Artificial Intelligence (AI) is increasingly shaping the way we work, live, and interact, leading to significant developments across various sectors of industry, including media, finance, business services, retail and education. In recent years, numerous high-level principles and guidelines for ‘responsible’ or ‘ethical’ AI have been formulated. However, these theoretical efforts often fall short when it comes to addressing the practical challenges of implementing AI in real-world contexts: Responsible Applied AI. The one-day workshop on Responsible Applied Artificial InTelligence (RAAIT) at HHAI 2024: Hybrid Human AI Systems for the Social Good in Malmö, Sweden, brought together researchers studying various dimensions of Responsible AI in practice.This was the second RAAIT workshop, following the first edition at the 2023 European Conference on Artificial Intelligence (ECAI) in Krakow, Poland.
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De Hogeschool van Amsterdam (HvA), Hogeschool Rotterdam (HR), Hogeschool Utrecht (HU) en de kernpartners Gemeenten Amsterdam en Rotterdam, Provincies Zuid-Holland en Utrecht, Cupola XS, Media Perspectives en CGI, hebben de ambitie om de komende acht jaar een krachtige onderzoeksgroep op te bouwen die regionaal en nationaal wordt (h)erkend als hét centrum voor praktijkgericht onderzoek op het gebied van Responsible Applied AI. Deze SPRONG-groep bouwt voort op bestaande onderzoeksgroepen met complementaire expertise van het Centre of Expertise Applied Artificial Intelligence van de HvA, het Datalab: Livinglab voor AI & Ethiek van HR en het Kenniscentrum Digital Business & Media van de HU. Responsible Applied AI methodologie Huidig AI-onderzoek is veelal fundamenteel en op de technologie gericht en voorziet daarmee tot nu toe nauwelijks in antwoorden op vragen hoe AI op een verantwoorde wijze te implementeren. De SPRONG-groep verricht onderzoek naar verantwoorde AI oplossingen voor bedrijven en instellingen. Met de onderzoekservaringen en resultaten heeft de SPRONG-groep vervolgens het doel om een Responsible Applied AI methodologie te ontwikkelen die helpt om AI oplossingen te ontwerpen, ontwikkelen en implementeren. Co-creatie in hybride leeromgevingen Om deze methodologie te ontwikkelen, is kennisopbouw en -deling nodig die onderzoekers samen ontwikkelen met de beroepspraktijk. Startpunt is daarom de (door)ontwikkeling van drie hybride leeromgevingen rondom de toepassingsgebieden Retail, Zakelijke dienstverlening en Media, waarin ontwerpers, AI-ontwikkelaars, probleemeigenaren, eindgebruikers, onderzoekers en studenten samen optrekken. Gedurende het SPRONG-programma wordt het aantal toepassingsgebieden uitgebreid en waar mogelijk nationaal opgeschaald. Aan iedere leeromgeving zijn specifieke opleidingen en praktijkpartners verbonden die meedenken over het programma. Doel is om vanuit de infrastructuur van de leeromgeving praktische tools, instrumenten, onderwijs en trainingen te ontwikkelen die breed inzetbaar zijn. Ondersteunende infrastructuur Centraal wordt een ondersteunende infrastructuur doorontwikkeld, waaronder processen en voorzieningen voor data-management en strategisch personeelsmanagement, een IT-Infrastructuur, trainingen en een impact-model.
Organisations are increasingly embedding Artificial Intelligence (AI) techniques and tools in their processes. Typical examples are generative AI for images, videos, text, and classification tasks commonly used, for example, in medical applications and industry. One danger of the proliferation of AI systems is the focus on the performance of AI models, neglecting important aspects such as fairness and sustainability. For example, an organisation might be tempted to use a model with better global performance, even if it works poorly for specific vulnerable groups. The same logic can be applied to high-performance models that require a significant amount of energy for training and usage. At the same time, many organisations recognise the need for responsible AI development that balances performance with fairness and sustainability. This KIEM project proposal aims to develop a tool that can be employed by organizations that develop and implement AI systems and aim to do so more responsibly. Through visual aiding and data visualisation, the tool facilitates making these trade-offs. By showing what these values mean in practice, which choices could be made and highlighting the relationship with performance, we aspire to educate users on how the use of different metrics impacts the decisions made by the model and its wider consequences, such as energy consumption or fairness-related harms. This tool is meant to facilitate conversation between developers, product owners and project leaders to assist them in making their choices more explicit and responsible.
The value of data in general has become eminent in recent times. Autonomous vehicles and Connected Intelligent Transport Systems (C-ITS), in particular, are rapidly emerging fields that rely a lot on “big data”. Data acquisition has been an important part of automotive research and development for years even before the advent of Internet of Things (IoT). Most datalogging is done using specialized hardware that stores data in proprietary formats on traditional hard drives in PCs or dedicated managed servers. The use of Artificial Intelligence (AI) throughout the world and specifically in the automotive sector is largely reliant on the data for the development of new and reliable technologies. With the advent of IoT technologies, the reliability of data capture could be enhanced and can improve ease of real-time analytics for analysis/development of C-ITS services and Autonomous systems using vehicle data. Data acquisition for C-ITS applications requires putting together several different domains ranging from hardware, software, communication systems, cloud storage/processing, data analytics, legal and privacy aspects. This requires expertise from different domains that small and medium scale businesses usually lack. This project aims at investigating requirements that have to be met in order to collect data from vehicles. Furthermore, this project also aims at laying foundations required for the development of a unified guidelines required to collect data from vehicles. With these guidelines, businesses that intend to use vehicle data for their applications are not only guided on the technical aspects of data collection but also equally understand how data from vehicles could be harvested in a secure, efficient and responsible manner.
