combined entry point into its cloud-based software., March 2023: SAP introduced new cloud ERP offerings for midsize enterprises. The new offering is specifically designed for midsize firms to allow them to enjoy the all-inclusive benefits of cloud ERP. The GROW with SAP offers features, tools, and services to simplify delivery at a fixed rate and assures customers of faster time to value their requirements., March 2023: Microsoft introduced Microsoft Dynamics 365, a copilot that comprises both ERP and CRM, and offers AI-powered, interactive assistance across various business functions. With the Dynamics 365 Copilot, enterprises can empower their employees with AI tools developed for sales, marketing, service, operations, and supply chain responsibilities., January 2023: Ramco Systems strengthened its 25-year association with Addison & Co., an exporter and manufacturer of metal cutting tools, by providing next-gen Enterprise Resource Planning (ERP) software to Addison & Co., July 2022: Infor collaborated with Syntellis, a supplier of enterprise resource solutions, to assist healthcare consumers in accessing the Syntellis Axiom Healthcare Suite. This suite offers EPM tools with data-driven insights for enhancing the company’s operations.. Key drivers for this market are: Growing Need to Improve Operational Efficiency and Streamline Business Processes among Enterprises to Drive Market Growth. Potential restraints include: Integration Issues With On-premises Deployment Models to Limit ERP Adoption. Notable trends are: Increasing Popularity of Two-Tier ERP to Augment Market Growth.

The AI Image Generator Market size was valued at USD 356.1 USD Million in 2023 and is projected to reach USD 1094.58 USD Million by 2032, exhibiting a CAGR of 17.4 % during the forecast period. AI image generator refers to a software application for generating image data by means of artificial intelligence, utilizing such models as deep learning, neural networks, and others. Some of them are GANs which stand for Generative Adversarial Networks, VAEs which stand for Variational Autoencoders, and diffusion models. Essential characteristics include crystal clear display of the resultant image, conversion of the source image to another style, and image improvement. It makes use for the generation of art, designing, virtual fitting, and even in-game design . These generators facilitate the quickly and cheaply generated visualization and image modifications depending on certain parameters or styles, hence changing the creative landscapes of various industries by improving efficiency and creativity. Recent developments include: September 2023 - OpenAI, a company specializing in the generative AI industry, introduced DALL-E 3, the latest version of its image generator. This upgrade, powered by the ChatGPT controller, produces high-quality images based on natural-language prompts and incorporates ethical safeguards., May 2023 - Stability AI introduced StableStudio, an open-source version of its DreamStudio AI application, specializing in converting text into images. This open-source release enabled developers and creators to access and utilize the technology, creating a wide range of applications for text-to-image generation., April 2023 - VanceAI launched an AI text-to-image generator called VanceAI Art Generator, powered by Stable Diffusion. This tool could interpret text descriptions and generate corresponding artworks. Users could combine image types, styles, artists, and adjust sizes to transform their creative ideas into visual art., March 2023 - Adobe unveiled Adobe Firefly, a generative AI tool in beta, catering to users without graphic design skills, helping them to create images and text effects. This announcement coincided with Microsoft’s launch of Copilot, offering automatic content generation for 365 and Dynamics 365 users. These advancements in generative AI provided valuable support and opportunities for individuals facing challenges related to writing, design, or organization., March 2023 - Runway AI introduced Gen-2, a combination of AI models capable of producing short video clips from text prompts. Gen-2, an advancement over its predecessor Gen-1, would generate higher-quality clips and provide users with increased customization options.. Key drivers for this market are: Growing Adoption of Augmented Reality (AR) and Virtual Reality (VR) to Fuel the Market Growth. Potential restraints include: Concerns related to Data Privacy and Creation of Malicious Content to Hamper the Market. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

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